The Design and Implementation of a Novel Pharmacogenomic Assay to Genotype the CYP3A53 (rs776746) and CYP3A51E (rs4646453) Genetic Variants

The Design and Implementation of a Novel Pharmacogenomic Assay to Genotype the CYP3A53 (rs776746) and CYP3A51E (rs4646453) Genetic Variants | 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 Design and Implementation of a Novel Pharmacogenomic Assay to Genotype the CYP3A5 3 (rs776746) and CYP3A5 1E (rs4646453) Genetic Variants Reema Sameem, Nafeesa Noordeen, Somasundaram Praveenan, Tithila Kalum Wetthasinghe, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2651198/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The cytochrome P450 3A5 CYP3A5 enzymes are important for metabolizing the drug tacrilomus, an immunosuppressive agent used in solid organ transplantation. Genetic variants in the CYP3A5 gene are significant determinants of tacrolimus efficacy. The present study was undertaken to design a novel pharmacogenetic assay (Single step-Tetra Arms Polymerase Chain Reaction) to study the distribution of the CYP3A5*3 (rs776746) and CYP3A5*1E (rs4646453) variants by genotyping a cohort of healthy individuals. Results The CYP3A5*3 variant was the most frequent allele detected at 82% and the CYP3A5*1E C allele was found in 66.5% of the samples. The allele frequencies of CYP3A5*3 (rs776746) and CYP3A5*1E (rs4646453) were statistically significant (p < 0.05) when compared with the Asian ethnic group. The observed CYP3A5 genotype frequency distributions for the CYP3A5*3 (rs776746) and CYP3A5*1E (rs4646453) variants in the study population were consistent with the Hardy–Weinberg equilibrium (P > 0.05). For the CYP3A5*3 variant the frequency of the T/T [extensive metabolizer], C/T [intermediate metabolizer] and C/C [poor metabolizer] variants were 4%, 28% and 68% respectively. Furthermore, a significant linkage disequilibrium among rs4646453 and rs776746 was identified (p < 0.05). Conclusions A novel tetra-primer ARMS PCR assay was successfully designed and implemented for genotyping of the CYP3A5 variants CYP3A5*3 (rs776746) and CYP3A5*1E (rs4646453). These pharmacogenomic assays could be offered to patients to predict their response to tacrolimus. Tacrolimus CYP3A5 tetra-primer ARMS PCR genotype pharmacogenetic assay Figures Figure 1 Figure 2 Figure 3 1.0 Introduction Tacrolimus is a first-line immunosuppressive drug of the calcineurin inhibitor class and is considered a cornerstone of maintenance drug therapy following solid organ transplantations ( 1 ). Since its approval by the US Food and Drug Administration in 1994, the clinical use of tacrolimus has been complicated due to its high inter-patient variability and narrow therapeutic index ( 2 ). Tacrolimus demonstrates wide inter-individual and inter-ethnic variability, leading to potential graft rejection due to underexposure or toxicity associated with overexposure ( 3 ). As the pharmacokinetic profile of tacrolimus varies widely among patients, identifying factors including genetic variants that affect the pharmacokinetic variability of tacrolimus is important ( 4 ). Pharmacogenetic studies have shown the association between the cytochrome P450 3A5 ( CYP3A5 ) genotype and tacrolimus pharmacokinetics ( 5 ) ( 6 ). Single Nucleotide Polymorphisms (SNPs) in CYP3A5 gene explain 40–50% of the variability in tacrolimus metabolism and clearance ( 7 ). The SNP in the CYP3A5 gene involving an A to G transition at position 6986 within intron 3 is the most well studied genomic variant which contributes to tacrolimus metabolism ( 8 ). The 6986A > G (rs776746), encodes the nonfunctional CYP3A5*3 allele of the CYP3A5 gene, thus CYP3A5*3 induces alternative splicing, followed by protein truncation, resulting in decreased enzymatic activity of CYP3A5 . Consequently, CYP3A5*3 allele is associated with reduced tacrolimus dose requirement ( 9 ) ( 10 ). Individuals homozygous for the mutant CYP3A5*3 allele are referred to as CYP3A5 non-expressers. CYP3A5 non-expression is the most frequent phenotype in most ethnic populations, except blacks ( 11 ). Accordingly, individuals carrying at least 1 CYP3A5*1 allele, also known as the wild-type allele are identified as CYP3A5 expressers ( 12 ) ( 13 ). The wild-type CYP3A5 *1 allele is correlated with higher production of functional CYP3A5 enzyme, thereby contributing to, higher drug-metabolizing activity by CYP3A overall ( 14 ). In 2015, the Clinical Pharmacogenetics Implementation Consortium (CPIC) published the guideline for CYP3A5 genotype and tacrolimus dosing. Although this association is well established, the variable frequency of the CYP3A5*1 allele among different populations makes the utility of the genetic test variable ( 15 ) ( 16 ). Although much effort has been devoted to the better understanding the pharmacogenomics of tacrolimus, the association between CYP3A5 genotypes and tacrolimus response has not yet been studied in the Sri Lankan population. The aim of this study was to design and implement a novel pharmacogenomics assay for the CYP3A5*3 (rs776746) and CYP3A5*1E (rs4646453) variants known to be associated with tacrolimus response, and to genotype the selected CYP3A5 gene variants in a cohort of healthy Sri Lankan individuals. 2.0 Methodology 2.1 Study population The present study was carried out at the Human Genetics Unit (HGU), Faculty of Medicine, University of Colombo. An existing resource of stored venous blood samples obtained from 100 individuals which were collected in EDTA tubes and stored at − 20°C were used for the present study. At the time of sample collection, written informed consent was obtained from the study participants for samples to be used for research in future studies. The study parameters were reviewed by the Ethics Review Committee of the Faculty of Medicine, University of Colombo Sri Lanka and ethical clearance was granted to conduct the study (EC-21-030). 2.2 DNA extraction Genomic DNA was extracted from peripheral blood specimens of the selected population using QIAamp DNA Blood Mini Kit (QIAGEN, Hilden, Germany) in accordance with the manufacturer’s protocol. DNA quantification was performed on 30 randomly selected samples using Quantus™ Fluorometer. 2.3 Designing of primers for the selected variants Tetra primer ARMS PCR primer pairs were designed using Primer1 software ( http://primer1.soton.ac.uk/primer1.html ). The mutation points were positioned asymmetrically with respect to the common (outer) primers so that allele specific amplicons with different product lengths could be easily separated by standard agarose gel electrophoresis. The specificity of the designed primers and their melting temperatures were checked by using NCBI Basic Local Alignment Search Tool (BLAST) ( https://www.ncbi.nlm.nih.gov/tools/primer-blast/ ) and Ensembl BLAST ( https://asia.ensembl.org/Multi/Tools/Blast?db=core ). In-silico PCR run was carried out using the University of California, Santa Cruz (UCSC) Genome Browser ( https://genome.ucsc.edu/ ) and Serial cloner software™. The primers used in this study are listed in Table 1 . Table 1 List of primer sequences, annealing temperatures and length of the amplified segment for rs776746 (T > C) and rs4646453 (C > A). Gene Primer Sequence Amplicon Size (bp) Tm ( o C) GC % CYP3A5*3 variant (rs776746) (T > C) Forward Inner (C Allele) 5’TAATGTGGTCCAAACAGGGAAGAGAGAC 3’ 142 67 46.4 Reverse Inner (T Allele) 5’ AGAGCTCTTTTGTCTTTCAA3’ 284 60 35 Forward Outer 5’ GAGTTGACCTTCATACGTTCTGTGTG 3’ 369 (From two outer primers) 63 46.2 Reverse Outer 5’ AAAACATTATGGAGAGTGGCATAGGAG 3’ 63 40.7 CYP3A5 *1E variant (rs4646453) (C > A) Forward Inner (C Allele) 5’ AAATTCTCATCTTCCTGGAACAC 3’ 180 58 39.1 Reverse Inner (A Allele) 5’ TTCTGAAAATGTGCAGGGAT 3’ 291 58 40 Forward Outer 5’ CTTCCTGATCGGTATGTTTGAT 3’ 428 (From two outer primers) 58 40.9 Reverse Outer 5’ CTTTTGCTTCTAGCACCGACTA 3’ 58 45.5 2.4 Tetra primer ARMS PCR optimization to detect the CYP3A5*3 (rs776746) variant A series of optimization reactions were performed on PCR conditions, including annealing temperature, concentrations of the PCR reagents as well as the ratio of outer and inner primers, to increase the PCR product specificity and obtain the expected band sizes. The optimized PCR reaction was performed in a single tube containing 25 µl of reaction volume made up of the following components: 5.0 µL of 5 × PCR Buffer, 1.5 µL of MgCl 2 (25 mM), 1.7 µL of dNTP mixture (2.5 mM each), 1 µL of 25 mM CYP3A5*3 CF, 1 µL of 25 mM CYP3A5*3 CR, 2 µL of 25 mM CYP3A5*3 F(C) and 2 µL of 25 mM CYP3A5*3 R(T), 0.3 µL of 5U/ µL Taq DNA polymerase, 9.5 µL of sterile nuclease free water and 1 µL of DNA. The final optimized PCR cycling conditions is as follows: Initial denaturation at 94°C for 5 min followed by 35 cycles of denaturation at 94°C for 1 minute, annealing at 55.5°C for 30 seconds, extension at 72°C for 1 minute and final extension at 72°C for 5 minutes, followed by cooling at 4°C. 2.5 Tetra primer ARMS PCR optimization to detect the CYP3A5 *1E (rs4646453) variant The optimized PCR was performed in a single tube containing 25 µl of reaction volume made up of the following components: 5.0 µL of 5 × PCR Buffer, 1.5 µL of MgCl 2 (25 mM), 1.5 µL of dNTP mixture (2.5 mM each), 0.5 µL of 25 mM CYP3A5*1E CF, 0.5 µL of 25 mM CYP3A5*1E CR, 1 µL of 25 mM CYP3A5*1E F(C), 1 µL of 25 mM CYP3A5*1E R(A), 0.3 µL of 5U/ µL Taq DNA polymerase, 12.7 µL of sterile nuclease free water and 1 µL of DNA. PCR cycling was performed as follows: Initial denaturation 94°C for 5 min followed by 30 cycles of denaturation at 94°C for 1 minute, annealing at 53.5°C for 30 seconds, extension at 72°C for 1 minute and final extension at 72°C for 5 minutes, followed by cooling at 4°C. Validation of the developed methodology for both gene variants was done by Sanger sequencing. 2.6 Agarose gel electrophoresis The PCR products were separated on 2% agarose gel with DNA size marker of 50 bp followed by staining with ethidium bromide. The samples were run for 50 minutes at 80V to obtain sufficient separation of bands and subsequently visualized by the gel documentation system. 2.7 Sanger validation To evaluate the accuracy of the assay, selected PCR-amplified DNA samples (n = 3, respectively, for each genotype) were sequenced using the Sanger DNA sequencing method ( 17 ). The PCR products were sequenced with forward and reverse outer primers of 0.8mM concentration using an automated Applied Biosystems 3130 DNA sequencer (Gene Labs, Ninewells Hospital, Sri Lanka). The Sanger validated samples were used as controls to compare the genotyping results of the remaining samples. 2.8 Genotyping of the study population Upon validation of the protocol, genotyping of the study population was carried out according to the optimized tetra-primer ARMS PCR assay. A total of 100 patient samples were genotyped for each variant. 2.9 Statistical analysis The statistical analysis was conducted using SPSS (version 22; IBM Corp., Armonk, NY, USA) software. The strength of association was evaluated using 95% confidence intervals (CIs). Estimation of allele and genotype frequencies and their deviation from Hardy-Weinberg equilibrium was assessed by Pearson’s goodness of fit chi-square test. Continuous variables were shown as mean and standard deviation and qualitative data were expressed as frequency and percentage. Additionally, linkage disequilibrium (LD) block and haplotype were assessed by Haploview 4.1 software. The D’ and r 2 values for all pairs of SNPs were calculated. In all statistical analysis, p < 0.05 was considered as a statistically significant association. 3.0 Results 3.1 Demographic characteristics of the study population The study population consisted of 54% females and 46% males (Sex ratio = 1.17). The mean ± SD age of the participants was 19.12 ± 15.06. 3.2 Results of genotyping 3.2.1 Genotyping results of the CYP3A5*3 variant rs776746 (T > C) Among the 100 samples genotyped, the CYP3A5*3/*3 genotype was observed in 68% of cases, CYP3A5*1/*3 genotype in 28% of cases, and CYP3A5*1/*1 genotype in 4% of cases. Total allelic frequency was 82% for CYP3A5*3 and 18% for CYP3A5*1 . Figure 1 shows a representative gel image of the genotyping result. The expected gel band patterns were observed in the gel image results (Control band: 369 bp; band for C allele: 142 bp; band for T allele: 284 bp). 3.2.2 Genotyping results of the CYP3A5 *1E variant rs4646453 (C > A) Out of the 100 individuals genotyped, 55% were heterozygous (A/C), 39% were identified to be homozygous wild type (C/C) while 6% were homozygous variant (A/A) for the CYP3A5 *1E variant. Total allelic frequency was 66.5% for CYP3A5 *1E C allele and 33.5% for CYP3A5 *1E A allele, respectively. Figure 2 shows a representative gel image of the genotyping result. The expected gel band patterns were observed in the gel image results (Control band: 428 bp; band for C allele: 180 bp; band for A allele: 291 bp). 3.3 Results of Sanger validation of the PCR assay The Sanger sequencing output results [under Additional file information, Figure S3 and Figure S4 ] were analysed using the BioEdit 7.2 software. The published CYP3A5 gene reference sequence (GenBank accession number NC_000007) was obtained from GenBank at the NCBI, USA for alignment and comparison of the nucleotide sequences generated from the random samples. 3.4 Phenotype prediction based on CYP3A5 genotype The frequency of each genotype identified in the study population categorized by gender is shown in Table 2 . Table 2 The frequency of each genotype identified in the study population categorized by gender. Variant Genotype Number of Subjects Frequency (%) Variant allele frequency Male Female rs776746 (T > C) Homozygous Wild type (T/T) 2 2 4 0.18 (χ 2 = 0.265) (p = 0.876) Homozygous Variant (C/C) 31 37 68 Heterozygous Variant (C/T) 13 15 28 rs4646453 (C > A) Homozygous Wild type (C/C) 16 23 39 0.33 (χ 2 = 5.496) (p = 0.064) Homozygous Variant (A/A) 2 4 6 Heterozygous Variant (C/A) 28 27 55 No deviation from Hardy-Weinberg equilibrium was observed for the genotype frequencies (p > 0.05). Based on the genotype results obtained for the CYP3A5 rs776746 variant the participants could be grouped in to 3 distinct phenotypes (Table 3 ). Table 3 CYP3A5 genotype and frequency of expected phenotype in a cohort of Sri Lankan patients CYP3A5 Genotype Frequency (%) Likely Phenotype *1/*1 (T/T) 4 Extensive Metabolizer ( CYP3A5 Expresser) *1/*3 (C/C) 28 Intermediate Metabolizer ( CYP3A5 Expresser) *3/*3 (C/C) 68 Poor Metabolizer ( CYP3A5 Non- Expresser) 3.5 Linkage disequilibrium and haplotype analysis of two CYP3A5 gene variants The Haploview program was used to assess the linkage disequilibrium (LD) block and haplotype of the two CYP3A5 variants. The LD analysis of rs776746 and rs4646453 showed an association with each other D’ =0.486 r 2 = 0.103 (Fig. 3 ) indicating significant LD between loci (p < 0.05). The most represented haplotype in the whole cohort was CC, followed by CA, TA and TC, as listed in Table 4 . Table 4 Haplotype frequencies of two CYP3A5 variants rs776746 and rs4646453 Haplotype Frequency CC 0.604 CA 0.216 TA 0.119 TC 0.061 4.0 Discussion The detection of CYP3A5 variant alleles, and knowledge about their allelic frequency in specific ethnic groups, is important to optimize pharmacotherapy ( 18 ). In this study a novel genotyping assay was developed for the identification CYP3A5*3 (rs776746) and CYP3A5*1E (rs4646453) variants in a sample cohort. Compared with other routinely used methods, tetra primer ARMS-PCR assay could be beneficial in terms of total time, cost and applicability in a genetic diagnostic laboratory ( 19 ) ( 20 ) ( 21 ). Hence the tetra-primer ARMS PCR technique was used in this study as it was found to be an accurate and robust technique for genotyping the CYP3A5 variants. CYP3A5 is highly polymorphic with significant inter-individual variation in the enzyme activity contributing to the absorption, metabolism and tissue distribution of drugs ( 22 ) ( 23 ). Homozygous carriers (*3/*3 or CC) of this variant lack functional CYP3A5 protein because of the frame shift mutation and truncation of the translated protein. Previous studies have confirmed that CYP3A5*3 is associated with drug metabolism, and CYP3A5 *3/*3 carriers have decreased metabolism of tacrolimus, compared to CYP3A5 *1/*1 and CYP3A5 *1/*3 carriers ( 24 ) ( 25 ) ( 26 ) ( 27 ). As a result, patients with CYP3A5 *3/*3 genotype, treated with tacrolimus may have an increased risk of nephrotoxicity as compared to patients without it ( 28 ) ( 29 ). More recently, scientists have identified that the CYP3A5*1E (rs4646453) variant remarkably contributes to the tacrolimus pharmacokinetics ( 30 ). This study investigated screening of the CYP3A5 variant alleles among 100 individuals, the results demonstrate that the CYP3A5*3 allele is abundantly present in the sample population, displaying an allelic frequency of 82%. There was a statistically significant association (p < 0.05) between the allele frequency reported in this study and CYP3A5*3 allele frequency data reported for global and East Asian ethnic groups from the gnomAD Genomes, 1000 Genomes and ALFA databases. On the basis of the CYP3A5*3 variant alleles detected in the study group of 100 samples, it was deduced that 68% may have low expression of CYP3A5 . This was in agreement with previous studies involving South Indian patients, where 60% of the population was identified to have the CYP3A5*3/*3 genotype ( 31 ). An East Asian study, focusing on the Japanese population, had recorded a similar CYP3A5*3/*3 genotype frequency of 60.5% ( 32 ). Moreover, these results were consistent with a recent study of Tunisian patients, displaying 63.5% of CYP3A5*3/*3 genotype and 80.7% of the most frequently occurring CYP3A5*3 variant allele ( 33 ). Screening of 100 individuals for the CYP3A5*1E (rs4646453) variant alleles revealed that the CYP3A5*1E C allele was commonly found in the sample population, demonstrating an allelic frequency of 66.5%. The allele frequency of the rs4646453 variant recorded in the study population showed significant association (p < 0.05) with the CYP3A5*1E allele frequency data reported for South Asian and East Asian ethnic groups from the gnomAD Genomes, 1000 Genomes and ALFA databases. As the rs4646453 is a novel CYP3A5 variant been explored by scientists, there is limited literature with respect to specific ethnic groups. Thus far, this variant has been recorded in the Chinese population and has shown to significantly affect tacrolimus metabolism ( 30 ). The observed CYP3A5 genotype frequency distributions for the rs776746 and rs4646453 variants in the study population was consistent with the Hardy–Weinberg equilibrium (P > 0.05). The LD analysis indicated a significant LD between the loci of the two SNPs, rs776746 and rs4646453 (p < 0.05). These findings are consistent with previous studies ( 34 ) ( 35 ) ( 36 ) which strengthens the confidence of the generalizability of the results. The findings of the present study further promote that the presence of LDs with rs776746 may partly explain the role of rs4646453 in tacrolimus metabolism. The present study has several limitations primarily due to its single-centred retrospective nature. Future studies should be initiated to investigate the genotype-phenotype relationship of the identified variants in the Sri Lankan population to better understand how variant alleles contribute to different drug responses. Hence, more case–control studies with large number of multi-ethnic samples and diversified factors are necessary to comprehensively evaluate the involvement of the CYP3A5 variants on tacrolimus metabolism. 5.0 Conclusion In conclusion, a novel T-ARMS PCR assay was successfully designed, optimized and validated to genotype the CYP3A5*3 (rs776746) and CYP3A5*1E (rs4646453) variants and could be implemented as a cost-effective technique. Abbreviations ALFA Allele Frequency Aggregator ARMS Amplification Refractory Mutation System BLAST Basic Local Alignment Search Tool CI Confidence Interval CPIC Clinical Pharmacogenetics Implementation Consortium CYP Cytochromes P450 CYP3A5 Cytochrome P450 3A5 DNA Deoxyribonucleic Acid dNTPs Deoxyribose Nucleotide Triphosphate EDTA Ethylenediaminetetraacetic Acid CF Control Forward CR Control Reverse HGU Human Genetics Unit HWE Hardy–Weinberg Equilibrium LD Linkage Disequilibrium MgCl2 Magnesium Chloride NCBI National Center for Biotechnology Information NGS Next Generation Sequencing PCR Polymerase Chain Reaction F(C) Forward primer for C allele R(T) Reverse primer for T allele R(A) Reverse primer for A allele SD Standard Deviation SNPs Single Nucleotide Polymorphism TBE buffer Tris/Borate/EDTA buffer TE buffer Tris EDTA UCSC University of California, Santa Cruz Declarations Ethics approval and consent to participate All methods were carried out in accordance with relevant institutional guidelines and regulations. An existing resource of stored venous blood samples were used for this study. At the time of sample collection, written informed consent was obtained from all participants for the study. The study was approved by the Ethics Review Committee of the Faculty of Medicine, University of Colombo Sri Lanka (EC-21-030). Consent to publication Not applicable. Data availability statement All the datasets generated and or analyzed during the current study are available in the Additional file section [under Additional file information, Table S1 and Table S2]. The following databases were accessed to obtain the details and FASTA sequence related to the gene of interest: NCBI—Gene—CYP3A5—https://www.ncbi.nlm.nih.gov/gene/1577; NCBI RefSeq—CYP3A5— https://www.ncbi.nlm.nih.gov/nuccore/NG_007938.2?from=5003&to=36805&report=fasta; SNPedia—CYP3A5 SNP— https://www.snpedia.com/index.php/Rs776746. Competing interests The authors declare that there are no competing interests. Funding Not applicable Authors' contributions RS*—study design, study planning, study execution (experiments/analysis laboratory and statistical work) and writing manuscript. All authors read and approved the final manuscript; NN—study design, expert advice, planning, supervision, manuscript review and revision; SP—study design and supervision, manuscript review; TKW—study planning and supervision, manuscript review; VHWD—study design, expert advice, supervision and manuscript review. Acknowledgements The authors extend their sincere appreciation to all staff at the Human Genetics Unit, Faculty of Medicine, University of Colombo for their support. References Mukherjee S, Mukherjee U. A comprehensive review of immunosuppression used for liver transplantation. J transplantation. 2009;2009:701464. Chen L, Prasad GVR. CYP3A5 polymorphisms in renal transplant recipients: influence on tacrolimus treatment. Pharmacogenomics and personalized medicine. 2018;11:23–33. 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Influence of combined CYP3A4 and CYP3A5 single-nucleotide polymorphisms on tacrolimus exposure in kidney transplant recipients: a study according to the post-transplant phase. Pharmacogenomics. 2015;16(18):2045–54. Hyland PL, Freedman ND, Hu N, Tang ZZ, Wang L, Wang C, et al. Genetic variants in sex hormone metabolic pathway genes and risk of esophageal squamous cell carcinoma. Carcinogenesis. 2013;34(5):1062–8. Wang P, Yin T, Ma HY, Liu DQ, Sheng YH, Wang C, et al. Effects of CYP3A4/5 and ABCB1 genetic polymorphisms on carbamazepine metabolism and transport in Chinese patients with epilepsy treated with carbamazepine in monotherapy and bitherapy. Epilepsy Res. 2015;117:52–7. Liang H, Zhang X, Ma Z, Sun Y, Shu C, Zhu Y, et al. Association of CYP3A5 Gene Polymorphisms and Amlodipine-Induced Peripheral Edema in Chinese Han Patients with Essential Hypertension. Pharmacogenomics and personalized medicine. 2021;14:189–97. Additional Declarations No competing interests reported. Supplementary Files AdditionalFileFigureS1.png AdditionalFileFigureS2.png AdditionalFileFigureS3.png AdditionalFileFigureS4.png AdditionalFileTableS1.xlsx AdditionalFileTableS2.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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-2651198","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":190138733,"identity":"52374ea7-3a84-49f3-a52f-ce045e8825f3","order_by":0,"name":"Reema Sameem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYNCDD0DMxk6KDsYZIC3MpGhh5gGTBFSZtzcf3fCDoU5eF8jYbPNrmzwfMwPjh485uLXInDmWdrOH4bDhNiDjdm7fbcM2ZgZmyZnbcGuRkMgxu8HDcIBx240cs9u5PbcZgVrYmHnxaZF//+3mH4Y6+23333+7bdlz256wFgketts8DMyJ224AGQw/bicS1sKTZnZbxuBw8rYzaWY3extuJ7cxMzbj9wv74Wc331TU2W47fvjZjR9/btvOb28++OEjHi0QYAClGdvAZAMh9cjgDymKR8EoGAWjYKQAAA5XUz5AatkuAAAAAElFTkSuQmCC","orcid":"","institution":"Dept. of Anatomy, Genetics \u0026 Bioinformatics, Faculty of Medicine, University of Colombo","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Reema","middleName":"","lastName":"Sameem","suffix":""},{"id":190138734,"identity":"1019378c-5fff-497c-adbe-adbdefaf0e83","order_by":1,"name":"Nafeesa Noordeen","email":"","orcid":"","institution":"Dept. of Anatomy, Genetics \u0026 Bioinformatics, Faculty of Medicine, University of Colombo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nafeesa","middleName":"","lastName":"Noordeen","suffix":""},{"id":190138735,"identity":"7a843175-4e02-4cd8-af72-cb25a08d525e","order_by":2,"name":"Somasundaram Praveenan","email":"","orcid":"","institution":"Dept. of Anatomy, Genetics \u0026 Bioinformatics, Faculty of Medicine, University of Colombo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Somasundaram","middleName":"","lastName":"Praveenan","suffix":""},{"id":190138736,"identity":"30c135b2-2dbc-456f-839c-0fce7485f905","order_by":3,"name":"Tithila Kalum Wetthasinghe","email":"","orcid":"","institution":"Dept. of Anatomy, Genetics \u0026 Bioinformatics, Faculty of Medicine, University of Colombo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tithila","middleName":"Kalum","lastName":"Wetthasinghe","suffix":""},{"id":190138737,"identity":"1f180976-b133-4522-ba77-b5a4d12831dd","order_by":4,"name":"Vajira Harshadeva Weerabaddana Dissanayake","email":"","orcid":"","institution":"Dept. of Anatomy, Genetics \u0026 Bioinformatics, Faculty of Medicine, University of Colombo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vajira","middleName":"Harshadeva Weerabaddana","lastName":"Dissanayake","suffix":""}],"badges":[],"createdAt":"2023-03-03 11:14:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2651198/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2651198/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":35549652,"identity":"dbf1e6c7-0022-4696-999e-b79cdbdb93fe","added_by":"auto","created_at":"2023-04-10 19:48:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109044,"visible":true,"origin":"","legend":"\u003cp\u003eGel band patterns of \u003cem\u003eCYP3A5*3 \u003c/em\u003evariant (rs776746)\u003cem\u003e.\u003c/em\u003e Gel picture of T-ARMS PCR of \u003cem\u003eCYP3A5*3 \u003c/em\u003eT\u0026gt;C genotyping showing control band at 369 bp (Lane 1, 3, 4, 5, 6), variant allele at 142 bp (Lane 1, 3, 4, 5, 6), wild type allele at 284 bp (Lane 3, 4, 5) and 50 bp ladder (Lane 2). Full-length gel image is presented in Supplementary Figure S1.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/8e5e2320a0dc7e5efb92a4b0.jpg"},{"id":35549653,"identity":"ef789273-6978-47d4-a79c-723067e2fdfd","added_by":"auto","created_at":"2023-04-10 19:48:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73895,"visible":true,"origin":"","legend":"\u003cp\u003eGel band patterns of \u003cem\u003eCYP3A5 *1E\u003c/em\u003e variant (rs4646453). Gel picture of T-ARMS PCR of \u003cem\u003eCYP3A5 *1E\u003c/em\u003e C\u0026gt;A genotyping showing control band at 428 bp (Lane 2, 3, 4, 5, 6), variant allele at 291 bp (Lane 2, 4, 6), wild type allele at 180 bp (Lane 2, 3, 4, 5, 6) and 50 bp ladder (Lane 1). Full-length gel image is presented in Supplementary Figure S2.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/379d437838c4984c697c4998.jpg"},{"id":35549743,"identity":"a8eb4b68-9dcd-4a8a-8067-29d5577c6282","added_by":"auto","created_at":"2023-04-10 19:56:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11985,"visible":true,"origin":"","legend":"\u003cp\u003eLinkage disequilibrium coefficients (|D’|) and LD block among the two \u003cem\u003eCYP3A5 \u003c/em\u003evariants rs776746 and rs4646453.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/7d7a9358ad2f1339d903f909.jpg"},{"id":43232562,"identity":"6862aebb-553f-43f0-bb8c-60f7d172fdbf","added_by":"auto","created_at":"2023-09-16 09:07:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":610138,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/b20d0bc6-c6fe-45de-9ede-652a68d4283c.pdf"},{"id":35549744,"identity":"441b1984-6cdc-46ad-8ddc-322f019cdd6d","added_by":"auto","created_at":"2023-04-10 19:56:09","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":92800,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileFigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/940e79cfe2e20c7c241d5d1d.png"},{"id":35549659,"identity":"004e6fb0-3fba-471d-92ac-4ecdc07b1c2f","added_by":"auto","created_at":"2023-04-10 19:48:09","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":136264,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileFigureS2.png","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/f09c136bdfc5d4095b136412.png"},{"id":35549745,"identity":"fb781f07-5231-4797-8a4d-634c81642449","added_by":"auto","created_at":"2023-04-10 19:56:09","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":187161,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileFigureS3.png","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/a4f39e1531ed9a2b30f8443e.png"},{"id":35549660,"identity":"c4f8c8ee-2c4e-4669-a1b3-6089e5eda22f","added_by":"auto","created_at":"2023-04-10 19:48:10","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":189611,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileFigureS4.png","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/261abcd810741c0d5656601f.png"},{"id":35549656,"identity":"d987d396-d65f-4737-9bd2-839658a752a0","added_by":"auto","created_at":"2023-04-10 19:48:09","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":13837,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/bf5c94e3be72c17837e8e967.xlsx"},{"id":35549654,"identity":"c3a8af76-8558-44f8-b5b8-25d50e9a44d3","added_by":"auto","created_at":"2023-04-10 19:48:09","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":12915,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2651198/v1/2663be289c65ddad42a14afd.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Design and Implementation of a Novel Pharmacogenomic Assay to Genotype the CYP3A5*3 (rs776746) and CYP3A5*1E (rs4646453) Genetic Variants","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eTacrolimus is a first-line immunosuppressive drug of the calcineurin inhibitor class and is considered a cornerstone of maintenance drug therapy following solid organ transplantations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Since its approval by the US Food and Drug Administration in 1994, the clinical use of tacrolimus has been complicated due to its high inter-patient variability and narrow therapeutic index (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTacrolimus demonstrates wide inter-individual and inter-ethnic variability, leading to potential graft rejection due to underexposure or toxicity associated with overexposure (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). As the pharmacokinetic profile of tacrolimus varies widely among patients, identifying factors including genetic variants that affect the pharmacokinetic variability of tacrolimus is important (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePharmacogenetic studies have shown the association between the cytochrome P450 3A5 (\u003cem\u003eCYP3A5\u003c/em\u003e) genotype and tacrolimus pharmacokinetics (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Single Nucleotide Polymorphisms (SNPs) in \u003cem\u003eCYP3A5\u003c/em\u003e gene explain 40\u0026ndash;50% of the variability in tacrolimus metabolism and clearance (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The SNP in the \u003cem\u003eCYP3A5\u003c/em\u003e gene involving an A to G transition at position 6986 within intron 3 is the most well studied genomic variant which contributes to tacrolimus metabolism (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The 6986A\u0026thinsp;\u0026gt;\u0026thinsp;G (rs776746), encodes the nonfunctional \u003cem\u003eCYP3A5*3\u003c/em\u003e allele of the \u003cem\u003eCYP3A5\u003c/em\u003e gene, thus \u003cem\u003eCYP3A5*3\u003c/em\u003e induces alternative splicing, followed by protein truncation, resulting in decreased enzymatic activity of \u003cem\u003eCYP3A5\u003c/em\u003e. Consequently, \u003cem\u003eCYP3A5*3\u003c/em\u003e allele is associated with reduced tacrolimus dose requirement (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndividuals homozygous for the mutant \u003cem\u003eCYP3A5*3\u003c/em\u003e allele are referred to as \u003cem\u003eCYP3A5\u003c/em\u003e non-expressers. \u003cem\u003eCYP3A5\u003c/em\u003e non-expression is the most frequent phenotype in most ethnic populations, except blacks (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Accordingly, individuals carrying at least 1 \u003cem\u003eCYP3A5*1\u003c/em\u003e allele, also known as the wild-type allele are identified as \u003cem\u003eCYP3A5\u003c/em\u003e expressers (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The wild-type \u003cem\u003eCYP3A5 *1\u003c/em\u003e allele is correlated with higher production of functional CYP3A5 enzyme, thereby contributing to, higher drug-metabolizing activity by CYP3A overall (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2015, the Clinical Pharmacogenetics Implementation Consortium (CPIC) published the guideline for \u003cem\u003eCYP3A5\u003c/em\u003e genotype and tacrolimus dosing. Although this association is well established, the variable frequency of the \u003cem\u003eCYP3A5*1\u003c/em\u003e allele among different populations makes the utility of the genetic test variable (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Although much effort has been devoted to the better understanding the pharmacogenomics of tacrolimus, the association between \u003cem\u003eCYP3A5\u003c/em\u003e genotypes and tacrolimus response has not yet been studied in the Sri Lankan population. The aim of this study was to design and implement a novel pharmacogenomics assay for the \u003cem\u003eCYP3A5*3\u003c/em\u003e (rs776746) and \u003cem\u003eCYP3A5*1E\u003c/em\u003e (rs4646453) variants known to be associated with tacrolimus response, and to genotype the selected \u003cem\u003eCYP3A5\u003c/em\u003e gene variants in a cohort of healthy Sri Lankan individuals.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe present study was carried out at the Human Genetics Unit (HGU), Faculty of Medicine, University of Colombo. An existing resource of stored venous blood samples obtained from 100 individuals which were collected in EDTA tubes and stored at \u0026minus;\u0026thinsp;20\u0026deg;C were used for the present study. At the time of sample collection, written informed consent was obtained from the study participants for samples to be used for research in future studies. The study parameters were reviewed by the Ethics Review Committee of the Faculty of Medicine, University of Colombo Sri Lanka and ethical clearance was granted to conduct the study (EC-21-030).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 DNA extraction\u003c/h2\u003e \u003cp\u003e Genomic DNA was extracted from peripheral blood specimens of the selected population using QIAamp DNA Blood Mini Kit (QIAGEN, Hilden, Germany) in accordance with the manufacturer\u0026rsquo;s protocol. DNA quantification was performed on 30 randomly selected samples using Quantus\u0026trade; Fluorometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Designing of primers for the selected variants\u003c/h2\u003e \u003cp\u003eTetra primer ARMS PCR primer pairs were designed using Primer1 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://primer1.soton.ac.uk/primer1.html\u003c/span\u003e\u003cspan address=\"http://primer1.soton.ac.uk/primer1.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The mutation points were positioned asymmetrically with respect to the common (outer) primers so that allele specific amplicons with different product lengths could be easily separated by standard agarose gel electrophoresis. The specificity of the designed primers and their melting temperatures were checked by using NCBI Basic Local Alignment Search Tool (BLAST) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/tools/primer-blast/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/tools/primer-blast/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Ensembl BLAST (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asia.ensembl.org/Multi/Tools/Blast?db=core\u003c/span\u003e\u003cspan address=\"https://asia.ensembl.org/Multi/Tools/Blast?db=core\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In-silico PCR run was carried out using the University of California, Santa Cruz (UCSC) Genome Browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genome.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://genome.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Serial cloner software\u0026trade;. The primers used in this study are listed in 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\u003eList of primer sequences, annealing temperatures and length of the amplified segment for rs776746 (T\u0026thinsp;\u0026gt;\u0026thinsp;C) and rs4646453 (C\u0026thinsp;\u0026gt;\u0026thinsp;A).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer Sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmplicon Size (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTm (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGC %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eCYP3A5*3\u003c/em\u003e variant (rs776746) (T\u0026thinsp;\u0026gt;\u0026thinsp;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Inner (C Allele)\u003c/p\u003e \u003cp\u003e5\u0026rsquo;TAATGTGGTCCAAACAGGGAAGAGAGAC 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse Inner (T Allele)\u003c/p\u003e \u003cp\u003e5\u0026rsquo; AGAGCTCTTTTGTCTTTCAA3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Outer\u003c/p\u003e \u003cp\u003e5\u0026rsquo; GAGTTGACCTTCATACGTTCTGTGTG 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e369 (From two outer primers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse Outer\u003c/p\u003e \u003cp\u003e5\u0026rsquo; AAAACATTATGGAGAGTGGCATAGGAG 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eCYP3A5 *1E\u003c/em\u003e variant (rs4646453)\u003c/p\u003e \u003cp\u003e(C\u0026thinsp;\u0026gt;\u0026thinsp;A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Inner (C Allele)\u003c/p\u003e \u003cp\u003e5\u0026rsquo; AAATTCTCATCTTCCTGGAACAC 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse Inner (A Allele)\u003c/p\u003e \u003cp\u003e5\u0026rsquo; TTCTGAAAATGTGCAGGGAT 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Outer\u003c/p\u003e \u003cp\u003e5\u0026rsquo; CTTCCTGATCGGTATGTTTGAT 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e428 (From two outer primers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse Outer\u003c/p\u003e \u003cp\u003e5\u0026rsquo; CTTTTGCTTCTAGCACCGACTA 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.5\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=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Tetra primer ARMS PCR optimization to detect the \u003cem\u003eCYP3A5*3\u003c/em\u003e (rs776746) variant\u003c/h2\u003e \u003cp\u003eA series of optimization reactions were performed on PCR conditions, including annealing temperature, concentrations of the PCR reagents as well as the ratio of outer and inner primers, to increase the PCR product specificity and obtain the expected band sizes. The optimized PCR reaction was performed in a single tube containing 25 \u0026micro;l of reaction volume made up of the following components: 5.0 \u0026micro;L of 5 \u0026times; PCR Buffer, 1.5 \u0026micro;L of MgCl\u003csub\u003e2\u003c/sub\u003e (25 mM), 1.7 \u0026micro;L of dNTP mixture (2.5 mM each), 1 \u0026micro;L of 25 mM \u003cem\u003eCYP3A5*3\u003c/em\u003e CF, 1 \u0026micro;L of 25 mM \u003cem\u003eCYP3A5*3\u003c/em\u003e CR, 2 \u0026micro;L of 25 mM \u003cem\u003eCYP3A5*3\u003c/em\u003e F(C) and 2 \u0026micro;L of 25 mM \u003cem\u003eCYP3A5*3\u003c/em\u003e R(T), 0.3 \u0026micro;L of 5U/ \u0026micro;L Taq DNA polymerase, 9.5 \u0026micro;L of sterile nuclease free water and 1 \u0026micro;L of DNA. The final optimized PCR cycling conditions is as follows: Initial denaturation at 94\u0026deg;C for 5 min followed by 35 cycles of denaturation at 94\u0026deg;C for 1 minute, annealing at 55.5\u0026deg;C for 30 seconds, extension at 72\u0026deg;C for 1 minute and final extension at 72\u0026deg;C for 5 minutes, followed by cooling at 4\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Tetra primer ARMS PCR optimization to detect the \u003cem\u003eCYP3A5 *1E\u003c/em\u003e (rs4646453) variant\u003c/h2\u003e \u003cp\u003eThe optimized PCR was performed in a single tube containing 25 \u0026micro;l of reaction volume made up of the following components: 5.0 \u0026micro;L of 5 \u0026times; PCR Buffer, 1.5 \u0026micro;L of MgCl\u003csub\u003e2\u003c/sub\u003e (25 mM), 1.5 \u0026micro;L of dNTP mixture (2.5 mM each), 0.5 \u0026micro;L of 25 mM \u003cem\u003eCYP3A5*1E\u003c/em\u003e CF, 0.5 \u0026micro;L of 25 mM \u003cem\u003eCYP3A5*1E\u003c/em\u003e CR, 1 \u0026micro;L of 25 mM \u003cem\u003eCYP3A5*1E\u003c/em\u003e F(C), 1 \u0026micro;L of 25 mM \u003cem\u003eCYP3A5*1E\u003c/em\u003e R(A), 0.3 \u0026micro;L of 5U/ \u0026micro;L Taq DNA polymerase, 12.7 \u0026micro;L of sterile nuclease free water and 1 \u0026micro;L of DNA. PCR cycling was performed as follows: Initial denaturation 94\u0026deg;C for 5 min followed by 30 cycles of denaturation at 94\u0026deg;C for 1 minute, annealing at 53.5\u0026deg;C for 30 seconds, extension at 72\u0026deg;C for 1 minute and final extension at 72\u0026deg;C for 5 minutes, followed by cooling at 4\u0026deg;C. Validation of the developed methodology for both gene variants was done by Sanger sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Agarose gel electrophoresis\u003c/h2\u003e \u003cp\u003e The PCR products were separated on 2% agarose gel with DNA size marker of 50 bp followed by staining with ethidium bromide. The samples were run for 50 minutes at 80V to obtain sufficient separation of bands and subsequently visualized by the gel documentation system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Sanger validation\u003c/h2\u003e \u003cp\u003eTo evaluate the accuracy of the assay, selected PCR-amplified DNA samples (n\u0026thinsp;=\u0026thinsp;3, respectively, for each genotype) were sequenced using the Sanger DNA sequencing method (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The PCR products were sequenced with forward and reverse outer primers of 0.8mM concentration using an automated Applied Biosystems 3130 DNA sequencer (Gene Labs, Ninewells Hospital, Sri Lanka). The Sanger validated samples were used as controls to compare the genotyping results of the remaining samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Genotyping of the study population\u003c/h2\u003e \u003cp\u003e Upon validation of the protocol, genotyping of the study population was carried out according to the optimized tetra-primer ARMS PCR assay. A total of 100 patient samples were genotyped for each variant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was conducted using SPSS (version 22; IBM Corp., Armonk, NY, USA) software. The strength of association was evaluated using 95% confidence intervals (CIs). Estimation of allele and genotype frequencies and their deviation from Hardy-Weinberg equilibrium was assessed by Pearson\u0026rsquo;s goodness of fit chi-square test. Continuous variables were shown as mean and standard deviation and qualitative data were expressed as frequency and percentage. Additionally, linkage disequilibrium (LD) block and haplotype were assessed by Haploview 4.1 software. The \u003cem\u003eD\u0026rsquo;\u003c/em\u003e and \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values for all pairs of SNPs were calculated. In all statistical analysis, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as a statistically significant association.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic characteristics of the study population\u003c/h2\u003e \u003cp\u003eThe study population consisted of 54% females and 46% males (Sex ratio\u0026thinsp;=\u0026thinsp;1.17). The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD age of the participants was 19.12\u0026thinsp;\u0026plusmn;\u0026thinsp;15.06.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Results of genotyping\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Genotyping results of the \u003cem\u003eCYP3A5*3\u003c/em\u003e variant rs776746 (T\u0026thinsp;\u0026gt;\u0026thinsp;C)\u003c/h2\u003e \u003cp\u003eAmong the 100 samples genotyped, the \u003cem\u003eCYP3A5*3/*3\u003c/em\u003e genotype was observed in 68% of cases, \u003cem\u003eCYP3A5*1/*3\u003c/em\u003e genotype in 28% of cases, and \u003cem\u003eCYP3A5*1/*1\u003c/em\u003e genotype in 4% of cases. Total allelic frequency was 82% for \u003cem\u003eCYP3A5*3\u003c/em\u003e and 18% for \u003cem\u003eCYP3A5*1\u003c/em\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a representative gel image of the genotyping result. The expected gel band patterns were observed in the gel image results (Control band: 369 bp; band for C allele: 142 bp; band for T allele: 284 bp).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Genotyping results of the \u003cem\u003eCYP3A5 *1E\u003c/em\u003e variant rs4646453 (C\u0026thinsp;\u0026gt;\u0026thinsp;A)\u003c/h2\u003e \u003cp\u003eOut of the 100 individuals genotyped, 55% were heterozygous (A/C), 39% were identified to be homozygous wild type (C/C) while 6% were homozygous variant (A/A) for the \u003cem\u003eCYP3A5 *1E\u003c/em\u003e variant. Total allelic frequency was 66.5% for \u003cem\u003eCYP3A5 *1E\u003c/em\u003e C allele and 33.5% for \u003cem\u003eCYP3A5 *1E\u003c/em\u003e A allele, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a representative gel image of the genotyping result. The expected gel band patterns were observed in the gel image results (Control band: 428 bp; band for C allele: 180 bp; band for A allele: 291 bp).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Results of Sanger validation of the PCR assay\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe Sanger sequencing output results [under Additional file information, Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e and Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e] were analysed using the BioEdit 7.2 software. The published \u003cem\u003eCYP3A5\u003c/em\u003e gene reference sequence (GenBank accession number NC_000007) was obtained from GenBank at the NCBI, USA for alignment and comparison of the nucleotide sequences generated from the random samples.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Phenotype prediction based on \u003cem\u003eCYP3A5\u003c/em\u003e genotype\u003c/h2\u003e \u003cp\u003eThe frequency of each genotype identified in the study population categorized by gender is shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe frequency of each genotype identified in the study population categorized by gender.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNumber of Subjects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariant allele frequency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers776746\u003c/p\u003e \u003cp\u003e(T\u0026thinsp;\u0026gt;\u0026thinsp;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomozygous Wild type (T/T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.18 \u003c/p\u003e \u003cp\u003e(χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.265) (p\u0026thinsp;=\u0026thinsp;0.876)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomozygous Variant (C/C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeterozygous Variant (C/T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers4646453 (C\u0026thinsp;\u0026gt;\u0026thinsp;A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomozygous Wild type (C/C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003cp\u003e(χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.496)\u003c/p\u003e \u003cp\u003e(p\u0026thinsp;=\u0026thinsp;0.064)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomozygous Variant (A/A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeterozygous Variant (C/A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo deviation from Hardy-Weinberg equilibrium was observed for the genotype frequencies (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Based on the genotype results obtained for the \u003cem\u003eCYP3A5\u003c/em\u003e rs776746 variant the participants could be grouped in to 3 distinct phenotypes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCYP3A5\u003c/em\u003e genotype and frequency of expected phenotype in a cohort of Sri Lankan patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCYP3A5\u003c/em\u003e Genotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLikely Phenotype\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e*1/*1\u003c/em\u003e (T/T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtensive Metabolizer (\u003cem\u003eCYP3A5\u003c/em\u003e Expresser)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e*1/*3\u003c/em\u003e (C/C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntermediate Metabolizer (\u003cem\u003eCYP3A5\u003c/em\u003e Expresser)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e*3/*3\u003c/em\u003e (C/C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor Metabolizer (\u003cem\u003eCYP3A5\u003c/em\u003e Non- Expresser)\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=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Linkage disequilibrium and haplotype analysis of two \u003cem\u003eCYP3A5\u003c/em\u003e gene variants\u003c/h2\u003e \u003cp\u003eThe Haploview program was used to assess the linkage disequilibrium (LD) block and haplotype of the two \u003cem\u003eCYP3A5\u003c/em\u003e variants. The LD analysis of rs776746 and rs4646453 showed an association with each other \u003cem\u003eD\u0026rsquo;\u003c/em\u003e=0.486 \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.103 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicating significant LD between loci (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe most represented haplotype in the whole cohort was CC, followed by CA, TA and TC, as listed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHaplotype frequencies of two \u003cem\u003eCYP3A5\u003c/em\u003e variants rs776746 and rs4646453\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaplotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.061\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"},{"header":"4.0 Discussion","content":"\u003cp\u003eThe detection of \u003cem\u003eCYP3A5\u003c/em\u003e variant alleles, and knowledge about their allelic frequency in specific ethnic groups, is important to optimize pharmacotherapy (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In this study a novel genotyping assay was developed for the identification \u003cem\u003eCYP3A5*3\u003c/em\u003e (rs776746) and \u003cem\u003eCYP3A5*1E\u003c/em\u003e (rs4646453) variants in a sample cohort. Compared with other routinely used methods, tetra primer ARMS-PCR assay could be beneficial in terms of total time, cost and applicability in a genetic diagnostic laboratory (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Hence the tetra-primer ARMS PCR technique was used in this study as it was found to be an accurate and robust technique for genotyping the \u003cem\u003eCYP3A5\u003c/em\u003e variants.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCYP3A5\u003c/em\u003e is highly polymorphic with significant inter-individual variation in the enzyme activity contributing to the absorption, metabolism and tissue distribution of drugs (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Homozygous carriers \u003cem\u003e(*3/*3\u003c/em\u003e or CC) of this variant lack functional \u003cem\u003eCYP3A5\u003c/em\u003e protein because of the frame shift mutation and truncation of the translated protein. Previous studies have confirmed that \u003cem\u003eCYP3A5*3\u003c/em\u003e is associated with drug metabolism, and \u003cem\u003eCYP3A5\u003c/em\u003e*3/*3 carriers have decreased metabolism of tacrolimus, compared to \u003cem\u003eCYP3A5\u003c/em\u003e*1/*1 and \u003cem\u003eCYP3A5\u003c/em\u003e*1/*3 carriers (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). As a result, patients with \u003cem\u003eCYP3A5\u003c/em\u003e*3/*3 genotype, treated with tacrolimus may have an increased risk of nephrotoxicity as compared to patients without it (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). More recently, scientists have identified that the \u003cem\u003eCYP3A5*1E\u003c/em\u003e (rs4646453) variant remarkably contributes to the tacrolimus pharmacokinetics (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study investigated screening of the \u003cem\u003eCYP3A5\u003c/em\u003e variant alleles among 100 individuals, the results demonstrate that the \u003cem\u003eCYP3A5*3\u003c/em\u003e allele is abundantly present in the sample population, displaying an allelic frequency of 82%. There was a statistically significant association (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the allele frequency reported in this study and \u003cem\u003eCYP3A5*3\u003c/em\u003e allele frequency data reported for global and East Asian ethnic groups from the gnomAD Genomes, 1000 Genomes and ALFA databases. On the basis of the \u003cem\u003eCYP3A5*3\u003c/em\u003e variant alleles detected in the study group of 100 samples, it was deduced that 68% may have low expression of \u003cem\u003eCYP3A5\u003c/em\u003e. This was in agreement with previous studies involving South Indian patients, where 60% of the population was identified to have the \u003cem\u003eCYP3A5*3/*3\u003c/em\u003e genotype (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). An East Asian study, focusing on the Japanese population, had recorded a similar \u003cem\u003eCYP3A5*3/*3\u003c/em\u003e genotype frequency of 60.5% (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Moreover, these results were consistent with a recent study of Tunisian patients, displaying 63.5% of \u003cem\u003eCYP3A5*3/*3\u003c/em\u003e genotype and 80.7% of the most frequently occurring \u003cem\u003eCYP3A5*3\u003c/em\u003e variant allele (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eScreening of 100 individuals for the \u003cem\u003eCYP3A5*1E\u003c/em\u003e (rs4646453) variant alleles revealed that the \u003cem\u003eCYP3A5*1E\u003c/em\u003e C allele was commonly found in the sample population, demonstrating an allelic frequency of 66.5%. The allele frequency of the rs4646453 variant recorded in the study population showed significant association (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with the \u003cem\u003eCYP3A5*1E\u003c/em\u003e allele frequency data reported for South Asian and East Asian ethnic groups from the gnomAD Genomes, 1000 Genomes and ALFA databases. As the rs4646453 is a novel \u003cem\u003eCYP3A5\u003c/em\u003e variant been explored by scientists, there is limited literature with respect to specific ethnic groups. Thus far, this variant has been recorded in the Chinese population and has shown to significantly affect tacrolimus metabolism (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The observed \u003cem\u003eCYP3A5\u003c/em\u003e genotype frequency distributions for the rs776746 and rs4646453 variants in the study population was consistent with the Hardy\u0026ndash;Weinberg equilibrium (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe LD analysis indicated a significant LD between the loci of the two SNPs, rs776746 and rs4646453 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings are consistent with previous studies (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) which strengthens the confidence of the generalizability of the results. The findings of the present study further promote that the presence of LDs with rs776746 may partly explain the role of rs4646453 in tacrolimus metabolism.\u003c/p\u003e \u003cp\u003eThe present study has several limitations primarily due to its single-centred retrospective nature. Future studies should be initiated to investigate the genotype-phenotype relationship of the identified variants in the Sri Lankan population to better understand how variant alleles contribute to different drug responses. Hence, more case\u0026ndash;control studies with large number of multi-ethnic samples and diversified factors are necessary to comprehensively evaluate the involvement of the \u003cem\u003eCYP3A5\u003c/em\u003e variants on tacrolimus metabolism.\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eIn conclusion, a novel T-ARMS PCR assay was successfully designed, optimized and validated to genotype the \u003cem\u003eCYP3A5*3\u003c/em\u003e (rs776746) and \u003cem\u003eCYP3A5*1E\u003c/em\u003e (rs4646453) variants and could be implemented as a cost-effective technique.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALFA\u0026nbsp; \u0026nbsp; \u0026nbsp;Allele Frequency Aggregator\u003c/p\u003e\n\u003cp\u003eARMS\u0026nbsp; \u0026nbsp;\u0026nbsp;Amplification Refractory Mutation System\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBLAST\u0026nbsp;\u0026nbsp;Basic Local Alignment Search Tool\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence Interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCPIC\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Clinical Pharmacogenetics Implementation Consortium\u003c/p\u003e\n\u003cp\u003eCYP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cytochromes P450\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCYP3A5 Cytochrome P450 3A5\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Deoxyribonucleic Acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003edNTPs\u0026nbsp; \u0026nbsp;\u0026nbsp;Deoxyribose Nucleotide Triphosphate\u003c/p\u003e\n\u003cp\u003eEDTA\u0026nbsp; \u0026nbsp; \u0026nbsp;Ethylenediaminetetraacetic Acid\u003c/p\u003e\n\u003cp\u003eCF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Control Forward\u003c/p\u003e\n\u003cp\u003eCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Control Reverse\u003c/p\u003e\n\u003cp\u003eHGU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Human Genetics Unit\u003c/p\u003e\n\u003cp\u003eHWE\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hardy\u0026ndash;Weinberg Equilibrium\u003c/p\u003e\n\u003cp\u003eLD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Linkage Disequilibrium\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMgCl2\u0026nbsp; \u0026nbsp;\u0026nbsp;Magnesium Chloride\u003c/p\u003e\n\u003cp\u003eNCBI\u0026nbsp; \u0026nbsp; \u0026nbsp;National Center for Biotechnology Information\u003c/p\u003e\n\u003cp\u003eNGS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Next Generation Sequencing\u003c/p\u003e\n\u003cp\u003ePCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003eF(C) \u0026nbsp; \u0026nbsp;Forward primer for C allele\u003c/p\u003e\n\u003cp\u003eR(T) \u0026nbsp; Reverse primer for T allele\u003c/p\u003e\n\u003cp\u003eR(A) Reverse primer for A allele\u003c/p\u003e\n\u003cp\u003eSD Standard Deviation\u003c/p\u003e\n\u003cp\u003eSNPs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Single Nucleotide Polymorphism\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTBE buffer\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tris/Borate/EDTA buffer\u003c/p\u003e\n\u003cp\u003eTE buffer\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tris EDTA\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUCSC \u0026nbsp; \u0026nbsp; University of California, Santa Cruz\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant institutional guidelines and regulations.\u0026nbsp;An existing resource of stored venous blood samples were used for this study. At the time of sample collection, written informed consent was obtained from all participants for the study. The study was approved by the Ethics Review Committee of the Faculty of Medicine, University of Colombo Sri Lanka (EC-21-030).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the datasets generated and or analyzed during the current study are available in the Additional file section [under Additional file information, Table S1 and Table S2].\u003c/p\u003e\n\u003cp\u003eThe following databases were accessed to obtain the details and FASTA sequence related to the gene of interest: NCBI\u0026mdash;Gene\u0026mdash;CYP3A5\u0026mdash;https://www.ncbi.nlm.nih.gov/gene/1577; NCBI RefSeq\u0026mdash;CYP3A5\u0026mdash;\u0026nbsp;https://www.ncbi.nlm.nih.gov/nuccore/NG_007938.2?from=5003\u0026amp;to=36805\u0026amp;report=fasta; SNPedia\u0026mdash;CYP3A5 SNP\u0026mdash;\u0026nbsp;https://www.snpedia.com/index.php/Rs776746.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRS*\u0026mdash;study design, study planning, study execution (experiments/analysis laboratory and statistical work) and writing manuscript. All authors read and approved the final manuscript; NN\u0026mdash;study design, expert advice, planning, supervision, manuscript review and revision; SP\u0026mdash;study design and supervision, manuscript review; TKW\u0026mdash;study planning and supervision, manuscript review; VHWD\u0026mdash;study design, expert advice, supervision and manuscript review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their sincere appreciation to all staff at the Human Genetics Unit, Faculty of Medicine, University of Colombo for their support.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMukherjee S, Mukherjee U. A comprehensive review of immunosuppression used for liver transplantation. J transplantation. 2009;2009:701464.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Prasad GVR. 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Biology of blood and marrow transplantation: journal of the American Society for Blood and Marrow Transplantation. 2019;25(4):656\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci USA. 1977;74(12):5463\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Schaik RH, van der Heiden IP, van den Anker JN, Lindemans J. CYP3A5 variant allele frequencies in Dutch Caucasians. Clin Chem. 2002;48(10):1668\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang H-W, Chuang L-Y, Cheng Y-H, Hung Y-C, Wen C-H, Gu D-L, et al. Prim-SNPing: A primer designer for cost-effective SNP genotyping. Biotechniques. 2009;46:421\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshavaid T, Raje H, Shah B, Shah S. 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Transplantation. 2010;90(12):1394\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Zheng M, Yang H, Han Z, Tao J, Chen H, et al. Association of Genetic Variants in CYP3A4, CYP3A5, CYP2C8, and CYP2C19 with Tacrolimus Pharmacokinetics in Renal Transplant Recipients. Curr Drug Metab. 2019;20(7):609\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarasamma S. Pharmacogenomics of CYP3A5 Polymorphism: Predicting Dose-adjusted Trough Levels of Tacrolimus in South Indian Renal Transplant Patients. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukuen S, Fukuda T, Maune H, Ikenaga Y, Yamamoto I, Inaba T, et al. Novel detection assay by PCR-RFLP and frequency of the CYP3A5 SNPs, CYP3A5*3 and *6, in a Japanese population. Pharmacogenetics. 2002;12(4):331\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAouam K, Kolsi A, Kerkeni E, Ben Fredj N, Chaabane A, Monastiri K, et al. 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Association of CYP3A5 Gene Polymorphisms and Amlodipine-Induced Peripheral Edema in Chinese Han Patients with Essential Hypertension. Pharmacogenomics and personalized medicine. 2021;14:189\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tacrolimus, CYP3A5, tetra-primer ARMS PCR, genotype, pharmacogenetic assay","lastPublishedDoi":"10.21203/rs.3.rs-2651198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2651198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe cytochrome P450 3A5 CYP3A5 enzymes are important for metabolizing the drug tacrilomus, an immunosuppressive agent used in solid organ transplantation. Genetic variants in the \u003cem\u003eCYP3A5\u003c/em\u003e gene are significant determinants of tacrolimus efficacy. The present study was undertaken to design a novel pharmacogenetic assay (Single step-Tetra Arms Polymerase Chain Reaction) to study the distribution of the \u003cem\u003eCYP3A5*3\u003c/em\u003e (rs776746) and \u003cem\u003eCYP3A5*1E\u003c/em\u003e (rs4646453) variants by genotyping a cohort of healthy individuals.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eCYP3A5*3\u003c/em\u003e variant was the most frequent allele detected at 82% and the \u003cem\u003eCYP3A5*1E\u003c/em\u003e C allele was found in 66.5% of the samples. The allele frequencies of \u003cem\u003eCYP3A5*3\u003c/em\u003e (rs776746) and \u003cem\u003eCYP3A5*1E\u003c/em\u003e (rs4646453) were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) when compared with the Asian ethnic group. The observed \u003cem\u003eCYP3A5\u003c/em\u003e genotype frequency distributions for the \u003cem\u003eCYP3A5*3\u003c/em\u003e (rs776746) and \u003cem\u003eCYP3A5*1E\u003c/em\u003e (rs4646453) variants in the study population were consistent with the Hardy\u0026ndash;Weinberg equilibrium (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For the \u003cem\u003eCYP3A5*3\u003c/em\u003e variant the frequency of the T/T [extensive metabolizer], C/T [intermediate metabolizer] and C/C [poor metabolizer] variants were 4%, 28% and 68% respectively. Furthermore, a significant linkage disequilibrium among rs4646453 and rs776746 was identified (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA novel tetra-primer ARMS PCR assay was successfully designed and implemented for genotyping of the \u003cem\u003eCYP3A5\u003c/em\u003e variants \u003cem\u003eCYP3A5*3\u003c/em\u003e (rs776746) and \u003cem\u003eCYP3A5*1E\u003c/em\u003e (rs4646453). These pharmacogenomic assays could be offered to patients to predict their response to tacrolimus.\u003c/p\u003e","manuscriptTitle":"The Design and Implementation of a Novel Pharmacogenomic Assay to Genotype the CYP3A53 (rs776746) and CYP3A51E (rs4646453) Genetic Variants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-10 19:48:04","doi":"10.21203/rs.3.rs-2651198/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8047c44-b185-4757-bf29-d9a95da1a619","owner":[],"postedDate":"April 10th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-09-16T08:59:16+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-10 19:48:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2651198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2651198","identity":"rs-2651198","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}