Ultrarapid and high-resolution HLA class I typing using transposase-based nanopore sequencing applied in pharmacogenetic testing

Front Genet. 2023 Jun 23:14:1213457. doi: 10.3389/fgene.2023.1213457. eCollection 2023.

Abstract

Nanopore sequencing has been examined as a method for rapid and high-resolution human leukocyte antigen (HLA) typing in recent years. We aimed to apply ultrarapid nanopore-based HLA typing for HLA class I alleles associated with drug hypersensitivity, including HLA-A*31:01, HLA-B*15:02, and HLA-C*08:01. Most studies have used the Oxford Nanopore Ligation Sequencing kit for HLA typing, which requires several enzymatic reactions and remains relatively expensive, even when the samples are multiplexed. Here, we used the Oxford Nanopore Rapid Barcoding kit, which is transposase-based, with library preparation taking less than 1 h of hands-on time and requiring minimal reagents. Twenty DNA samples were genotyped for HLA-A, -B, and -C; 11 samples were from individuals of different ethnicity and nine were from Thai individuals. Two primer sets, a commercial set and a published set, were used to amplify the HLA-A, -B, and -C genes. HLA-typing tools that used different algorithms were applied and compared. We found that without using several third-party reagents, the transposase-based method reduced the hands-on time from approximately 9 h to 4 h, making this a viable approach for obtaining same-day results from 2 to 24 samples. However, an imbalance in the PCR amplification of different haplotypes could affect the accuracy of typing results. This work demonstrates the ability of transposase-based sequencing to report 3-field HLA alleles and its potential for race- and population-independent testing at considerably decreased time and cost.

Keywords: ADR; HLA-B*15:02; Thai; human leukocyte antigen; long-read sequencing; nanopore; pharmacogenomics; turnaround time.

Grants and funding

This research project was supported by the Faculty of Medicine Siriraj Hospital, Mahidol University, Grant No. R016533028; the Faculty of Associated Medical Sciences, Chiang Mai University, Thailand (R000030375); and the NSRF via the Program Management Unit for Human Resources and Institutional Development, Research Innovation (Grant No. B13F660073). The data processing facility was supported by the Office of the Ministry of Higher Education, Science, Research, and Innovation under the Reinventing University project: the Center of Excellence in AI-Based Medical Diagnosis (AI-MD) sub-project. Editorial support was provided by the Science Communication Group at the University of Arkansas for Medical Sciences, United States.