0709 203000 - Nairobi 0709 983000 - Kilifi
0709 203000 - NRB 0709 983000 - Kilifi
0709 203000 - NRB | 0709 983000 - Kilifi

Abstract

Validation of high-throughput oxford nanopore technology for HIV-1 transmitted/founder virus identification

Byamukama D Ndekezi C Omara D Nakyanzi A Natwijuka F Kato F Mugaba S Kimuda MP Kapaata A Nduati E Kaleebu P Balinda SN
Int J Infect Dis. 2025;161108138

Permenent descriptor
https://doi.org/10.1016/j.ijid.2025.108138


HIV-1 Transmitted/Founder (T/F) viruses cause 80-90% of heterosexual transmissions, making their rapid identification vital for vaccine and cure development. Single-genome amplification (SGA) followed by Sanger sequencing is the gold standard for T/F virus detection, but low throughput and high cost limit its scalability. Here, we evaluated the Oxford Nanopore Technology (ONT) as a high-throughput alternative. We sequenced 195 archived HIV-1 single genome amplicons (SGAs) from 20 acutely infected participants, encompassing both 3' and 5' genome halves. Libraries were prepared via end repair, native barcoding, and adapter ligation, and then sequenced on a MinION MK1C device with R9.4 flow cells. Data processing included read filtering, error correction, and haplotype reconstruction. T/F viruses were identified using Highlighter plots and by applying a criterion of intra-patient mean pairwise diversity <0.60%, together with phylogenetic clustering. The sequence most closely related to the most recent common ancestor (MRCA) was designated as the T/F virus. Phylogenetic analysis showed strong concordance between ONT and Sanger sequences, with 100% bootstrap support. ONT identified 35 of 39 T/F viruses detected by Sanger, achieving 89.70% sensitivity. Sequence similarity between ONT and Sanger - derived T/Fs averaged 99.81% (95% CI: 99.76-99.87%), ranging from 99.45-99.96%. These findings demonstrate ONT's promise as a reliable, high-throughput alternative for HIV-1 T/F identification. Advances such as the Dorado basecaller and Q20+ chemistry are expected to further improve ONT's accuracy, supporting its use in large-scale, resource-limited settings.