Understanding Plasmodium vivax recurrent infections using an amplicon deep sequencing assay, PvAmpSeq, identity-by-descent and model-based classification

作者：Jason Rosado, Jiru Han, Thomas Obadia, Jacob E. Munro, Zeinabou Traore, Kael L. Schoffer, Jessica Brewster, Caitlin Bourke, Joseph M. Vinetz, Aimee R. Taylor, Michael White, Melanie Bahlo, Dionicia Gamboa, Ivo Müeller, Shazia Ruybal‐Pesántez · 发表于：medRxiv · 年份：2025 · DOI：10.1101/2025.05.26.25327775 · 被引用次数：4 · 研究领域：Mycobacterium research and diagnosis、Bacterial Identification and Susceptibility Testing、Genomics and Phylogenetic Studies

Summary Plasmodium vivax infections are characterised by recurrent bouts of blood-stage parasitaemia. Understanding the genetic relatedness of recurrences can help distinguish whether these are caused by relapse, reinfection, or recrudescence, which is critical to understand treatment efficacy and transmission dynamics. We developed PvAmpseq, an amplicon sequencing assay targeting 11 SNP-rich regions of the P. vivax genome. PvAmpSeq was applied to field isolates from a clinical trial in the Solomon Islands and a longitudinal observational cohort in Peru, and statistical models were applied for genetic classification of recurrences. In the Solomon Islands trial, where participants received antimalarials at baseline, half of the recurrent infections were caused by parasites with >50% relatedness to the baseline infection (identity-by-descent), with statistical models providing further classification as probable relapses and recrudescences, although with wide uncertainty. In the Peruvian cohort, half of the recurrent infections were caused by parasites with >22% relatedness to the baseline infection. PvAmpSeq provides high-resolution genotyping to characterise P. vivax recurrences, offering insights into transmission and treatment outcomes. We also discuss the nuances and limitations of available statistical methods for the classification of P. vivax genotyping data. Graphical abstract Created in BioRender. Rosado, J. (2026) https://BioRender.com/p66i686