bioRxivMicrobiologyPreprint
Single-cell transcriptional changes of artemisinin-sensitive K13C580 and artemisinin-resistant K13580Y Plasmodium falciparum upon dihydroartemisinin exposure
S. V. Connelly, C. Cunningham, I. Petrow, N. Rustamzade, J. Zuromski, D. M. Sipolski, C. P. Nixon, J. D. Kurtis, J. J. Juliano, J. A. Bailey, C. I. Oduor
Abstract
Artemisinin-based therapies have been central to malaria control, but partial resistance in Plasmodium falciparum, driven by mutations in the Kelch13 (K13) protein, threatens these gains. To investigate the molecular basis of this resistance, we applied single-cell RNA sequencing to coisogenic parasite lines, K13 wild-type (K13C580) and the artemisinin-resistant mutant (K13580Y), following a 6 hour pulse of dihydroartemisinin (DHA). This approach enabled high-resolution profiling across intraerythrocytic stages. Both lines exhibited stage-specific transcriptional responses, with pronounced changes in ring and trophozoite stages. Using Manifold Enhancement of Latent Dimensions (MELD), a computational framework for quantifying transcriptional perturbation, DHA-treatment induces stage-specific differences in protein export and metabolic pathways in K13C580 and K13580Y parasites, relating to an altered metabolic stress response state. GARP, a potential therapeutic target, was highly differentially expressed in untreated ring stages of K13580Y and K13C580. Functional assays confirmed that anti-GARP antibodies retained efficacy against K13580Y, supporting its potential as a therapeutic target. These findings provide a comprehensive view of the cellular responses related to artemisinin resistance, identify molecular features of pathogenesis, and highlight surface proteins like GARP as promising intervention targets. This work underscores the power of single-cell approaches to dissect drug responses and guide strategies to overcome resistant parasites.
- organism: plant (other)
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: deep learning
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