Wei, X.; Li, H.; Sai, L.; Song, L.; Gao, X.; Li, C.; Zhang, X.; Shen, Y.; Li, S.; Liu, L.; Golubchik, T.; Xu, Y.

Severe fever with thrombocytopenia syndrome virus (SFTSV) is an emerging tick-borne virus that can cause severe clinical symptoms and high fatality. However, the dynamics of SFTSV intra-host diversity during infection and its association with disease severity remain unexplored. Here, we characterized SFTSV iSNVs in longitudinal clinical samples from patients with distinct clinical outcomes. Our analyses revealed that SFTSV intra-host diversity was associated with disease severity and showed distinct patterns in severe and mild cases. Severe cases exhibited paradoxically lower SFTSV intra-host diversity despite higher viral loads, likely driven by reduced selection pressure. Notably, iSNVs linked to viral replication emerged in severe cases, while iSNVs associated with immunity invasion arose in mild cases. Machine learning models showed that intra-host diversity can contribute to classification of SFTS by severity. Our findings, through the lens of within-host viral populations, highlight variations in viral-host interactions during SFTSV infection and provide potential biomarkers for predicting SFTS severity.