Abstract
HIV-1 genetic diversity can be used to infer time since infection (TSI) and infection recency. We adapted this approach for HCV and identified genomic regions with informative diversity. We included 72 HCV/HIV-1 coinfected participants of the Swiss HIV Cohort Study, for whom reliable estimates of infection date and viral sequences were available. Average pairwise diversity (APD) was calculated over each codon position for the entire open reading frame of HCV. Utilizing cross validation, we evaluated the correlation of APD with TSI, and its ability to infer TSI via a linear model. We additionally studied the ability of diversity to classify infections as recent (infected for <1 year) or chronic, using receiver-operator-characteristic area under the curve (ROC-AUC) in 50 patients whose infection could be unambiguously classified as either recent or chronic. Measuring HCV diversity over third or all codon positions gave similar performances, and notable improvement over first or second codon positions. APD calculated over the entire genome enabled classification of infection recency (ROC-AUC = 0.76). Additionally, APD correlated with TSI (R2 = 0.33) and could predict TSI (mean absolute error = 1.67 years). Restricting the region over which APD was calculated to E2-NS2 further improved accuracy (ROC-AUC = 0.85, R2 = 0.54, mean absolute error = 1.38 years). Genetic diversity in HCV correlates with TSI and is a proxy for infection recency and TSI, even several years post-infection.
Highlights
Inferring the duration of infection is of key importance for understanding both the epidemiology and pathogenesis of hepatitis C virus (HCV) infections
We examined how well average pairwise diversity correlated with time since infection (TSI)
We studied how well average pairwise diversity could be used to infer infection recency using receiver operator characteristics (ROC) analyses, with recent infection (
Summary
Inferring the duration of infection is of key importance for understanding both the epidemiology and pathogenesis of hepatitis C virus (HCV) infections. The time of infection can inform incidence assays, phylogenetic studies, and prediction of future chronic liver disease burdens. It could be vital for monitoring public health progress in the context of elimination [1], as it enables the identification of ongoing transmission. Studies typically have to rely on some combination of cohort data and mathematical modelling to infer infection dates [2,3,4,5,6,7,8,9,10,11,12,13], which remain highly uncertain for most HCV-infected individuals. We took advantage of the unique opportunity of a cohort with annual HCV screening, detailed clinical characteristics, and sampling
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