Multiple Approaches to Genetic Sequencing to Identify Hepatitis C Virus Reinfection Among People Who Inject Drugs

The burden of hepatitis C virus (HCV) among persons who inject drugs is determined by dynamics of infection, spontaneous clearance, treatment clearance, treatment failure, and reinfection. We used deep sequencing by Nanopore to study sequences of persons with and without self-reported HCV treatment. Even after years of evolution, sequences from the same person were always more similar than sequences from different persons and a Hamming distance threshold of 0.064 reliably differentiated (AUC 0.999) the groups. By comparison to sequences before treatment, identification of unique sequences (distance > 0.064) after treatment reliably identified 8 of 28 instances of post-treatment reinfection. There were multiple causes for finding the same (distance < 0.064) sequence after intended treatment including not commencing or abbreviating treatment, pharmacological treatment failure, or possibly reinfection from same source. These data underscore the value of HCV sequence analysis in understanding viral dynamics among PWID.

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Publication Details

Journal
The Journal of Infectious Diseases
Published
2025-05-14
DOI
https://doi.org/10.1093/infdis/jiaf235
Primary Topic
Hepatitis C virus research
Type
article
Field-Weighted Citation Impact
0.00

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article

Multiple Approaches to Genetic Sequencing to Identify Hepatitis C Virus Reinfection Among People Who Inject Drugs

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The Journal of Infectious Diseases
Hepatitis C virus research
article

Multiple Approaches to Genetic Sequencing to Identify Hepatitis C Virus Reinfection Among People Who Inject Drugs

Oluwaseun O Falade-Nwulia, Shruti H. Mehta, Kyra H. Grantz, Abraham J. Kandathil, Amy Wesolowski, Javier Cepeda, Jeffrey Quinn, Gregory Dale Kirk, Raghavendran Anantharam, David L. Thomas, Jacqueline Astemborski
article en

Abstract

The burden of hepatitis C virus (HCV) among persons who inject drugs is determined by dynamics of infection, spontaneous clearance, treatment clearance, treatment failure, and reinfection. We used deep sequencing by Nanopore to study sequences of persons with and without self-reported HCV treatment. Even after years of evolution, sequences from the same person were always more similar than sequences from different persons and a Hamming distance threshold of 0.064 reliably differentiated (AUC 0.999) the groups. By comparison to sequences before treatment, identification of unique sequences (distance > 0.064) after treatment reliably identified 8 of 28 instances of post-treatment reinfection. There were multiple causes for finding the same (distance < 0.064) sequence after intended treatment including not commencing or abbreviating treatment, pharmacological treatment failure, or possibly reinfection from same source. These data underscore the value of HCV sequence analysis in understanding viral dynamics among PWID.

The Journal of Infectious DiseasesVol. 232(4)
Johns Hopkins University (US), Johns Hopkins Medicine (US)
Center for AIDS Research, Johns Hopkins University, National Institute on Drug Abuse
Good health and well-being
Openalex Percentile: Top 84%
Hepatitis C virus research
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