Inconsistent temporal patterns of genetic variation of HCV among high-risk subjects may impact inference of transmission networks

Rebecca Rose, Christopher Rodriguez, James Jarad Dollar, Susanna L. Lamers, Guido Massaccesi, William Osburn, Stuart C. Ray, David L. Thomas, Andrea L. Cox, Oliver Laeyendecker

Research output: Contribution to journalArticle

Abstract

Hepatitis-C Virus (HCV) sequences are often used to establish networks of people who inject drugs (PWID). However, the degree to which within-host evolutionary dynamics affect those inferences has not been carefully studied. Here, we analyzed 702 longitudinally-sampled HCV E1 sequences from 88 HCV+ people who inject drugs (PWID) in the Baltimore Before and After Acute Study of Hepatitis (BBAASH) cohort. Individuals were tested for HCV RNA over multiple visits to the clinic, and the HCV E1 gene was sequenced for HCV+ samples. Genetic clustering was performed on the full set of sequences using a 3% genetic distance threshold to define epidemiological linkage. Maximum-likelihood (ML) phylogenies were inferred to assess evolutionary relationships. We found 22 clusters containing sequences sampled over five or more years (long-term clusters, LTC), of which 17 had >1 subject. In six of the multi-subject LTC, one subject had a sequence sampled >3 years earlier or later than the next-closest subject in the cluster (time-gap LTC). ML trees showed that, in three of the time-gap LTC, two subjects had identical sequences despite 7–10 years separating the sampling times. In four of the time-gap LTC for whom additional data were available, the subject with the later detected shared variant had both different variants and visits with no detectable HCV RNA (RNA-) prior to the appearance of the shared variant. In the subject with the earlier detection of the shared variant, different variants and RNA- visits were also detected in multiple cases subsequent to appearance of the shared variant. Complex patterns of shared viral variation among PWID reflect on-going re-infection, multiple transmission partners, and/or inconsistent detection of viral variants. Our results suggest that transmission events are currently underestimated by analysis of sequences at a single point in time.

Original languageEnglish (US)
Pages (from-to)1-6
Number of pages6
JournalInfection, Genetics and Evolution
Volume71
DOIs
StatePublished - Jul 2019

Keywords

  • Clustering
  • Epidemiology
  • Evolutionary dynamics
  • Evolutionary rate
  • Phylogeny

ASJC Scopus subject areas

  • Microbiology
  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics
  • Microbiology (medical)
  • Infectious Diseases

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