Accurate genetic detection of hepatitis C virus transmissions in outbreak settings.

Campo DS1, Xia GL1, Dimitrova Z1, Lin Y1, Forbi JC1, Ganova-Raeva L1, Punkova L1, Ramachandran S1, Thai H1, Skums P1, Sims S1, Rytsareva I1, Vaughan G1, Roh HJ1, Purdy MA1, Sue A1, Khudyakov Y1.

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Abstract

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections are associated with unsafe injection practices, drug diversion, and other exposures to blood, being difficult to detect and investigate. Here, we developed and validated a simple approach for molecular detection of HCV transmissions in outbreak settings. We obtained sequences from the HCV hypervariable region 1 (HVR1) using End-Point Limiting-Dilution (EPLD) from 127 cases involved in 32 epidemiologically defined HCV outbreaks and 193 individuals with unrelated HCV strains. We compared several types of genetic distances and calculated a threshold using minimal Hamming distances that identifies transmission clusters in all tested outbreaks with 100% accuracy. The approach was also validated on sequences from 239 individuals obtained using next-generation sequencing, showing the same accuracy as EPLD. In average, nucleotide diversity of the intra-host population was 6.2-times greater in the source than in any incident case, allowing the correct detection of transmission direction in 8 outbreaks for which source cases were known. A simple and accurate distance-based approach for detecting HCV transmissions developed here streamlines molecular investigation of outbreaks, thus improving the public health capacity for rapid and effective control of hepatitis C.

Published by Oxford University Press for the Infectious Diseases Society of America 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.