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Next-generation sequencing reveals large connected networks of intra-host HCV variants
BACKGROUND: Next-generation sequencing (NGS) allows for sampling numerous viral variants from infected patients. This provides a novel opportunity to represent and study the mutational landscape of Hepatitis C Virus (HCV) within a single host. RESULTS: Intra-host variants of the HCV E1/E2 region wer...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4120142/ https://www.ncbi.nlm.nih.gov/pubmed/25081811 http://dx.doi.org/10.1186/1471-2164-15-S5-S4 |
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author | Campo, David S Dimitrova, Zoya Yamasaki, Lilian Skums, Pavel Lau, Daryl TY Vaughan, Gilberto Forbi, Joseph C Teo, Chong-Gee Khudyakov, Yury |
author_facet | Campo, David S Dimitrova, Zoya Yamasaki, Lilian Skums, Pavel Lau, Daryl TY Vaughan, Gilberto Forbi, Joseph C Teo, Chong-Gee Khudyakov, Yury |
author_sort | Campo, David S |
collection | PubMed |
description | BACKGROUND: Next-generation sequencing (NGS) allows for sampling numerous viral variants from infected patients. This provides a novel opportunity to represent and study the mutational landscape of Hepatitis C Virus (HCV) within a single host. RESULTS: Intra-host variants of the HCV E1/E2 region were extensively sampled from 58 chronically infected patients. After NGS error correction, the average number of reads and variants obtained from each sample were 3202 and 464, respectively. The distance between each pair of variants was calculated and networks were created for each patient, where each node is a variant and two nodes are connected by a link if the nucleotide distance between them is 1. The work focused on large components having > 5% of all reads, which in average account for 93.7% of all reads found in a patient. The distance between any two variants calculated over the component correlated strongly with nucleotide distances (r = 0.9499; p = 0.0001), a better correlation than the one obtained with Neighbour-Joining trees (r = 0.7624; p = 0.0001). In each patient, components were well separated, with the average distance between (6.53%) being 10 times greater than within each component (0.68%). The ratio of nonsynonymous to synonymous changes was calculated and some patients (6.9%) showed a mixture of networks under strong negative and positive selection. All components were robust to in silico stochastic sampling; even after randomly removing 85% of all reads, the largest connected component in the new subsample still involved 82.4% of remaining nodes. In vitro sampling showed that 93.02% of components present in the original sample were also found in experimental replicas, with 81.6% of reads found in both. When syringe-sharing transmission events were simulated, 91.2% of all simulated transmission events seeded all components present in the source. CONCLUSIONS: Most intra-host variants are organized into distinct single-mutation components that are: well separated from each other, represent genetic distances between viral variants, robust to sampling, reproducible and likely seeded during transmission events. Facilitated by NGS, large components offer a novel evolutionary framework for genetic analysis of intra-host viral populations and understanding transmission, immune escape and drug resistance. |
format | Online Article Text |
id | pubmed-4120142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41201422014-08-11 Next-generation sequencing reveals large connected networks of intra-host HCV variants Campo, David S Dimitrova, Zoya Yamasaki, Lilian Skums, Pavel Lau, Daryl TY Vaughan, Gilberto Forbi, Joseph C Teo, Chong-Gee Khudyakov, Yury BMC Genomics Research BACKGROUND: Next-generation sequencing (NGS) allows for sampling numerous viral variants from infected patients. This provides a novel opportunity to represent and study the mutational landscape of Hepatitis C Virus (HCV) within a single host. RESULTS: Intra-host variants of the HCV E1/E2 region were extensively sampled from 58 chronically infected patients. After NGS error correction, the average number of reads and variants obtained from each sample were 3202 and 464, respectively. The distance between each pair of variants was calculated and networks were created for each patient, where each node is a variant and two nodes are connected by a link if the nucleotide distance between them is 1. The work focused on large components having > 5% of all reads, which in average account for 93.7% of all reads found in a patient. The distance between any two variants calculated over the component correlated strongly with nucleotide distances (r = 0.9499; p = 0.0001), a better correlation than the one obtained with Neighbour-Joining trees (r = 0.7624; p = 0.0001). In each patient, components were well separated, with the average distance between (6.53%) being 10 times greater than within each component (0.68%). The ratio of nonsynonymous to synonymous changes was calculated and some patients (6.9%) showed a mixture of networks under strong negative and positive selection. All components were robust to in silico stochastic sampling; even after randomly removing 85% of all reads, the largest connected component in the new subsample still involved 82.4% of remaining nodes. In vitro sampling showed that 93.02% of components present in the original sample were also found in experimental replicas, with 81.6% of reads found in both. When syringe-sharing transmission events were simulated, 91.2% of all simulated transmission events seeded all components present in the source. CONCLUSIONS: Most intra-host variants are organized into distinct single-mutation components that are: well separated from each other, represent genetic distances between viral variants, robust to sampling, reproducible and likely seeded during transmission events. Facilitated by NGS, large components offer a novel evolutionary framework for genetic analysis of intra-host viral populations and understanding transmission, immune escape and drug resistance. BioMed Central 2014-07-14 /pmc/articles/PMC4120142/ /pubmed/25081811 http://dx.doi.org/10.1186/1471-2164-15-S5-S4 Text en Copyright © 2014 Campo et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Campo, David S Dimitrova, Zoya Yamasaki, Lilian Skums, Pavel Lau, Daryl TY Vaughan, Gilberto Forbi, Joseph C Teo, Chong-Gee Khudyakov, Yury Next-generation sequencing reveals large connected networks of intra-host HCV variants |
title | Next-generation sequencing reveals large connected networks of intra-host HCV variants |
title_full | Next-generation sequencing reveals large connected networks of intra-host HCV variants |
title_fullStr | Next-generation sequencing reveals large connected networks of intra-host HCV variants |
title_full_unstemmed | Next-generation sequencing reveals large connected networks of intra-host HCV variants |
title_short | Next-generation sequencing reveals large connected networks of intra-host HCV variants |
title_sort | next-generation sequencing reveals large connected networks of intra-host hcv variants |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4120142/ https://www.ncbi.nlm.nih.gov/pubmed/25081811 http://dx.doi.org/10.1186/1471-2164-15-S5-S4 |
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