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. 2015 Oct 13:4:1062.
doi: 10.12688/f1000research.7111.1. eCollection 2015.

ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens

Affiliations

ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens

David Bonsall et al. F1000Res. .

Abstract

The routine availability of high-depth virus sequence data would allow the sensitive detection of resistance-associated variants that can jeopardize HIV or hepatitis C virus (HCV) treatment. We introduce ve-SEQ, a high-throughput method for sequence-specific enrichment and characterization of whole-virus genomes at up to 20% divergence from a reference sequence and 1,000-fold greater sensitivity than direct sequencing. The extreme genetic diversity of HCV led us to implement an algorithm for the efficient design of panels of oligonucleotide probes to capture any sequence among a defined set of targets without detectable bias. ve-SEQ enables efficient detection and sequencing of any HCV genome, including mixtures and intra-host variants, in a single experiment, with greater tolerance of sequence diversity than standard amplification methods and greater sensitivity than metagenomic sequencing, features that are directly applicable to other pathogens or arbitrary groups of target organisms, allowing the combination of sensitive detection with sequencing in many settings.

Keywords: Anti-viral resistance; Hepatitis C virus; Sequence capture and enrichment; Virus genome sequencing.

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Conflict of interest statement

Competing interests: No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. HCV metagenomic sequence yield is proportional to viral load.
The yield of reads that map to any HCV genome and the probability of successful de novo assembly of a complete genome sequence both depend on viral load (VL). Samples were prepared as replicate libraries that were sequenced simultaneously with consistent yield. Blue circles: successful de novo assembly (>90% complete genome length recovered); red circles: incomplete genome assembly. a. With standard mapping criteria, up to 2.8% of reads match HCV and a background 0.02–0.1% of low-complexity human-derived sequences overwhelms the HCV signal in low-VL samples. Linear trend is plotted for samples with VL > 10 5 IU/ml. b. Under stringent mapping criteria (mapping Q > 40), lower complexity human and HCV reads are excluded and yield is proportional to VL (slope of linear trend in log-log space not significantly different from 1) across the VL range.
Figure 2.
Figure 2.. Enrichment efficiency decreases with phylogenetic distance.
Read depth across the genome before (blue, left axis) and after (red, right axis) enrichment with a single-sequence subtype 1a probe set. a. The HCV genome comprises 5’ and 3’ untranslated regions (UTRs) and a large central segment encoding a single polyprotein that is cleaved into ten proteins. b. A subtype 1a sample enriched with probes derived from its own consensus sequence yields coverage patterns across the genome essentially identical to metagenomic sequencing. c. A distinct subtype 1a sample produces highly similar but non-identical patterns of pre- and post-enrichment genomic coverage. d. A subtype 1b sample yields low read depths at loci that are relatively divergent from the 1a probe sequence (E1, E2, NS2 and NS5a). e. Sequence capture of a sample from a different genotype, 3a, is poor across large segments of the genome. f. Heat map representing average diversity (calculated as Shannon entropy) among 165 HCV reference genomes. Nucleotide diversity varies dramatically across the genome and tracks drops in enrichment efficiency between phylogenetically distinct probe-target combinations.
Figure 3.
Figure 3.. Enrichment efficiency is directly related to probe-target identity.
A set of 10 HCV samples with highest VL was sequenced before and after enrichment with a single-genome, subtype 1a probe set, and for each sample the relative read depth for each probe window was plotted against the maximum identity between target and any probe. Read depth ratio was normalized by giving the most efficiently enriched probe position (in the highly conserved 5’ UTR) a value of 1. Maximal enrichment is observed where probe-target identity exceeds approximately 80% and enrichment decreases dramatically as identity falls below 80%.
Figure 4.
Figure 4.. Detection of resistance-associated variants after DAA treatment failure.
VL and RAV status for three patients who failed to achieve sustained virological response after Telaprevir-based therapy. Grey shading: duration of therapy (weeks starting at time 0); squares: VL measurements; inverted triangles: samples sequenced using the comprehensive probe panel (open: no Telaprevir RAVs detected, black: RAVs and supporting read proportions, where <100%).

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