- Research article
- Open Access
- Open Peer Review
Deep sequencing of hepatitis C virus hypervariable region 1 reveals no correlation between genetic heterogeneity and antiviral treatment outcome
© Carabello Cortés et al.; licensee BioMed Central Ltd. 2014
- Received: 10 March 2014
- Accepted: 7 July 2014
- Published: 13 July 2014
Hypervariable region 1 (HVR1) contained within envelope protein 2 (E2) gene is the most variable part of HCV genome and its translation product is a major target for the host immune response. Variability within HVR1 may facilitate evasion of the immune response and could affect treatment outcome. The aim of the study was to analyze the impact of HVR1 heterogeneity employing sensitive ultra-deep sequencing, on the outcome of PEG-IFN-α (pegylated interferon α) and ribavirin treatment.
HVR1 sequences were amplified from pretreatment serum samples of 25 patients infected with genotype 1b HCV (12 responders and 13 non-responders) and were subjected to pyrosequencing (GS Junior, 454/Roche). Reads were corrected for sequencing error using ShoRAH software, while population reconstruction was done using three different minimal variant frequency cut-offs of 1%, 2% and 5%. Statistical analysis was done using Mann–Whitney and Fisher’s exact tests.
Complexity, Shannon entropy, nucleotide diversity per site, genetic distance and the number of genetic substitutions were not significantly different between responders and non-responders, when analyzing viral populations at any of the three frequencies (≥1%, ≥2% and ≥5%). When clonal sample was used to determine pyrosequencing error, 4% of reads were found to be incorrect and the most abundant variant was present at a frequency of 1.48%. Use of ShoRAH reduced the sequencing error to 1%, with the most abundant erroneous variant present at frequency of 0.5%.
While deep sequencing revealed complex genetic heterogeneity of HVR1 in chronic hepatitis C patients, there was no correlation between treatment outcome and any of the analyzed quasispecies parameters.
- Hypervariable region 1
- Ultra-deep sequencing
- Genetic heterogeneity
- Hepatitis C virus
Hepatitis C virus (HCV) circulates within the infected host as a pool of related but distinct genetic variants (quasispecies); . The genetic variability is mainly generated by viral RNA-dependent RNA polymerase (RdRp) which lacks a proof-reading activity . Genes encoding envelope E1 and E2 proteins, especially the hypervariable 1 region (HVR1) of E2, display the highest genetic variability within the whole HCV genome . HVR1 contains sequences encoding important immune epitopes; thus genetic variability within this region may facilitate evasion of the immune responses and is largely shaped by the immune pressure of the host [4–8]. Complexity and evolution of HVR1 quasispecies was reported to be predictive factor of the outcome of natural infection [9, 10].
Antiviral treatment protocols using interferon and ribavirin have limited efficacy and are plagued by side effects, which often require premature discontinuation of therapy. Factors known to be associated with treatment outcome include both host (i.e. IL28B gene polymorphisms, race, sex, age) as well as viral factors (genotype, serum load and genetic heterogeneity); [11–13].
Interferon and ribavirin treatment is based largely on direct antiviral effect as well as immunomodulation . Thus, HVR1 heterogeneity could facilitate treatment failure since coexistence of multiple antigenic variants could increase the probability of positive selection of those effectively evading immune pressure induced by treatment [15, 16]. However, despite attempts to correlate HVR1 heterogeneity with antiviral treatment outcome, published studies are inconclusive [17–20].
Recent years brought the advent of ultra-deep sequencing techniques which enable parallel sequencing of multiple sequences present in a sample, thus providing better insight into the quasispecies phenomenon. Pyrosequencing (454/Roche), one of the available deep sequencing platforms, is capable of reading sequences up to 1 kb, and it was used successfully for sequence analysis of human immunodeficiency virus (HIV) and HCV [21–25].
Similarly, our previous analysis of HVR1 in chronic HCV infection confirmed the utility of pyrosequencing for HCV haplotypes inference, including identification of very rare variants constituting as little as 0.1% of the whole population .
The present study employed pyrosequencing to explore HVR1 complexity and variability in pretreatment serum samples of patients treated with pegylated interferon α (PEG-IFN α ) and ribavirin. We demonstrated that complexity, Shannon entropy, nucleotide diversity per site, genetic distance and the number of genetic substitutions were not significantly different between responders and non-responders, when analyzing populations present at ≥1%, ≥2% and ≥5% frequency.
Clinical and virological characteristics of 25 studied patients infected with genotype 1b
Treatment responders SVR+, n = 12
Treatment non-responders SVR-, n = 13
Complete early viral response (cEVR)
42.6 ± 17.9
50.1 ± 12.4
Alanine aminotransferase levels [U/l]*
95.9 ± 74.1
109.2 ± 50.1
Liver histology*, §
1.1 ± 0.4
1.1 ± 0.4
1.3 ± 1.3
1.7 ± 0.8
Pretreatment viral load (IU/ml)* , **
1.2 × 106 ± 1.2 × 106
1.5 × 106 ± 1.2× 106
HVR1 amplification was performed from pretreatment serum samples as described previously . In brief, viral RNA was extracted from 250 μl of serum by modified guanidinium thiocyanate-phenol/chlorophorm method, then subjected to reverse transcription at 37°C for 30 minutes using AccuScript High Fidelity Reverse Transcriptase (Agilent Technologies). A fragment of E2 region containing HVR1 was amplified in two-step PCR using FastStart High Fidelity Taq DNA Polymerase (Roche). Primers for the second round PCR contained tags recognized by GS Junior sequencing platform, standard 10-nucleotide multiplex identifiers (MID) and target-specific sequence.
Cloned HVR1 sequence
To determine the inherent sequencing error, amplified HVR1 from one sample was purified by Wizard SV Genomic DNA Purification System (Promega) and cloned into TOPO TA vector using TOPO TA Cloning Kit (Invitrogen). Plasmid DNA was extracted from bacterial culture using Quick Plasmid Miniprep Kit (Life technologies). Subsequently, pyrosequencing-specific tags with multiplex identifier (MID) were introduced by means of PCR using plasmid sequence as a target and sample was subjected to pyrosequencing.
Each amplicon was purified from agarose gel by QIAquick Gel Extraction kit (Qiagen) and then by Agencourt AMPure XP beads (Beckman Coulter) using 1.6:1 ratio of beads to sample. Products were quantified by dsDNA HS Qubit® Assay Kit (Life Technologies), fourteen samples were pooled in equivalent amounts and of 3 × 107 DNA copies were subjected to emulsion PCR using GS Junior Titanium emPCR Kit (Lib-A). After initial denaturation at 94°C for 1 minute, the reaction was run for 50 cycles of 94°C for 30 seconds, 58°C for 4 minutes and 30 seconds, and 68°C for 30 seconds. DNA library beads enrichment was carried out according to the emPCR Amplification Method Manual Lib-A (Roche), with the exception that the number of bead washes was 15. The required input of 500 000 enriched beads was loaded onto the Pico Titer Plate (PTP) and sequencing was carried out for 200 cycles using full processing mode for amplicons (GS Junior Sequencer, 454/Roche). In total, two independent pyrosequencing runs were performed (14 samples with specific MID were pooled in each).
N – number of observations (haplotypes),
f i - frequency of haplotypes
Differences in age, alanine aminotransferase activity, viral load, HVR1 complexity, diversity, number of substitutions within HVR1, Shannon entropy, genetic distance, number of polymorphic amino acid positions and number of inner nodes in phylogenetic trees were compared using Mann–Whitney test, while proportions were compared by Fisher’s exact test.
Estimation of pyrosequencing and amplification errors based on cloned HVR1 sequence
Deep sequencing of cloned HVR1 sample
Number of reads of cloned plasmid (control)
Number of variants
Most abundant erroneous variant
Least abundant erroneous variant
Overall error rate per base
Types of errors:
Overall insertions at homopolymeric regions
Number of variants after ShoRAH
Most abundant erroneous variant after ShoRAH
Least abundant erroneous variant after ShoRAH
Errors included insertions (83.3%), substitutions (12.5%) and deletions (4.2%). Probability of error occurrence per base was estimated to be 0.04% for insertion, 0.006% for substitution and 0.002% for deletion. Fifty one percent of insertions occurred at homopolymeric regions (four repeats of T). Altogether, the probability of any error per base was 0.05%.
After error correction performed with ShoRAH, four variants were identified: one identical to the template at 99.0% frequency, and three erroneous variants present at frequency of 0.5%, 0.3% and 0.2%, respectively.
Characteristics of deep sequencing
Characteristics of pyrosequencing of pretreatment serum samples from 25 HCV-positive patients receiving PEG-IFN α and ribavirin treatment
Number of sequenced reads aligned to reference genome
Number of sequenced nucleotides
15 100 000
Median of reads per patient (IQR)
Mean number of haplotypes per patient after ShoRAH
Most abundant haplotype
Least abundant haplotype
HVR1 genetic heterogeneity
HCV HVR1 genetic characteristics in responders and non-responders to PEG-IFN α and ribavirin treatment
Number of patients
HVR1 complexity (number of haplotypes)
Mean Shannon entropy
Mean nucleotide diversity per nucleotide
Mean genetic distance
Number of nucleotide substitutions within HVR1
Percentage of polymorphic amino acid positions
Amino acid variability of HVR1
Within 27 amino acid stretch of HVR1, responders were found to have similar mean number of polymorphic amino acid positions (59.3% ± 9.5%) as non-responders (60% ± 11%); (Table 4). Additional file 1 shows multiple sequence alignment of amino acid sequences of HVR1 populations in responders (R) and non-responders to treatment (NR).
A number of previous studies attempted to correlate HVR1 heterogeneity with antiviral treatment outcome, but their results were usually inconclusive and occasionally even contradictory. These discrepancies could be partly due to the use of different techniques: two most commonly used were single strand conformational polymorphism (SSCP) and clonal Sanger sequencing [17–20, 31–34]. The latter requires extensive cloning to achieve high sensitivity for minor variants detection, a process that is costly and time-consuming. Thus, studies using this technique rarely included significant number of clones per sample, typically attaining only 15-20% sensitivity. While SSCP has been shown to detect variants constituting as little as 3% of the viral population , it is not informative of the nucleotide sequence, the nature of genetic changes or genetic distances between variants. Furthermore, in a mixture of heterogeneous sequences, certain bands may overlap, resulting in underestimation of viral complexity. Our current study, which was based on deep sequencing, overcomes the above shortcomings and represents a novel approach to analysis of HCV heterogeneity.
While our analysis did not find any significant differences in HVR1 heterogeneity between responders and non-responders to antiviral treatment, these results are largely compatible with some previous studies employing SSCP and clonal sequencing. In the study of Pawlotsky et al.  based on single strand conformational polymorphism and in the study of Saludes et al.  based on clonal sequencing, no significant differences in pretreatment HVR1 complexity were observed between responders and non-responders. Similar results were reported in a study of re-treated patients with advanced fibrosis , while Abbate et al.  found that low pretreatment HVR1 heterogeneity correlated with early response (EVR), but not with SVR. A number of other studies found no correlation between HVR1 complexity and treatment outcome [17, 18, 36].
In our study, such HVR1 heterogeneity parameters, as nucleotide diversity per site, genetic distance, and number of nucleotide substitutions also did not differ significantly between responders and non-responders. These findings are similar to several earlier studies [20, 34, 37, 38]. In the only published study using deep sequencing approach, there were no differences in pretreatment complexity parameters (e.g. Shannon entropy) between immediate virological responders and non-responders. However, the final treatment outcome was not reported .
Lack of statistically significant differences in analyzed heterogeneity parameters between responders and non-responders suggest that the heterogeneity generated by minor variants detectable by deep sequencing has no effect on treatment outcome. Alternatively, it may be speculated that the analyzed depth of frequency is still insufficient to detect minor variants whose heterogeneity would have clinical significance.
Some recent studies brought attention to the problem of inherent ultra-deep sequencing errors affecting the detection of minor variants of the quasispecies population [26, 39, 40]. In our analysis, the internal control experiment using cloned HVR1 revealed the overall sequencing error to be 0.05% per nucleotide, comprising mostly of insertions and occurring predominantly in homopolymeric regions. This error rate contributed to the high proportion of erroneous sequences (4% of total reads, the most abundant erroneous variant being present at a frequency of 1.48%). To minimize the risk of including erroneous variants into analysis, we implemented ShoRAH error correction method, which allowed for correction of 99% of reads reducing both the absolute number and frequency of erroneous variants. Thus, error correction methods should be used to facilitate analysis of minor quasispecies by pyrosequencing.
There were no significant differences in the pretreatment HVR1 heterogeneity parameters such as complexity, Shannon entropy, nucleotide diversity per site, genetic distance and the number of genetic substitutions between responders and non-responders. Thus, pretreatment HVR1 quasispecies composition and heterogeneity analysis seems to have limited value for the prediction of treatment outcome.
This work was supported by grants NN401 6467 40 and 1M24/PM12/12 from The Polish National Science Centre.
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