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Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans.

ABSTRACT: Hepatitis C virus (HCV) is a common RNA virus that causes hepatitis and liver cancer. Infection is treated with IFN-alpha and ribavirin, but this expensive and physically demanding therapy fails in half of patients. The genomic sequences of independent HCV isolates differ by approximately 10%, but the effects of this variation on the response to therapy are unknown. To address this question, we analyzed amino acid covariance within the full viral coding region of pretherapy HCV sequences from 94 participants in the Viral Resistance to Antiviral Therapy of Chronic Hepatitis C (Virahep-C) clinical study. Covarying positions were common and linked together into networks that differed by response to therapy. There were 3-fold more hydrophobic amino acid pairs in HCV from nonresponding patients, and these hydrophobic interactions were predicted to contribute to failure of therapy by stabilizing viral protein complexes. Using our analysis to detect patterns within the networks, we could predict the outcome of therapy with greater than 95% coverage and 100% accuracy, raising the possibility of a prognostic test to reduce therapeutic failures. Furthermore, the hub positions in the networks are attractive antiviral targets because of their genetic linkage with many other positions that we predict would suppress evolution of resistant variants. Finally, covariance network analysis could be applicable to any virus with sufficient genetic variation, including most human RNA viruses.

SUBMITTER: Aurora R

PROVIDER: S-EPMC2613460 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans.

Aurora Rajeev R Donlin Maureen J MJ Cannon Nathan A NA Tavis John E JE

The Journal of clinical investigation 20081222 1

Hepatitis C virus (HCV) is a common RNA virus that causes hepatitis and liver cancer. Infection is treated with IFN-alpha and ribavirin, but this expensive and physically demanding therapy fails in half of patients. The genomic sequences of independent HCV isolates differ by approximately 10%, but the effects of this variation on the response to therapy are unknown. To address this question, we analyzed amino acid covariance within the full viral coding region of pretherapy HCV sequences from 94 ...[more]

PMID: 19104147

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Project description:BackgroundViral relapse is a major concern in hepatitis C virus (HCV) antiviral therapy. Currently, there are no satisfactory methods to predict viral relapse, especially in the era of direct acting antivirals in which the virus often quickly becomes undetectable using PCR-based approaches that focus on a small viral region. Next-generation sequencing (NGS) provides an alternative option for viral detection in a genome-wide manner. However, owing to the overwhelming dominance of human genetic content in clinical specimens, direct detection of HCV by NGS has a low sensitivity and hence viral enrichment is required.MethodsBased on template-dependent multiple displacement amplification (tdMDA), an improved method for whole genome amplification (Wang et al., 2017. Biotechniques 63, 21-27), we evaluated two strategies to enhance the sensitivity of NGS-based HCV detection: duplex-specific nuclease (DSN)-mediated depletion of human sequences and HCV probe-based capture sequencing.ResultsIn DSN-mediated depletion, human sequences were significantly reduced in the two HCV serum samples tested, 65.3% → 55.6% → 33.7% (#4727) and 68.6% → 56% → 21% (#4970), respectively for no normalization, self- and driver-applied normalization. However, this approach was associated with a loss of HCV sequences perhaps due to its micro-homology with the human genome. In capture sequencing, HCV-mapped sequencing reads occupied 96.8% (#4727) and 22.14% (#4970) in NGS data, equivalent to 1936x and 7380x enrichment, respectively. Capture sequencing was then applied to ten serum samples collected at the end of HCV antiviral therapy. Interestingly, the number of HCV-mapped reads was significantly higher in relapsed patients (n = 5) than those from patients with sustained virological response (SVR) (n = 5), 102.4 ± 72.3 vs. 2.6 ± 0.55, p = 0.014.ConclusionsOur data provides concept evidence for a highly sensitive HCV detection by capture sequencing. The abundance difference of HCV sequencing reads at the end of HCV antiviral therapy could be applied to predict treatment outcomes.

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Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans.

Publications

Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans.

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