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.

Figure 1. The HCV ORF.

The approximately 9,600-nt positive-polarity HCV RNA genome encodes a long ORF. The ORF is translated into an approximately 3,010–amino acid polyprotein that is cleaved to 10 mature proteins. The known or predicted functions of the proteins are indicated.

Figure 2. The All covariance networks.

Each network is composed of 47 sequences per genotype, 1a (A and B) and 1b (C and D), totaling 94 sequences. (A and C) Networks formed by the covariances. The nodes represent covarying amino acid positions, and the edges represent covariances between the nodes. The most highly connected nodes are in yellow. (B and D) Edge distribution for the genotype networks.

Figure 3. The connectivity of the response-specific covariance networks is different.

Shown are 1a Marked (A and B) and 1a Poor (C and D) response classes. (A and C) Networks formed by the covariant pairs of residue positions. The most highly connected nodes are in yellow. (B and D) Positions that directly covary with position 463 (square node) are shown to highlight the differences between the networks.

Figure 4. Segregation of the edges and nodes by phenotype.

The overlap and segregation of the covarying nodes (A and B) and edges (C and D) by response class is shown for genotype 1a (A and C) and 1b (B and D).

Figure 5. Random shuffling of sequences causes loss of information in the response-specific networks.

We generated 10 independent alignments, in which 1a Marked sequences were randomly replaced with 1a Poor sequences at 3 levels of replacement: 2, 4, or 6 sequences, giving 12.5%, 25%, and 37.5% sequences shuffled, respectively. Shown are proportions of true positive covarying pairs relative to the unshuffled Marked sequences. P values showing significance of the differences in conserved edges relative to the unshuffled network were determined by Student’s t test.

Figure 6. Example subnetworks associated with outcome of anti-HCV therapy, with 100% accuracy and 0% false coverage rates, that are potential biomarkers for prediction of therapy outcome.

The subnetworks contain 1 or more covariances that together are found in the indicated fraction (coverage) of the appropriate response class and are never found in the opposing response class. Polyprotein residue numbers are shown for each subnetwork, and the identity of the mature HCV protein is indicated in subscript.