The evolution of the major hepatitis C genotypes correlates with clinical response to interferon therapy

Abstract

Background: Patients chronically infected with hepatitis C virus (HCV) require significantly different durations of therapy and achieve substantially different sustained virologic response rates to interferon-based therapies, depending on the HCV genotype with which they are infected. There currently exists no systematic framework that explains these genotype-specific response rates. Since humans are the only known natural hosts for HCV-a virus that is at least hundreds of years old-one possibility is that over the time frame of this relationship, HCV accumulated adaptive mutations that confer increasing resistance to the human immune system. Given that interferon therapy functions by triggering an immune response, we hypothesized that clinical response rates are a reflection of viral evolutionary adaptations to the immune system.

Methods and findings: We have performed the first phylogenetic analysis to include all available full-length HCV genomic sequences (n = 345). This resulted in a new cladogram of HCV. This tree establishes for the first time the relative evolutionary ages of the major HCV genotypes. The outcome data from prospective clinical trials that studied interferon and ribavirin therapy was then mapped onto this new tree. This mapping revealed a correlation between genotype-specific responses to therapy and respective genotype age. This correlation allows us to predict that genotypes 5 and 6, for which there currently are no published prospective trials, will likely have intermediate response rates, similar to genotype 3. Ancestral protein sequence reconstruction was also performed, which identified the HCV proteins E2 and NS5A as potential determinants of genotype-specific clinical outcome. Biochemical studies have independently identified these same two proteins as having genotype-specific abilities to inhibit the innate immune factor double-stranded RNA-dependent protein kinase (PKR).

Conclusion: An evolutionary analysis of all available HCV genomes supports the hypothesis that immune selection was a significant driving force in the divergence of the major HCV genotypes and that viral factors that acquired the ability to inhibit the immune response may play a role in determining genotype-specific response rates to interferon therapy.

Figure 1. Unrooted HCV Cladograms From Previous Studies.

Panel A shows the first cladogram to divide HCV into six genotypes, based on a neighbor-joining analysis of the NS5 region of HCV that included 76 sequences (Simmonds et al. 1993). Panel B shows a more recent HCV consensus tree with a different genotype branching pattern compared to Panel A, based on an analysis of 27 full-length HCV genomic sequences (Salemi et al. 2002). The table below each panel indicates the genotype distribution of the sequences analyzed in these studies.

Figure 2. Flow Chart of the Evolutionary Analyses Performed in This Study.

ML: maximum likelihood; MP: maximum parsimony; NJ: neighbor joining; MAFFT: multiple sequence alignment based on fast Fourier transform.

Figure 3. Rooted Neighbor-Joining (NJ), Maximum Parsimony (MP), and Maximum Likelihood (ML) cladograms depicting the evolution of the major hepatitis C virus genotypes.

A nexus file of complete tree data is available online (Dataset S1). Numbers represent bootstrap support and Bremer decay indices. AA: amino acid sequences; NT: nucleotide sequences.

Figure 4. Rooted Consensus Cladogram Resulting From An Analysis of 345 Full-Length HCV Genomic Sequences.

The evolution of the major (HCV) genotypes and their correlation to clinical outcome is depicted. Values denote bootstrap support and Bremer decay indices for the indicated phylogenetic inference method.

Figure 5. HCV Resistance Loci.

Positions (“hotspots”) in the HCV genome that appear to have undergone directed change with respect to immune resistance. N1: positions associated, using ancestral sequence reconstruction techniques, with genotype (GN) 1 being the most resistant. N2: positions associated, using ancestral sequence reconstruction techniques, with GNs 1 and 4 being more resistant than GNs 2, 3, 5, and 6. N3: positions associated, using ancestral sequence reconstruction techniques, with GNs 1, 3, 4, 5, and 6 being more resistant than GN 2. A hotspot histogram (binned in groups of 50 amino acids) is shown at the top. The 10 proteins encoded by the HCV genome are also illustrated. Two loci were identified with the highest density of hotspots (black bars); these loci map to the PePHD domain of E2 and the PKR binding domain of NS5A, both of which have been shown to inhibit the innate immune factor PKR.