Adaptation to human populations is revealed by within-host polymorphisms in HIV-1 and hepatitis C virus

Abstract

CD8(+) cytotoxic T-lymphocytes (CTLs) perform a critical role in the immune control of viral infections, including those caused by human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV). As a result, genetic variation at CTL epitopes is strongly influenced by host-specific selection for either escape from the immune response, or reversion due to the replicative costs of escape mutations in the absence of CTL recognition. Under strong CTL-mediated selection, codon positions within epitopes may immediately "toggle" in response to each host, such that genetic variation in the circulating virus population is shaped by rapid adaptation to immune variation in the host population. However, this hypothesis neglects the substantial genetic variation that accumulates in virus populations within hosts. Here, we evaluate this quantity for a large number of HIV-1- (n > or = 3,000) and HCV-infected patients (n > or = 2,600) by screening bulk RT-PCR sequences for sequencing "mixtures" (i.e., ambiguous nucleotides), which act as site-specific markers of genetic variation within each host. We find that nonsynonymous mixtures are abundant and significantly associated with codon positions under host-specific CTL selection, which should deplete within-host variation by driving the fixation of the favored variant. Using a simple model, we demonstrate that this apparently contradictory outcome can be explained by the transmission of unfavorable variants to new hosts before they are removed by selection, which occurs more frequently when selection and transmission occur on similar time scales. Consequently, the circulating virus population is shaped by the transmission rate and the disparity in selection intensities for escape or reversion as much as it is shaped by the immune diversity of the host population, with potentially serious implications for vaccine design.

Figure 1. Genetic Variation within Hosts Is Shaped by Host-Specific Selection for CTL Escape

(A) The difference in nonsynonymous and synonymous mixture frequencies within hosts (mN − mS) is positively correlated with diversifying selection among hosts (dN − dS) per codon position. Each point corresponds to a unique codon position in the respective gene sequence. Dashed lines indicate the mean value for each quantity, which is consistently negative in dN − dS, implying purifying selection overall. Solid lines indicate a linear fit to the data. HCV genotypes are plotted separately as shown in the figure legends. A single outlier caused by a rare substitution lies outside the plot region for HIV-1 RT, but does not influence the significance of this correlation (Pearson's ρ = 0.619, p-value < 3 × 10⁻¹⁶). (B) Selection for CTL escape elevates the frequency of nonsynonymous mixtures (solid circles) relative to synonymous mixtures (open triangles) at anchor residues within known A2-supertype–restricted epitopes in HIV-1 PR and RT and HCV E1 (predicted). Asterisks indicate anchor residues associated with disproportionately high frequencies of nonsynonymous mixtures.

Figure 2. Effect of Transmission Rate on the Frequency of Mixtures

This schematic depicts the transmission chain of a virus population, where each host is represented by an enclosed graph that represents the evolving frequency of a CTL escape variant over time. The hosts either possess an HLA allele which favors the escape variant (HLA⁺, orange-shaded boxes) or the wild-type virus (HLA⁻, white-shaded boxes). A severe transmission bottleneck causes the population in the next host to be initially fixed for either the wild-type or escape variant (filled circle). If selection for escape or reversion is sufficiently strong (upper schematic in blue), then the favored virus genotype will tend to become fixed within the host before transmission occurs (open circle). Under such conditions, transient polymorphisms will only occur whenever the virus is transmitted between hosts of opposite type. On the other hand, if transmission and selection occur on similar time scales (lower schematic in red), then the host type does not necessarily predict which virus genotype becomes transmitted, causing transient polymorphisms to become more abundant (starred boxes).

Figure 3. Factors Influencing Within-Host Polymorphisms and the Global Frequency of Escape Variants

(A) A contour plot depicting the mean effect of selection and transmission rate on displacing the frequency of detectable polymorphisms from the neutral expectation (Δf_poly, refer to the color key), as estimated from simulations. (The expectation E[f_poly] is jointly determined by the forward and back mutation rates, μ and ν, and population size, N.) The x-axis corresponds to the log-transformed transmission rate, log₁₀ k. The y-axis represents the mean log-transformed selection coefficient, E(log₁₀ s) = qlog₁₀(s_esc) + (1 − q)log₁₀(s_rev). (B) A 10-fold disparity in selection intensities s_esc = 0.02, s_rev = 0.002) causes π˄ to substantially exceed q with increasing transmission rate, k. Each set of points represents mean estimates of π˄ from simulations (with virus population size N = 5,000 and μ = ν = 10⁻⁴). Dashed lines indicate predicted values from the deterministic model, which performs poorly when k is too high (i.e., when transmissions occur rapidly, allele frequencies are almost always near zero or one where stochastic variation is greatest [31]). The typical range of q is indicated by the shaded plot region.