An HIV Epidemic Model Based on Viral Load Dynamics: Value in Assessing Empirical Trends in HIV Virulence and Community Viral Load

<div><p>Trends in HIV virulence have been monitored since the start of the AIDS pandemic, as studying HIV virulence informs our understanding of HIV epidemiology and pathogenesis. Here, we model changes in HIV virulence as a strictly evolutionary process, using <i>set point viral load</i> (SPVL) as a proxy, to make inferences about empirical SPVL trends from longitudinal HIV cohorts. We develop an agent-based epidemic model based on HIV viral load dynamics. The model contains functions for viral load and transmission, SPVL and disease progression, viral load trajectories in multiple stages of infection, and the heritability of SPVL across transmissions. We find that HIV virulence evolves to an intermediate level that balances infectiousness with longer infected lifespans, resulting in an optimal SPVL∼4.75 log₁₀ viral RNA copies/mL. Adaptive viral evolution may explain observed HIV virulence trends: our model produces SPVL trends with magnitudes that are broadly similar to empirical trends. With regard to variation among studies in empirical SPVL trends, results from our model suggest that variation may be explained by the specific epidemic context, <i>e.g.</i> the mean SPVL of the founding lineage or the age of the epidemic; or improvements in HIV screening and diagnosis that results in sampling biases. We also use our model to examine trends in community viral load, a population-level measure of HIV viral load that is thought to reflect a population's overall transmission potential. We find that community viral load evolves in association with SPVL, in the absence of prevention programs such as antiretroviral therapy, and that the mean community viral load is not necessarily a strong predictor of HIV incidence.</p></div

Mean community viral load is not linearly or consistently associated with annual incidence.

<p><b>A.</b> Plot of yearly estimates of mean community viral load versus annual incidence, for 100 years of a simulated epidemic. <b>B.</b> Distributions of <i>P</i>-values for Spearman correlations between mean community viral load and incidence, by year, for 10-year periods from the same 100 year epidemic, from a sliding window of 10 with one year increments. Shown are a plot of CVL and incidence for a 100-year simulated epidemic, Spearman correlation coefficients between CVL and incidence for each overlapping 10-year period, and <i>P</i>-value for each Spearman correlation test. Significant associations between CVL and incidence can be positive or negative, depending on epidemic context.</p

A. Distributions of SPVL trends change as simulated epidemics progress.

<p>More extreme SPVL trends occur very early in simulated epidemics (the first 20 years). Boxplots of linear SPVL trends estimated from 100 randomly sampled 20-year time periods; thick line = median; box edges = quartiles; whiskers = minimum and maximum trends. <b>B. </b><b>Trends in mean community viral load and mean set point viral load are related.</b> Mean community viral load can evolve over time in the absence of HIV prevention programs. Community viral loads are estimated for each day using viral load measurements from each infected and alive individual, except for those individuals who have been infected less than 45 days (acute infection lasts for 3 months days in these simulations).</p

Parameters of the model and standard initial values.

<p>Parameters of the model and standard initial values.</p

A. Empirical SPVL trends overlaid onto distributions of simulated 20-year trends.

<p>Distributions of linear SPVL trends (log₁₀ HIV RNA copies/mL/year) were estimated from 100 randomly sampled 20-year time periods for 10 replicate simulations for each initial mean SPVL = 3.5, 4.5 or 5.5 log₁₀ HIV RNA copies/mL (creating 1000 total 20-year trends for each initial mean SPVL). Empirical (published) annual linear SPVL trends are overlaid (arrows and references). References with asterisks are seroprevalent cohorts; all others are seroconverter cohorts. <b>B. </b><b>Selection of an appropriate null changes the distribution of simulated SPVL trends.</b> Separate null distributions, each spanning a different subset of the complete 100-year simulated epidemics: all 100 years of the model output; years 10–100 of the model output, as European and North American subtype B epidemics began ∼1970, and studies of empirical SPVL trends began sampling at the earliest in 1984, leaving a ∼10-year window of the HIV epidemic not sampled by the cohorts; years 0–40 of the model output, as the empirical studies of SPVL trends include years up to ∼2010, so this represents the first 40 years of the subtype B epidemic (∼1970 to 2010); and years 10–40, reflecting the empirical sampling years ∼1980 to 2010.</p

The effect of sampling biases on the estimation of model-based SPVL trends.

<p><b>A.</b> Comparison of distribution of 20-year linear SPVL trends estimated from unbiased (black lines, initial mean SPVL = 3.5; grey lines, initial mean SPVL = 4.5) and biased (dotted lines, multiple colors representing multiple sub-sampling levels) data sets. The underlying distributions are produced from years 0 to 100 from simulated epidemics. Removing subsets (%) of all individuals (a schematic representation of the biased sampling process is shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003673#pcbi.1003673.s001" target="_blank">Figure S1A</a>) results in a distribution of linear trends with a median SPVL trend of greater magnitude than the unbiased trends. <b>B.</b> Comparison of distribution of 20-year linear SPVL trends estimated from unbiased and biased data sets, but with the underlying distributions produced from years 10 to 40 from simulated epidemics.</p

Variation across replicate simulated epidemics.

<p><b>A.</b> Epidemic size over time. Epidemic runs with each initial SPVL were repeated 10 times, each run with a different random number seed. <b>B.</b> Population mean set point viral load (SPVL; log₁₀ HIV RNA copies/mL at the end of acute infection) over time, using a locally weighted polynomial regression curve (Lowess fit = 0.1). Mean SPVL evolves toward 4.75 log₁₀ copies/mL.</p

Fitness Costs of Mutations at the HIV-1 Capsid Hexamerization Interface

<div><p>The recently available x-ray crystal structure of HIV-1 capsid hexamers has provided insight into the molecular interactions crucial for the virus’s mature capsid formation. Amino acid changes at these interaction points are likely to have a strong impact on capsid functionality and, hence, viral infectivity and replication fitness. To test this hypothesis, we introduced the most frequently observed single amino acid substitution at 30 sites: 12 at the capsid hexamerization interface and 18 at non-interface sites. Mutations at the interface sites were more likely to be lethal (Fisher’s exact test p = 0.027) and had greater negative impact on viral replication fitness (Wilcoxon rank sum test p = 0.040). Among the interface mutations studied, those located in the cluster of hydrophobic contacts at NTD-NTD interface and those that disrupted NTD-CTD inter-domain helix capping hydrogen bonds were the most detrimental, indicating that these interactions are particularly important for maintaining capsid structure and/or function. These functionally constrained sites provide potential targets for novel HIV drug development and vaccine immunogen design.</p></div

Relationship between sequence conservation and replication fitness.

<p>Relative fitness of all mutants evaluated as a function of database frequency of the amino acid found in the prototype COTM-CA sequence. Values shown are an average from two experiments, done in triplicate. The replication fitness of non-viable viruse is plotted as zero.</p

Quality Score Based Identification and Correction of Pyrosequencing Errors

<div><p>Massively-parallel DNA sequencing using the 454/pyrosequencing platform allows in-depth probing of diverse sequence populations, such as within an HIV-1 infected individual. Analysis of this sequence data, however, remains challenging due to the shorter read lengths relative to that obtained by Sanger sequencing as well as errors introduced during DNA template amplification and during pyrosequencing. The ability to distinguish real variation from pyrosequencing errors with high sensitivity and specificity is crucial to interpreting sequence data. We introduce a new algorithm, CorQ (<u>Cor</u>rection through <u>Q</u>uality), which utilizes the inherent base quality in a sequence-specific context to correct for homopolymer and non-homopolymer insertion and deletion (indel) errors. CorQ also takes uneven read mapping into account for correcting pyrosequencing miscall errors and it identifies and corrects carry forward errors. We tested the ability of CorQ to correctly call SNPs on a set of pyrosequences derived from ten viral genomes from an HIV-1 infected individual, as well as on six simulated pyrosequencing datasets generated using non-zero error rates to emulate errors introduced by PCR. When combined with the AmpliconNoise error correction method developed to remove ambiguities in signal intensities, we attained a 97% reduction in indel errors, a 98% reduction in carry forward errors, and >97% specificity of SNP detection. When compared to four other error correction methods, AmpliconNoise+CorQ performed at equal or higher SNP identification specificity, but the sensitivity of SNP detection was consistently higher (>98%) than other methods tested. This combined procedure will therefore permit examination of complex genetic populations with improved accuracy.</p></div