Gabriel E. Leventhal

Virulence and Pathogenesis of HIV-1 Infection: An Evolutionary Perspective

Background Why some individuals develop AIDS rapidly whereas others remain healthy without treatment for many years remains a central question of HIV research. Of the quantities that predict how quickly an untreated infection progresses, the most widely used is set-point viral load. This measure varies by orders of magnitude between infected individuals and is predictive of infectiousness and time to onset of AIDS. Host factors, predominantly linked to the immune system, are known to influence the set point, but much variation remains unexplained. A transmission chain with heritable virulence. Individuals infected with HIV show differences in clinical progression. Untreated infections are characterized by viral loads (the viral particle density in the blood) that are relatively stable for years, but they can differ by orders of magnitude between individuals. Host factors clearly influence viral load, but viral loads have also recently been found to correlate among individuals in transmission pairs and chains. This indicates a moderate to strong influence of viral genotype on the viral load. Strikingly, this influence persists for years and across transmission events, despite intense within-host viral evolution. A transmission chain with heritable virulence. Individuals infected with HIV show differences in clinical progression. Untreated infections are characterized by viral loads (the viral particle density in the blood) that are relatively stable for years, but they can differ by orders of magnitude between individuals. Host factors clearly influence viral load, but viral loads have also recently been found to correlate among individuals in transmission pairs and chains. This indicates a moderate to strong influence of viral genotype on the viral load. Strikingly, this influence persists for years and across transmission events, despite intense within-host viral evolution. Advances We review recent evidence showing that HIV genotype influences the set-point viral load far more than anticipated. Our summary of published estimates suggests that 33% (95% confidence interval, 20 to 46%) of the variation is attributable to the virus. Because set-point viral load is heritable (partially controlled by virus genotype) and is linked to transmissibility, it is likely to have evolved to maintain transmission fitness and may continue to evolve in response to diverse selection pressures. These findings are unexpected and paradoxical because rapid and error-prone viral replication should favor within-host adaptation and rapidly scramble signals of viral genotype as infection progresses, rather than leaving a lasting footprint that is preserved throughout an infection and from one infection to the next in transmission chains. Outlook We propose that resolving the paradox of heritability of set-point viral load will provide new insights into the mechanisms of HIV pathogenesis. To this end, we provide three parsimonious, testable, and nonexclusive explanatory mechanisms. The first states that HIV evolution in virulence genes is more functionally constrained than previously thought. The second proposes that virulence of HIV is mediated through the virus’s capacity to systemically activate target cells in which it can efficiently replicate. The capacity to activate would not be expected to evolve rapidly because it does not provide a specific selective advantage to virus strains that activate more cells; rather, it is an advantage shared by all viruses. The third mechanism implicates the preferential transmission of viruses that are stored in nonreplicating cells or during early infection, and the disproportionate influence on long-term pathogenesis of these early viruses. In addition to these insights into mechanisms of pathogenesis, we believe that this research highlights a major gap in our knowledge of HIV. The identification of the genetic determinants of HIV virulence, which appear to vary between closely related strains of the virus, should be a major priority. Thus, whole-genome association studies that are focused on the virus genome should be pursued and expanded, as well as more functional and mechanistic studies, which could be guided by hypotheses such as those presented here. HIV Virulence A major focus of research on HIV is on host responses to infection—understandably, because the virus targets the immune system and because of the interest in vaccine development. In reviewing what little research has been done on viral virulence determinants, Fraser et al. (10.1126/science.1243727) present evolutionary explanations for some of the poorly understood phenomena that mark HIV infection, including long-term survivorship, latency, rapid within-host evolution, and inheritability of between-host virulence. Why some individuals develop AIDS rapidly whereas others remain healthy without treatment for many years remains a central question of HIV research. An evolutionary perspective reveals an apparent conflict between two levels of selection on the virus. On the one hand, there is rapid evolution of the virus in the host, and on the other, new observations indicate the existence of virus factors that affect the virulence of infection whose influence persists over years in infected individuals and across transmission events. Here, we review recent evidence that shows that viral genetic factors play a larger role in modulating disease severity than anticipated. We propose conceptual models that reconcile adaptive evolution at both levels of selection. Evolutionary analysis provides new insight into HIV pathogenesis.

Exploring the complexity of the HIV-1 fitness landscape.

Although fitness landscapes are central to evolutionary theory, so far no biologically realistic examples for large-scale fitness landscapes have been described. Most currently available biological examples are restricted to very few loci or alleles and therefore do not capture the high dimensionality characteristic of real fitness landscapes. Here we analyze large-scale fitness landscapes that are based on predictive models for in vitro replicative fitness of HIV-1. We find that these landscapes are characterized by large correlation lengths, considerable neutrality, and high ruggedness and that these properties depend only weakly on whether fitness is measured in the absence or presence of different antiretrovirals. Accordingly, adaptive processes on these landscapes depend sensitively on the initial conditions. While the relative extent to which mutations affect fitness on their own (main effects) or in combination with other mutations (epistasis) is a strong determinant of these properties, the fitness landscape of HIV-1 is considerably less rugged, less neutral, and more correlated than expected from the distribution of main effects and epistatic interactions alone. Overall this study confirms theoretical conjectures about the complexity of biological fitness landscapes and the importance of the high dimensionality of the genetic space in which adaptation takes place.

Inferring Epidemic Contact Structure from Phylogenetic Trees

Contact structure is believed to have a large impact on epidemic spreading and consequently using networks to model such contact structure continues to gain interest in epidemiology. However, detailed knowledge of the exact contact structure underlying real epidemics is limited. Here we address the question whether the structure of the contact network leaves a detectable genetic fingerprint in the pathogen population. To this end we compare phylogenies generated by disease outbreaks in simulated populations with different types of contact networks. We find that the shape of these phylogenies strongly depends on contact structure. In particular, measures of tree imbalance allow us to quantify to what extent the contact structure underlying an epidemic deviates from a null model contact network and illustrate this in the case of random mixing. Using a phylogeny from the Swiss HIV epidemic, we show that this epidemic has a significantly more unbalanced tree than would be expected from random mixing.

Evolution and emergence of infectious diseases in theoretical and real-world networks.

One of the most important advancements in theoretical epidemiology has been the development of methods that account for realistic host population structure. The central finding is that heterogeneity in contact networks, such as the presence of ‘superspreaders’, accelerates infectious disease spread in real epidemics. Disease control is also complicated by the continuous evolution of pathogens in response to changing environments and medical interventions. It remains unclear, however, how population structure influences these adaptive processes. Here we examine the evolution of infectious disease in empirical and theoretical networks. We show that the heterogeneity in contact structure, which facilitates the spread of a single disease, surprisingly renders a resident strain more resilient to invasion by new variants. Our results suggest that many host contact structures suppress invasion of new strains and may slow disease adaptation. These findings are important to the natural history of disease evolution and the spread of drug-resistant strains.

Using an Epidemiological Model for Phylogenetic Inference Reveals Density Dependence in HIV Transmission

The control, prediction, and understanding of epidemiological processes require insight into how infectious pathogens transmit in a population. The chain of transmission can in principle be reconstructed with phylogenetic methods which analyze the evolutionary history using pathogen sequence data. The quality of the reconstruction, however, crucially depends on the underlying epidemiological model used in phylogenetic inference. Until now, only simple epidemiological models have been used, which make limiting assumptions such as constant rate parameters, infinite total population size, or deterministically changing population size of infected individuals. Here, we present a novel phylogenetic method to infer parameters based on a classical stochastic epidemiological model. Specifically, we use the susceptible-infected-susceptible model, which accounts for density-dependent transmission rates and finite total population size, leading to a stochastically changing infected population size. We first validate our method by estimating epidemic parameters for simulated data and then apply it to transmission clusters from the Swiss HIV epidemic. Our estimates of the basic reproductive number R0 for the considered Swiss HIV transmission clusters are significantly higher than previous estimates, which were derived assuming infinite population size. This difference in key parameter estimates highlights the importance of careful model choice when doing phylogenetic inference. In summary, this article presents the first fully stochastic implementation of a classical epidemiological model for phylogenetic inference and thereby addresses a key aspect in ongoing efforts to merge phylogenetics and epidemiology.

Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison

Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation > 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods’ development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention.

High Heritability Is Compatible with the Broad Distribution of Set Point Viral Load in HIV Carriers

Set point viral load in HIV patients ranges over several orders of magnitude and is a key determinant of disease progression in HIV. A number of recent studies have reported high heritability of set point viral load implying that viral genetic factors contribute substantially to the overall variation in viral load. The high heritability is surprising given the diversity of host factors associated with controlling viral infection. Here we develop an analytical model that describes the temporal changes of the distribution of set point viral load as a function of heritability. This model shows that high heritability is the most parsimonious explanation for the observed variance of set point viral load. Our results thus not only reinforce the credibility of previous estimates of heritability but also shed new light onto mechanisms of viral pathogenesis.

How well can the exponential-growth coalescent approximate constant-rate birth-death population dynamics?

One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth–death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R0 close to 1, but substantial differences remain for large R0. In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.

Potential Pitfalls in Estimating Viral Load Heritability

In HIV patients, the set-point viral load (SPVL) is the most widely used predictor of disease severity. Yet SPVL varies over several orders of magnitude between patients. The heritability of SPVL quantifies how much of the variation in SPVL is due to transmissible viral genetics. There is currently no clear consensus on the value of SPVL heritability, as multiple studies have reported apparently discrepant estimates. Here we illustrate that the discrepancies in estimates are most likely due to differences in the estimation methods, rather than the study populations. Importantly, phylogenetic estimates run the risk of being strongly confounded by unrealistic model assumptions. Care must be taken when interpreting and comparing the different estimates to each other.

Virus-induced target cell activation reconciles set-point viral load heritability and within-host evolution

The asymptomatic phase of HIV-1 infections is characterised by a stable set-point viral load (SPVL) within patients. The SPVL is a strong predictor of disease progression and shows considerable variation of multiple orders of magnitude between patients. Recent studies have found that the SPVL in donor and recipient pairs is strongly correlated indicating that the virus genotype strongly influences viral load. Viral genetic factors that increase both viral load and the replicative capacity of the virus would result in rapid within-host evolution to higher viral loads. Reconciling a stable SPVL over time with high SPVL heritability requires viral genetic factors that strongly influence SPVL but only weakly influence the competitive ability of the virus within hosts. We propose a virus trait that affects the activation of target cells, and therefore viral load, but does not confer a competitive advantage to the virus. We incorporate this virus-induced target cell activation into within- and between-host models and determine its effect on the competitive ability of virus strains and on the variation in SPVL in the host population. On the within-host level, our results show that higher rates of virus-induced target cell activation increase the SPVL and confer no selective advantage to the virus. This leads to a build up of diversity in target cell activation rates in the virus population during within-host evolution. On the between-host level, higher rates of target cell activation and therefore higher SPVL affect the transmission potential of the virus. Random selection of a new founder strain from the diverse virus population within a donor results in a standing variation in SPVL in the host population. Therefore, virus-induced target cell activation can explain the heritability of SPVL, the absence of evolution to higher viral loads during infection and a large standing variation in SPVL between hosts.