Phylogenetic dependency networks: Inferring patterns of adaptation in HIV.

摘要

Populations adapt to their environment through a process of natural selection. By studying this process, one can gain insight into the specific functions of adaptive traits that provide an advantage in certain environments. HIV has proven to be remarkably adept at adaptation. So much so that the virus quickly adapts to each individual who is infected, effectively nullifying the immune response of most patients. By identifying the specific adaptations HIV employs against the immune system, it may be possible to identify vaccine targets that reduce HIV's capacity to successfully adapt.;This dissertation introduces the Phylogenetic Dependency Network (PDN) for the identification of adaptive traits and the environments in which they arise. The PDN is a directed graphical model in which nodes represent measurable traits of the population and the environment and arcs represent probabilistic dependencies among traits. The probability component of the PDN consists of a model of adaptive evolution in which each population trait adapts to a set of predictors, traits to which it is connected in the PDN. The structure of the PDN is identified through a model selection approach and can be interpreted as an estimate of which traits directly interact. We introduce a class of probabilistic adaptive evolution models called conditional adaptation models. These models assume that each trait has evolved independent of all other traits in the PDN until it reached the current environment, at which point the predictors act to influence adaptation of the trait.;One of the key benefits of this approach over traditional methods is the ability to simultaneously model multiple interactions. Existing approaches are typically constrained to consider the evolutionary interaction of two traits at a time. In complex environments in which each trait interacts with many other traits, this constrained view of adaptation blurs the distinction of which traits are truly interacting and which are only indirectly correlated. By modeling these interactions using conditional adaptation models, we are able to accurately capture dense networks of interactions.;We apply our PDN approach to study adaptation of HIV to the human cellular immune response, identifying a large set of HIV adaptations that consistently arise in patients with similar immune genetics. These adaptations often take the form of multiple mutations spanning large regions of HIV proteins and indicate the presence of preferred patterns of adaptation. Although these adaptation networks are quite complex, the presence of these preferred adaptation patterns suggest weak points in viral adaptation that may be exploited by future vaccines.