Description of HIV social behavior using a phylogenetic model of structured co-evolution

PROJECT SUMMARY Chronic viral and bacterial infections are characterized by pathogens that are difficult to control by the host immune system and therapeutic interventions. Evasion of anti-infection mechanisms for a number of these pathogens can be attributed to the formation of structured populations, comprised of highly organized pathogen subgroups with specialized, cooperative functions. Pathogen communities, such as biofilms, can arise as a result of replication, initiating with just one infectious particle. Effective persistence in the face of natural and synthetic defenses, however, requires maintenance in the form of compensatory mutations that correct for deleterious mutations favoring individual fitness over that of the population. These co-evolutionary events can be observed over time via genomic sequencing of individual subgroups and, importantly, can be used to investigate unknown biological pathways or mechanisms. HIV shares many characteristics with bacterial biofilms and other social viruses, including chronic infection, antiretroviral resistance, and population structure among and within infected host organs. Given these characteristics, it was not surprising to uncover initial evidence of across-tissue co- evolution in the SIV-infected macaque model. In the absence of a cure for HIV infection, there is a critical need to explore alternative behaviors that can explain HIV persistence and lead to novel therapeutic strategies. The overarching objective of this proposal is to improve our understanding of the complex population dynamics that characterize chronic HIV infection through 1) development of a novel statistical phylogenetic tool (GOSIP) and framework, capable of reliably identifying cooperative behavior through compensatory evolution, and 2) application of this tool/framework to full-genome sequence data available from an existing cohort of extensively sampled SIV-infected macaques. The central hypothesis is that the implementation of a statistical graphical modeling approach can accurately capture co-evolving sites in interacting subpopulations, including those of the SIV intra-host population. The innovation of this project lies in its treatment of cooperative behavior as the driving factor of the HIV population survival in the face of immune and therapeutic defenses, which can be statistically inferred via a novel phylogenetic platform.