We have created a robust model system for studying the evolution of human immunodeficiency virus (HIV) through a complementary combination of a novel in vitro experimental approach and molecular-level computational simulations. This novel approach has allowed us to directly observe, explain, and predict many aspects of the stochastic process by which HIV evolves novel types of resistance to therapeutic intervention. These investigations have suggested a novel mechanism by which individual viruses of different genetic sequences and phenotypes cooperate to overcome an evolutionary challenge. We hope that by better understanding this process of escape and the cooperativity that enables it, it will be possible to thwart this process with appropriately designed therapeutic interventions.

Current treatments for HIV infections utilize combinations of chemotherapeutic drugs to block viral replication. However, the efficacy of this approach is often undermined by undesired side effects, the emergence of drug-resistant viral strains, and the high costs of lifelong medication. Many researchers are now seeking to utilize therapeutic gene delivery to block HIV replication and prevent the onset of AIDS, since this approach may overcome some of the limitations of drug therapy. We are focusing on genetic therapies that utilize a particularly robust technology called RNA interference (RNAi), which makes it possible to specifically inhibit the expression of virtually any gene by activating and directing an innate cellular pathway. In particular, we are interested in therapies that use RNAi to inhibit the expression of HIV genes and thereby suppress its replication. However, it is now clear that HIV evolution also threatens the success of these novel therapies, since it has been shown that HIV can modulate its own genetic sequence to become resistant to some implementations of RNAi-based inhibition.

In order to develop antiviral RNAi strategies that delay or prevent the emergence of resistant HIV, we seek to understand the complex and cooperative processes by which HIV evolves and replicates while under selection by antiviral RNAi. Previously developed theoretical methods are not amenable to directly incorporating the molecular mechanisms of viral inhibition and escape in this context. Therefore, we developed a novel agent-based computational simulation to capture the stochastic molecular-level events that occur during HIV's unique life cycle and to explicitly incorporate the unique aspects of HIV biology. Importantly, this approach incorporates many of the cooperative and competitive interactions that occur between individual viruses within a single population, leading to an aggregate behavior that cannot be predicted based on the behavior of any given individual. This approach has allowed us to quantitatively evaluate clinically relevant questions that cannot be addressed using other methods, and these results facilitated the design of in vitro experiments to validate these predictions.

We have also constructed an in vitro system to observe the process of HIV evolution directly. In order to provide HIV with an evolutionary challenge that would be difficult to overcome, we first developed a novel RNAi-based inhibitor directed against one of the most genetically inflexible sequences in HIV. We incorporated this inhibitor into a gene delivery vector and used it to create stable human lymphocyte cell lines that elicit antiviral RNAi, and these cells were challenged with replicating HIV-1 virus. Importantly, this setup exhibits highly reproducible and quantifiable dynamics at relevant time scales. Our experimental observations validated several important predictions made using the simulations, including the existence of a critical RNAi “dose” threshold that is required to suppress viral escape. We have also observed that HIV appears to evolve resistance to this potent inhibitor by a cooperative mechanism that differs substantially from the manner in which the virus evolves resistance to traditional antiviral drugs. This system thus serves as a robust tool for observing and understanding the many phenomena that comprise the complex adaptive process of HIV's evolution. These studies should help to inform the design of effective RNAi-based therapies for suppressing HIV infections and improving the long-term outcomes of clinical antiviral applications. Moreover, this approach could potentially be extended to facilitate understanding complex adaptive behaviors in other rapidly evolving human viral pathogens and designing effective therapies.