Speaker
Description
Background
Growing evidence suggests social interactions within viral populations may influence adaptation and persistence. For example, hepatitis C virus (HCV) is hypothesized to exhibit antigenic cooperation (AC), a kind of altruistic behavior enabling immune escape of specific variant populations. The AC model raises the question as to whether other chronic viral infections can be better understood through a social framework? For example, cooperative behavioral dynamics within measurably evolving organisms are often disrupted by mutation, requiring compensatory mutations (i.e., co-evolution) to restore homeostasis. Because HIV is one of the most rapidly mutating infectious viruses, occurrences of compensatory mutations within the host may occur with sufficient frequency to detect the presence of cooperative behavior and inform the interactions responsible. Yet, no computational tool exists that can distinguish population-level from individual genome co-evolution to investigate the putative role of viral cooperative interactions in disease.
Methods
We developed the Graphical mOdel of Social Interactions using Phylogenies (GOSIP), capable of capturing significantly co-evolving sites across branches of simulated viral populations with 98% accuracy. We applied this tool to S[imian]IV envelope sequences sampled longitudinally from multiple tissues within two cohorts of untreated macaques – one undergoing transient CD8+ cell depletion and rapid SAIDS onset, and the other allowed to naturally progress to SAIDS. The resulting co-evolutionary data were evaluated for tissue involvement, location within targeted immune epitopes, and potential role in disease progression.
Results
The major finding of this study was the significant inverse correlation among all animals of the time to SAIDS onset with the rate (per day) of amino acid co-evolution (R2=0.83, p<0.001).
Conclusion
Results point to viral population-level co-evolution as a more prominent biomarker of disease progression than prior markers, such as CD4+ T cell nadir or viral load set point. Determining whether the site dependencies described can be explained by AC is a critical next step, as an effective therapeutic regime in this context would require a multi-faceted immune attack that may not be achievable through a single broadly neutralizing antibody response.
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