期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：7
DOI：10.1073/pnas.2109172119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
HIV evolution within infected individuals creates large barriers to successful vaccination and therapy. Here, we used a model that matches viral loads and mutation rates to characterize the driving forces behind HIV evolution early during infection. Surprisingly, the best model of the data did not require explicit pressure from the host immune system. Instead, the model predicts most new viral variants are intrinsically worse at infecting new cells relative to their parents. Thus, most variants do not persist and only by occasional chance does a new fit variant come to dominate. These findings also highlight the tight connection between viral population dynamics and evolution, warranting more modeling to disentangle these processes in the future.
Modern HIV research depends crucially on both viral sequencing and population measurements. To directly link mechanistic biological processes and evolutionary dynamics during HIV infection, we developed multiple within-host phylodynamic models of HIV primary infection for comparative validation against viral load and evolutionary dynamics data. The optimal model of primary infection required no positive selection, suggesting that the host adaptive immune system reduces viral load but surprisingly does not drive observed viral evolution. Rather, the fitness (infectivity) of mutant variants is drawn from an exponential distribution in which most variants are slightly less infectious than their parents (nearly neutral evolution). This distribution was not largely different from either in vivo fitness distributions recorded beyond primary infection or in vitro distributions that are observed without adaptive immunity, suggesting the intrinsic viral fitness distribution may drive evolution. Simulated phylogenetic trees also agree with independent data and illuminate how phylogenetic inference must consider viral and immune-cell population dynamics to gain accurate mechanistic insights.
