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MBE Advance Access published online on October 27, 2004
Molecular Biology and Evolution, doi:10.1093/molbev/msi029
Molecular Biology and Evolution © Society for Molecular Biology and Evolution 2004; all rights reserved
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Accepted October 20, 2004

Research Article

A Statistical Characterization of Consistent Patterns of Human Immunodeficiency Virus Evolution Within Infected Patients

Scott Williamson 1*, Steven M. Perry 2, Carlos D. Bustamante 3, Maria E. Orive 2, Miles N. Stearns 2, and John K. Kelly 2

1 Department of Biological Statistics and Computational Biology, 434 Warren Hall, Cornell University, Ithaca, NY 14853; Department of Ecology and Evolutionary Biology, University of Kansas, 1200 Sunnyside Avenue, Lawrence, KS 66045
2 Department of Ecology and Evolutionary Biology, University of Kansas, 1200 Sunnyside Avenue, Lawrence, KS 66045
3 Department of Biological Statistics and Computational Biology, 434 Warren Hall, Cornell University, Ithaca, NY 14853

* To whom correspondence should be addressed.
Scott Williamson, E-mail: sw292{at}cornell.edu


  Abstract

Within-patient HIV populations evolve rapidly because of a high mutation rate, short generation time, and strong positive selection pressures. Previous studies have identified "consistent patterns" of viral sequence evolution. Just before HIV infection progresses to AIDS, evolution seems to slow markedly and the genetic diversity of the viral population drops. This evolutionary slowdown could be caused either by a reduction in the average viral replication rate or because selection pressures weaken with the collapse of the immune system. The former hypothesis (which we denote "cellular exhaustion") predicts a simultaneous reduction in both synonymous and non-synonymous evolution, while the latter hypothesis (denoted "immune relaxation") predicts that only non-synonymous evolution will slow. In this paper, we present a set of statistical procedures for distinguishing between these alternative hypotheses using DNA sequences sampled over the course of infection. The first component is a new method for estimating evolutionary rates that takes advantage of the temporal information in longitudinal DNA sequence samples. Second, we develop a set of probability models for the analysis of evolutionary rates in HIV populations in vivo. Application of these models to both synonymous and non-synonymous evolution affords a comparison of the cellular exhaustion and immune relaxation hypotheses. We apply the procedures to longitudinal data sets where sequences of the env gene were sampled over the entire course of infection. Our analyses (1) statistically confirm that an evolutionary slowdown occurs late in infection, (2) strongly support the immune relaxation hypothesis, and (3) indicate that the cessation of non-synonymous evolution is associated with disease progression.

Keywords: HIV; divergence rate; longitudinal study.
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