The genetic diversity of human immunodeficiency virus (HIV) is a major concern thought to impact on immunologic escape and eventual vaccine efficacy. Here, simple and rapid methods are described for the detection and estimation of genetic divergence between HIV strains on the basis of the observation that DNA heteroduplexes formed between related sequences have a reduced mobility in polyacrylamide gels proportional to their degree of divergence. Reliable phylogenetic subtypes were assigned for HIV-1 strains from around the world. Relationships between viruses were closest when derived from the same or epidemiologically linked individuals. When derived from epidemiologically unlinked individuals, the relationships between viruses in a given geographic region correlated with the length of time HIV-1 had been detected in the population and the number of strains initiating widespread infection. Heteroduplex mobility analysis thus provides a tool to expedite epidemiological investigations by assisting in the classification of HIV and is readily applicable to the screening and characterization of other infectious agents and cellular genes.
Feline immunodeficiency virus (FlI) is a lentivirus associated with AIDS-like illnesses in cats. As such, FIV appears to be a feline analog of human immunodeficiency virus (HIV). A hallmark of HIV infection is the large degree of viral genetic diversity that can develop within an infected individual and the even greater and continually increasing level of diversity among virus isolates from different individuals. Our goal in this study was to determine patterns of FIV genetic diversity by focusing on a 684-nucleotide region encompassing variable regions V3, V4, and V5 of the FIV env gene in order to establish parallels and distinctions between FIV and HIV type 1 (HIV-1). Our data demonstrate that, like HIV-1, FIV can be separated into distinct envelope sequence subtypes (three are described here). Similar to that found for HIV-1, the pairwise sequence divergence within an FIV subtype ranged from 2.5 to 15.0%, whereas that between subtypes ranged from 17.8 to 26.2%. However, the high number of synonymous nucleotide changes among FIV V3 to V5 env sequences may also include a significant number of back mutations and suggests that the evolutionary distances among FIV subtypes are underestimated. Although only a few subtype B viruses were available for examination, the pattern of diversity between the FIV A and B subtypes was found to be significantly distinct; subtype B sequences had proportionally fewer mutations that changed amino acids, compared with silent changes, suggesting a more advanced state of adaptation to the host. No similar distinction was evident for HIV-1 subtypes. The diversity of FIV genomes within individual infected cats was found to be as high as 3.7% yet twofold lower than that within HIV-1-infected people over a comparable region of the env gene. Despite these differences, significant parallels between patterns of FIV evolution and HIV-1 evolution exist, indicating that a wide array of potentially divergent virus challenges need to be considered in FIV vaccine and pathogenesis studies.
The generation time of HIV Type 1 (HIV-1) in vivo has previously been estimated using a mathematical model of viral dynamics and was found to be on the order of one to two days per generation. Here, we describe a new method based on coalescence theory that allows the estimate of generation times to be derived by using nucleotide sequence data and a reconstructed genealogy of sequences obtained over time. The method is applied to sequences obtained from a long-term nonprogressing individual at five sampling occasions. The estimate of viral generation time using the coalescent method is 1.2 days per generation and is close to that obtained by mathematical modeling (1.8 days per generation), thus strengthening confidence in estimates of a short viral generation time. Apart from the estimation of relevant parameters relating to viral dynamics, coalescent modeling also allows us to simulate the evolutionary behavior of samples of sequences obtained over time.The integration of mathematical modeling and experimental approaches has led to a deeper understanding of HIV-1 viral dynamics in vivo. In particular, these studies suggest that the viral population in the peripheral blood (and the secondary lymphatics) turns over rapidly, with generation times estimated to be on the order of one to two days on average (ref. 1; A. Perelson, personal communication). However, because these studies are based on abstract and simple models of a complex biological system, it is not obvious how accurate these estimates are. One way to address this is to try to estimate the same parameters by using different methods and different types of data.Here, we apply a new method developed by Rodrigo and Felsenstein (2) which is based on a mathematical construct introduced by Kingman (3, 4) called the n-coalescent (or coalescent for short). The coalescent relies on the fundamental notion that all individuals in a population have a genealogy, so that if we begin with a sample of n individuals, each drawn randomly from a population, and we reconstruct the genealogy of these n individuals, we will begin to see lineages coalescing as we move further back in time. Each coalescent event represents the split of two lineages from a common ancestor. Obviously, if we go back far enough, we arrive at a point where all lineages have coalesced, and this point represents the most recent common ancestor of all sequences in the sample. The mathematics of coalescent theory gives us the distribution of times, measured as the number of generations, between coalescent events, as one moves back in time along the genealogy. The distribution of times itself is contingent on the dynamics of the population in question. For instance, the expected coalescence time of two individuals drawn at random, each from a compartment of a subdivided population, depends on the rate of migration between the compartments. Similarly, two individuals drawn from a growing population will have a different expected time to coalescence than two individuals drawn from a population at e...
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