Human and simian immunodeficiency viruses (HIV and SIV), influenza virus, and hepatitis C virus (HCV) have heavily glycosylated, highly variable surface proteins. Here we explore N-linked glycosylation site (sequon) variation at the population level in these viruses, using a new Web-based program developed to facilitate the sequon tracking and to define patterns (www.hiv.lanl.gov). This tool allowed rapid visualization of the two distinctive patterns of sequon variation found in HIV-1, HIV-2, and SIV CPZ. The first pattern (fixed) describes readily aligned sites that are either simply present or absent. These sites tend to be occupied by high-mannose glycans. The second pattern (shifting) refers to sites embedded in regions of extreme local length variation and is characterized by shifts in terms of the relative position and local density of sequons; these sites tend to be populated by complex carbohydrates. HIV, with its extreme variation in number and precise location of sequons, does not have a net increase in the number of sites over time at the population level. Primate lentiviral lineages have host species-dependent levels of sequon shifting, with HIV-1 in humans the most extreme. HCV E1 and E2 proteins, despite evolving extremely rapidly through point mutation, show limited sequon variation, although two shifting sites were identified. Human influenza A hemagglutinin H3 HA1 is accumulating sequons over time, but this trend is not evident in any other avian or human influenza A serotypes.

BACKGROUND: Many RNA viruses do not have a single, representative genome but instead form a set of related variants that has been called a quasispecies. The sequence variability of such viruses presents a significant bioinformatics challenge. In order for the sequence information to be understood, the complete mutational spectrum needs to be distilled to a biologically relevant and analyzable representation. RESULTS: Here, we develop a "selection mapping" algorithm?QUASI?that identifies the positively selected variants of viral proteins. The key to the selection mapping algorithm is the identification of particular replacement mutations that are overabundant relative to silent mutations at each codon (e.g., threonine at hemagglutinin position 262). Selection mapping identifies such replacement mutations as positively selected. Conversely, selection mapping recognizes negatively selected variants as mutational "noise" (e.g., serine at hemagglutinin position 262). CONCLUSION: Selection mapping is a fundamental improvement over earlier methods (e.g., dN/dS) that identify positive selection at codons but do not identify which amino acids at these codons confer selective advantage. Using QUASI?s selection maps, we characterize the selected mutational landscapes of influenza A H3 hemagglutinin, HIV-1 reverse transcriptase, and HIV-1 gp120.

A simple nonparameteric test for population structure was applied to temporally spaced samples of HIV-1 sequences from the gag-pol region within two chronically infected individuals. The results show that temporal structure can be detected for samples separated by about 22 months or more. The performance of the method, which was originally proposed to detect geographic structure, was tested for temporally spaced samples using neutral coalescent simulations. Simulations showed that the method is robust to variation in samples sizes and mutation rates, to the presence/absence of recombination, and that the power to detect temporal structure is high. By comparing levels of temporal structure in simulations to the levels observed in real data, we estimate the effective intra-individual population size of HIV-1 to be between 103 and 104 viruses, which is in agreement with some previous estimates. Using this estimate and a simple measure of sequence diversity, we estimate an effective neutral mutation rate of about 5 x 10-6 per site per generation in the gag-pol region. The definition and interpretation of estimates of such "effective" population parameters are discussed. 10.1093/molbev/msh196

HIV-1 group O originated through cross-species transmission of SIV from chimpanzees to humans and has established a relatively low prevalence in Central Africa. Here, we infer the population genetics and epidemic history of HIV-1 group O from viral gene sequence data and evaluate the effect of variable evolutionary rates and recombination on our estimates. First, model selection tools were used to specify suitable evolutionary and coalescent models for HIV group O. Second, divergence times and population genetic parameters were estimated in a Bayesian framework using Markov chain Monte Carlo sampling, under both strict and relaxed molecular clock methods. Our results date the origin of the group O radiation to around 1920 (1890-1940), a time frame similar to that estimated for HIV-1 group M. However, group O infections, which remain almost wholly restricted to Cameroon, show a slower rate of exponential growth during the twentieth century, explaining their lower current prevalence. To explore the effect of recombination, the Bayesian framework is extended to incorporate multiple unlinked loci. Although recombination can bias estimates of the time to the most recent common ancestor, this effect does not appear to be important for HIV-1 group O. In addition, we show that evolutionary rate estimates for different HIV genes accurately reflect differential selective constraints along the HIV genome.