We analyzed HIV-1 genome sequences from 68 newly-infected volunteers in the Step HIV-1 vaccine trial. To determine whether the vaccine exerted selective T-cell pressure on breakthrough viruses, we identified potential T-cell epitopes in the founder sequences and compared them to epitopes in the vaccine. We found greater distances for sequences from vaccine recipients than from placebo recipients (p-values ranging from < 0.0001 to 0.09). The most significant signature site distinguishing vaccine from placebo recipients was Gag-84, a site encompassed by several epitopes contained in the vaccine and restricted by HLA alleles common in the cohort. Moreover, the extended divergence was confined to the vaccine components of the virus (Gag, Pol, Nef) and not found in other HIV-1 proteins. These results represent the first evidence of selective pressure from vaccine-induced T-cell responses on HIV-1 infection.

DIVEIN is a web interface that performs automated phylogenetic and other analyses of nucleotide and amino acid sequences. Starting with a set of aligned sequences, DIVEIN estimates evolutionary parameters and phylogenetic trees while allowing the user to choose from a variety of evolutionary models, it then reconstructs the consensus, Most Recent Common Ancestor (MRCA) and Center of Tree (COT) sequences. DIVEIN also provides tools for further analyses, including condensing sequence alignments to show only informative sites or private mutations, computing phylogenetic or pairwise divergence from any user-specified sequence (MRCA, Consensus, COT, or existing sequence from the alignment), computing and outputting all genetic distances in column format, calculating summary statistics of diversity and divergence from pairwise distances, and graphically representing the inferred tree and plots of divergence, diversity, and distance distribution histograms. DIVEIN is available at http://indra.mullins.microbiol.washington.edu/DIVEIN.

Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1–the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses.

Human immunodeficiency virus type 1 (HIV-1) gene sequences develop a large degree of variation between and within infected individuals (4,13,14,19,20,34,43,45,48,54,55,83,85,97,(103)(104)(105)112). In the initial period after infection, most individuals evaluated to date have shown homogeneous sequence populations of the HIV-1 surface envelope glycoprotein gene (env) (54,64,106,109,112) and a low level of variation in other structural genes, including gag p17 (109, 112) and gp41/nef (112). However, some individuals, especially women, have relatively diverse HIV-1 populations at or before seroconversion (49,63,68,69,112,114). After this initial period, divergent HIV-1 variants with different but related genetic sequences emerge and turn over throughout the course of infection (4,