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,

Selecting human immunodeficiency virus (HIV) sequences for inclusion within vaccines has been a difficult problem, as circulating HIV strains evolve relentlessly and become increasingly divergent over time. We report an assessment of this divergence from three perspectives: (i) across different hosts as a function of time of infection, (ii) between donors and recipients in known transmission pairs, and (iii) within individual hosts over time in relation to the initially replicating virus and to the deduced ancestral sequence of the intrahost viral population. Surprisingly, we consistently found less divergence between viruses from different individuals sampled in primary infection than in individuals sampled at more advanced stages of illness. Furthermore, longitudinal analysis of intrahost divergence revealed a 2-to 3-year period of evolution toward a common ancestral sequence at the start of infection, indicating that HIV recovers certain ancestral features when infecting a new host. These results have important implications for the study of HIV population genetics and rational vaccine design, including favoring the inclusion of viral gene sequences taken early in infection.

To evaluate human immunodeficiency virus type 1 (HIV-1) replication and selection of drug-resistant viruses during seemingly effective highly active antiretroviral therapy (HAART), multiple HIV-1 env and pol sequences were analyzed and viral DNA levels were quantified from nucleoside analog-experienced children prior to and during a median of 5.1 (range, 1.8 to 6.4) years of HAART. Viral replication was detected at different rates, with apparently increasing sensitivity: 1 of 10 by phylogenetic analysis; 2 of 10 by viral evolution with increasing genetic distances from the most recent common ancestor (MRCA) of infection; 3 of 10 by selection of drug-resistant mutants; and 6 of 10 by maintenance of genetic distances from the MRCA. When four-or five-drug antiretroviral regimens were given to these children, persistent plasma viral rebound did not occur despite the accumulation of highly drug-resistant genotypes. Among the four children without genetic evidence of viral replication, a statistically significant decrease in the genetic distance to the MRCA was detected in three, indicating the persistence of a greater number of early compared to recent viruses, and their HIV-1 DNA decreased by >0.9 log 10 , resulting in lower absolute DNA levels (P ‫؍‬ 0.007). This study demonstrates the variable rates of viral replication when HAART has suppressed plasma HIV-1 RNA for years to a median of <50 copies/ml and that combinations of four or five antiretroviral drugs suppress viral replication even after short-term virologic failure of three-drug HAART and despite ongoing accumulation of drug-resistant mutants. Furthermore, the decrease of cellular HIV-1 DNA to low absolute levels in those without genetic evidence of viral replication suggests that monitoring viral DNA during HAART may gauge low-level replication.

Editorial group: Cochrane Tobacco Addiction Group. Publication status and date: New search for studies and content updated (no change to conclusions), published in Issue 12, 2017.

The deoxycytidine analog KP1212, and its prodrug KP1461, are prototypes of a new class of antiretroviral drugs designed to increase viral mutation rates, with the goal of eventually causing the collapse of the viral population. Here we present an extensive analysis of viral sequences from HIV-1 infected volunteers from the first “mechanism validation” phase II clinical trial of a mutagenic base analog in which individuals previously treated with antiviral drugs received 1600 mg of KP1461 twice per day for 124 days. Plasma viral loads were not reduced, and overall levels of viral mutation were not increased during this short-term study, however, the mutation spectrum of HIV was altered. A large number (N = 105 per sample) of sequences were analyzed, each derived from individual HIV-1 RNA templates, after 0, 56 and 124 days of therapy from 10 treated and 10 untreated control individuals (>7.1 million base pairs of unique viral templates were sequenced). We found that private mutations, those not found in more than one viral sequence and likely to have occurred in the most recent rounds of replication, increased in treated individuals relative to controls after 56 (p = 0.038) and 124 (p = 0.002) days of drug treatment. The spectrum of mutations observed in the treated group showed an excess of A to G and G to A mutations (p = 0.01), and to a lesser extent T to C and C to T mutations (p = 0.09), as predicted by the mechanism of action of the drug. These results validate the proposed mechanism of action in humans and should spur development of this novel antiretroviral approach.