High-resolution deep sequencing reveals biodiversity, population structure, and persistence of HIV-1 quasispecies within host ecosystems
© Yin et al.; licensee BioMed Central Ltd. 2012
Received: 25 October 2012
Accepted: 20 November 2012
Published: 17 December 2012
Deep sequencing provides the basis for analysis of biodiversity of taxonomically similar organisms in an environment. While extensively applied to microbiome studies, population genetics studies of viruses are limited. To define the scope of HIV-1 population biodiversity within infected individuals, a suite of phylogenetic and population genetic algorithms was applied to HIV-1 envelope hypervariable domain 3 (Env V3) within peripheral blood mononuclear cells from a group of perinatally HIV-1 subtype B infected, therapy-naïve children.
Biodiversity of HIV-1 Env V3 quasispecies ranged from about 70 to 270 unique sequence clusters across individuals. Viral population structure was organized into a limited number of clusters that included the dominant variants combined with multiple clusters of low frequency variants. Next generation viral quasispecies evolved from low frequency variants at earlier time points through multiple non-synonymous changes in lineages within the evolutionary landscape. Minor V3 variants detected as long as four years after infection co-localized in phylogenetic reconstructions with early transmitting viruses or with subsequent plasma virus circulating two years later.
Deep sequencing defines HIV-1 population complexity and structure, reveals the ebb and flow of dominant and rare viral variants in the host ecosystem, and identifies an evolutionary record of low-frequency cell-associated viral V3 variants that persist for years. Bioinformatics pipeline developed for HIV-1 can be applied for biodiversity studies of virome populations in human, animal, or plant ecosystems.
Human immunodeficiency virus type 1 (HIV-1) displays extensive genetic diversity, reflecting the error prone characteristics of reverse transcriptase-dependent replication, elevated recombination rate and continuous selection of more fit viral variants within fluctuating host ecosystems. HIV-1 populations within an infected individual are complex and comprised of swarms of related genomes, or quasispecies [1, 2]. Studies of HIV-1 diversity within quasispecies benefited over the years by the development of novel sequencing technologies that extended the depth of sampling [1–11]. Next generation deep sequencing increases significantly the sensitivity to identify within HIV-1 quasispecies low frequency genetic variants that might lead to reduced susceptibility to antiretroviral treatments [12, 13] or escape from immunity . Beyond surveillance for drug resistance, deep sequencing provides additional advantages to detect epistatic interactions , estimate population structure , identify evolutionary intermediates, and evaluate biodiversity of organisms within an ecosystem [17–26].
Biodiversity is used in population genetics to present a unified view of the extent of variation of life forms within habitats  and assumes that genomes within an environment are taxonomically similar, randomly distributed, and sufficiently large . Assessments of biodiversity from deep sequencing data provide unprecedented views of the richness of immune loci in primates, zebra fish, and humans [17, 18, 26] or the complexity of microbiomes independent of an ability to culture microorganisms [21, 24, 25, 29]. Biodiversity defines complexity within populations that extend beyond evaluations of diversity based on pairwise genetic distance, the major approach for analysis of small data sets of HIV-1 sequences from infected individuals [30, 31]. Biodiversity within HIV-1 populations might reflect host environments, infection by circulating recombinant forms of HIV-1 or co-infection by multiple subtypes, and provide unique and sensitive biomarkers for changes in viral populations. Moreover, structure of HIV-1 quasispecies, or the frequency distribution of viral variants within individuals, may reveal the potential for viral populations to evolve within a fitness landscape and contribute to viral persistence [4, 32–34].
We designed a deep-sequencing study of HIV-1 Env V3 quasispecies within peripheral blood cells that applied population genetics tools in a novel bioinformatics pipeline to define viral biodiversity, examine viral population structure, and explore directly the extent to which deep sequencing enriches analysis of the HIV-1 evolutionary landscape.
Biodiversity of HIV-1 quasispecies
Calculated and estimated biodiversity defined by operational taxonomic units (OTUs)
Rarefaction curves at 3% distance approached, but failed to achieve a plateau, raising the possibility that depth of sequencing was insufficient to capture all viral diversity. Yet, estimated maximum biodiversity was only about two-fold greater than, and correlated with, calculated biodiversity (r = 0.89; p = 0.02) (Table 1), indicating that sequence depth (about 25-fold coverage) was sufficient to provide a robust assessment of V3 biodiversity within a sample. In general, biodiversity among the six subjects appeared unrelated to viral levels in plasma or cells, length of infection, or CD4 T cell levels (Additional file 1), but revealed patterns of complexity within viral quasispecies in different host environments.
Enriched evolutionary landscape within HIV-1 quasispecies
Most recent common ancestors in the evolutionary landscape
V3 populations in S5 developed along lineages with multiple amino acid changes at branch nodes, providing an opportunity to infer the most recent common ancestor (MRCA) of each lineage. Based on clonal sequences, the earliest viral population gave rise through ancestral node 1 (anc1) to two subsequent lineages (Figure 4B). L1 progressed through node 2 (anc2) with changes in V3 at two amino acid positions, E322D and Y316H (Figure 4D), while L2 gave rise by two different amino acid substitutions, Q308R and E322K (Figure 4D) to viruses at 6 to 7 years of infection through anc3 (Figure 4B). Depth of conventional clonal sequencing was inadequate to assign a temporal order to the amino acid changes between MRCA at anc1 and anc2 or anc3. Inclusion of pyrosequences in the analysis provided sufficient coverage of the viral population to infer that the E322D change (anc2’) appeared before the Y316H substitution, while Q308R (anc3’) preceded the E322K substitution (Figure 4D).
Biodiversity is routinely applied to metagenomics of a variety of species, including the human microbiome, but only limited, if any, assessment of viromes in different ecological niches. Our study applies an efficient bioinformatic pipeline that we developed to assess the complexity of HIV-1 quasispecies in unique ecosystems within infected individuals. The power of pyrosequencing to generate extensive sequence data sets provides a foundation to apply population genetic analyses and extends the value for deep sequencing beyond analysis of rare variants that might indicate reduced sensitivity to drugs. Analysis of biodiversity based on sequence clustering provides a novel viral population profile for different environments independent of viral levels in cells or plasma, perhaps reflecting length of infection if sequences were archived in lineages of long-lived cells. Consistent with this model, complex viral population structure with high biodiversity appeared as early as eighteen months, or by four to six years, of infection in some individuals. Yet, similar periods of infection in other individuals were characterized by monomorphic viral populations with low complexity, indicating that biodiversity of V3 populations represents complex combinations of factors; for example, changes in viral fitness in the environmental landscape in response to host immunity, host target cells, or coreceptor evolution under selective pressure.
Another novel aspect of our study involved a combination of cross-sectional deep sequencing with conventional longitudinal sequences to provide high-resolution detection of evolutionary intermediates, which may be less fit or infrequent in peripheral blood, but nonetheless contribute to the genetic flexibility of the population. The specific order of amino acid substitutions over time may reflect important epistatic interactions that could focus detection of compensatory mutations contributing to fitness in the genetic landscape to other regions of the virus genome. Deep sequencing data sets fill in the evolutionary landscape and increase the power to infer the temporal accumulation of amino acid substitutions, or provide a basis for rational functional analysis of ancestral envelopes and the progeny that emerge from recurring viral population bottlenecks.
An apparent paradox from our analyses is the contribution by low-frequency, presumably less-fit viral variants, rather than the dominant variants, to next generation plasma HIV-1 populations with enhanced fitness. Low-frequency variants expand the fitness landscape for virus populations, while providing an array of evolutionary options to maximize survival in a changing ecosystem . Low frequency cell-associated HIV-1 quasispecies may represent residual genomes from a past dominant population archived in long-lived cells, a sequestered reservoir that only infrequently finds its way into the peripheral blood, and/or progenitors that gives rise to the next generation of dominant variants in the plasma. Transient dominance of a population leaves a molecular trail that persists as low frequency variants archived in peripheral blood. In agreement with studies of heterosexual HIV-1 transmission , archeological evidence of the earliest viral populations was found in our study of pediatric cells as long as four years after infection by maternal transmission, suggesting those early viruses, or at least their V3 domains, endure during the natural history of infection.
While the study focused on HIV-1 populations in human environments, the approach is applicable to an array of viruses with complex populations, including other subtypes or recombinant forms of HIV-1, hepatitis C or hepatitis B viruses, as well as the repertoire of related viruses that infect animals. Increased depth of sampling and extended length of the target region now possible by pyrosequencing combined with efficient bioinformatic pipelines provides a basis for developing quantitative measures of the ebb and flow of viral populations in changing environments.
Deep sequencing of HIV-1 Env V3 hypervariable domains combined with conventional longitudinal V3 sequence data sets provides high resolution of the evolutionary landscape of HIV-1 quasispecies, reveals the richness of viral diversity within the ecosystems of infected individuals, explores the ebb and flow of dominant high-fit and low frequency less-fit viral variants, infers details of multistep evolutionary events in the fitness landscape, and identifies persistence of low-frequency viral variants in peripheral blood cells that resemble transmitted viruses.
Peripheral mononuclear cells (PBMC) were obtained from a cohort of HIV-1 children with parental informed consent under a protocol approved by the Institutional Review Board of the University of Florida. Study included six therapy-naïve subjects, infected perinatally between 1989 and 1995 through maternal transmission of subtype B HIV-1, with median plasma viral load of 4.9 (quartile range 4.6 to 5.3) log10 HIV-1 RNA copies per ml, median age/length of infection of 4.4 (quartile range: 2.0 to 5.1) years, and median CD4 levels of 22% (quartile range 13.3% to 25.5%) at the time of deep sequencing (Additional file 1).
Clonal and pyrosequences
Clonal sequences from HIV-1 Env V1 through V5 were generated using AmpliTaq (Life Technologies Corporation, Carlsbad, CA, US) as previously described . Amplicon libraries were constructed from PBMC DNA with 400 HIV-1 copies using GoTaq DNA polymerase (Promega, Madison, WI, US), as previously described [38, 39] and submitted to the University of Florida Interdisciplinary Center for Biotechnology Research for pyrosequencing using a proprietary DNA polymerase (a mixture of Taq and high fidelity DNA polymerases) (Roche/454 Life Sciences) on a Genome Sequencer FLX (Roche/454 Life Sciences) to produce an average of about 10,000 reads per sample or about 25-fold coverage of 400 template copies (10,000 sequences ÷ 400 viral copies = 25 fold coverage). Raw clonal and pyrosequencing nucleic acid data sets are deposited in EMBL data base (EMBL accession numbers pending).
A bioinformatics pipeline developed by our group was applied to the data sets. The pipeline incorporates a series of quality control and error correction filters to reduce random nucleotide substitutions, correct frame shifts, and eliminate hypermutated or recombinant sequences (Additional file 2). Overall, the analysis pipeline produced high-quality data sets with retention of about 90% to 97% of the sequences from any sample (Additional file 3). Integrity of error-corrected datasets from deep sequencing was verified by phylogentic construction (Additional file 4).
In general, maximum likelihood pairwise distances within deep sequence data sets were significantly greater than among conventional sequence data from each individual (p < 0.001). To assess biodiversity of HIV-1 Env quasispecies, rarefaction curves were constructed using the ESPRIT software suite . Numbers of OTU are displayed on the y-axis as a function of percentage of sequences (sequences sampled ÷ total sequences generated from 400 input viral copies x 100%) displayed on the x-axis. Sequences were clustered across a range of pairwise distances from 0% to 10% with all previously collapsed reads counted for their absolute occurrence. One OTU equates to one sequence cluster. ESPRIT was also used to estimate maximum biodiversity within 400 input viral copies using abundance-based coverage estimator (ACE), constructed consensus sequence from each sequence cluster, and calculated the frequency of each OTU.
Construction of phylogenetic trees and most recent common ancestor (MRCA) analysis
Maximum likelihood (ML) phylogenetic trees combined deep sequencing cluster consensus reads and longitudinal clonal sequences for subjects S1 and S5 were constructed from nucleotide sequences aligned in BioEdit. Alignments were trimmed to the V3 loop defined by codons for cysteine 296 to cysteine 331 based on gp160 amino acid numbering in HXB2 genome, and identical nucleic acid clusters were collapsed.
Phylogenetic signal within S1 or S5 datasets of aligned sequences was evaluated by likelihood mapping analyses with the program TREE-PUZZLE, and proven to be sufficient for reliable phylogeny inference [40–42] (Additional file 5). Trees were constructed as previously described . Briefly, the heuristic search for the best tree was performed using a neighbor-joining tree and the tree bisection reconnection algorithm with PAUP* 4.0b10 [43, 44]. Trees were rooted using the earliest clonal sequences as the out group. Significance of branches was determined by the approximate likelihood ratio test [45–47]. For analysis of MRCA, ancestral nucleic acid sequences in the genealogy obtained for S5 were inferred by the maximum likelihood method using the codon substitution model M0 in the PAML software package . Reconstructed ancestral sequences from internal nodes were analyzed in BioEdit for nonsynonymous changes at each codon position.
Pearson correlation was applied to analyze correlations between biodiversity calculated from rarefaction curves generated at 0% and 3% pairwise distances, and between calculated and ACE-estimated maximum biodiversity. Statistical analyses were performed using SAS version 9.1 (SAS 191 Institute, Cary, NC) with P < 0.05 defined as significant.
LL is currently a faculty member at the University of Arizona.
YS is currently a faculty member at the University of Buffalo.
WH is currently a faculty member at the Stony Brook University Medical Center.
BPG is currently a medical student in Philadelphia College of Osteopathic Medicine in Suwanee, Georgia. WBW is currently a postdoctoral research fellow at the Duke University.
The authors thank the study volunteers for participating; Drs. Connie J. Mulligan, Volker Mai, Mark A. Wallet, Nazle Mendonca Veres, and Rebecca R. Gray for critical reading of this manuscript. Research was supported in part by NIH/NIAID R01 AI065265 and R01 AI047723; Elizabeth Glaser Pediatric AIDS Foundation MV-00-9-900-0143-0-00; Florida Center for AIDS Research; Center for Research in Human Immune Deficiency and Inflammation; and Stephany W. Holloway University Chair for AIDS Research.
- Garcia-Arriaza J, Domingo E, Briones C: Characterization of minority subpopulations in the mutant spectrum of HIV-1 quasispecies by successive specific amplifications. Virus Res. 2007, 129 (2): 123-134. 10.1016/j.virusres.2007.07.001.View ArticlePubMedGoogle Scholar
- Paredes R, Clotet B: Clinical management of HIV-1 resistance. Antiviral Res. 2010, 85 (1): 245-265. 10.1016/j.antiviral.2009.09.015.View ArticlePubMedGoogle Scholar
- Boutwell CL, Rolland MM, Herbeck JT, Mullins JI, Allen TM: Viral evolution and escape during acute HIV-1 infection. J Infect Dis. 2010, 202 (Suppl 2): S309-314.PubMed CentralView ArticlePubMedGoogle Scholar
- Goodenow M, Huet T, Saurin W, Kwok S, Sninsky J, Wain-Hobson S: HIV-1 isolates are rapidly evolving quasispecies: evidence for viral mixtures and preferred nucleotide substitutions. J Acquir Immune Defic Syndr. 1989, 2 (4): 344-352.PubMedGoogle Scholar
- Lamers SL, Sleasman JW, She JX, Barrie KA, Pomeroy SM, Barrett DJ, Goodenow MM: Independent variation and positive selection in env V1 and V2 domains within maternal-infant strains of human immunodeficiency virus type 1 in vivo. J Virol. 1993, 67 (7): 3951-3960.PubMed CentralPubMedGoogle Scholar
- Lamers SL, Sleasman JW, She JX, Barrie KA, Pomeroy SM, Barrett DJ, Goodenow MM: Persistence of multiple maternal genotypes of human immunodeficiency virus type I in infants infected by vertical transmission. J Clin Invest. 1994, 93 (1): 380-390. 10.1172/JCI116970.PubMed CentralView ArticlePubMedGoogle Scholar
- Nickle DC, Shriner D, Mittler JE, Frenkel LM, Mullins JI: Importance and detection of virus reservoirs and compartments of HIV infection. Curr Opin Microbiol. 2003, 6 (4): 410-416. 10.1016/S1369-5274(03)00096-1.View ArticlePubMedGoogle Scholar
- Nowak MA, May RM, Anderson RM: The evolutionary dynamics of HIV-1 quasispecies and the development of immunodeficiency disease. AIDS. 1990, 4 (11): 1095-1103. 10.1097/00002030-199011000-00007.View ArticlePubMedGoogle Scholar
- Salemi M, Burkhardt BR, Gray RR, Ghaffari G, Sleasman JW, Goodenow MM: Phylodynamics of HIV-1 in lymphoid and non-lymphoid tissues reveals a central role for the thymus in emergence of CXCR4-using quasispecies. PLoS One. 2007, 2 (9): e950-10.1371/journal.pone.0000950.PubMed CentralView ArticlePubMedGoogle Scholar
- Simmonds P, Balfe P, Ludlam CA, Bishop JO, Brown AJ: Analysis of sequence diversity in hypervariable regions of the external glycoprotein of human immunodeficiency virus type 1. J Virol. 1990, 64 (12): 5840-5850.PubMed CentralPubMedGoogle Scholar
- Wolinsky SM, Wike CM, Korber BT, Hutto C, Parks WP, Rosenblum LL, Kunstman KJ, Furtado MR, Munoz JL: Selective transmission of human immunodeficiency virus type-1 variants from mothers to infants. Science. 1992, 255 (5048): 1134-1137. 10.1126/science.1546316.View ArticlePubMedGoogle Scholar
- Simen BB, Simons JF, Hullsiek KH, Novak RM, Macarthur RD, Baxter JD, Huang C, Lubeski C, Turenchalk GS, Braverman MS, Desany B, Rothberg JM, Egholm M, Kozal MJ: Low-abundance drug-resistant viral variants in chronically HIV-infected, antiretroviral treatment-naive patients significantly impact treatment outcomes. J Infect Dis. 2009, 199 (5): 693-701. 10.1086/596736.View ArticlePubMedGoogle Scholar
- Tsibris AM, Korber B, Arnaout R, Russ C, Lo CC, Leitner T, Gaschen B, Theiler J, Paredes R, Su Z, Hughes MD, Gulick RM, Greaves W, Coakley E, Flexner C, Nusbaum C, Kuritzkes DR: Quantitative deep sequencing reveals dynamic HIV-1 escape and large population shifts during CCR5 antagonist therapy in vivo. PLoS One. 2009, 4 (5): e5683-10.1371/journal.pone.0005683.PubMed CentralView ArticlePubMedGoogle Scholar
- Henn MR, Boutwell CL, Charlebois P, Lennon NJ, Power KA, Macalalad AR, Berlin AM, Malboeuf CM, Ryan EM, Gnerre S, Zody MC, Erlich RL, Green LM, Berical A, Wang Y, Casali M, Streeck H, Bloom AK, Dudek T, Tully D, Newman R, Axten KL, Gladden AD, Battis L, Kemper M, Zeng Q, Shea TP, Gujja S, Zedlack C, Gasser O, Brander C, Hess C, Gunthard HF, Brumme ZL, Brumme CJ, Bazner S, Rychert J, Tinsley JP, Mayer KH, Rosenberg E, Pereyra F, Levin JZ, Young SK, Jessen H, Altfeld M, Birren BW, Walker BD, Allen TM: Whole genome deep sequencing of HIV-1 reveals the impact of early minor variants upon immune recognition during acute infection. PLoS Pathog. 2012, 8 (3): e1002529-10.1371/journal.ppat.1002529.PubMed CentralView ArticlePubMedGoogle Scholar
- Poon AF, Swenson LC, Dong WW, Deng W, Kosakovsky Pond SL, Brumme ZL, Mullins JI, Richman DD, Harrigan PR, Frost SD: Phylogenetic analysis of population-based and deep sequencing data to identify coevolving sites in the nef gene of HIV-1. Mol Biol Evol. 2009, 27 (4): 819-832.PubMed CentralView ArticlePubMedGoogle Scholar
- Eriksson N, Pachter L, Mitsuya Y, Rhee SY, Wang C, Gharizadeh B, Ronaghi M, Shafer RW, Beerenwinkel N: Viral population estimation using pyrosequencing. PLoS Comput Biol. 2008, 4 (4): e1000074-PubMed CentralView ArticlePubMedGoogle Scholar
- Bimber BN, Burwitz BJ, O’Connor S, Detmer A, Gostick E, Lank SM, Price DA, Hughes A, O’Connor D: Ultradeep pyrosequencing detects complex patterns of CD8+ T-lymphocyte escape in simian immunodeficiency virus-infected macaques. J Virol. 2009, 83 (16): 8247-8253. 10.1128/JVI.00897-09.PubMed CentralView ArticlePubMedGoogle Scholar
- Boyd SD, Marshall EL, Merker JD, Maniar JM, Zhang LN, Sahaf B, Jones CD, Simen BB, Hanczaruk B, Nguyen KD, Nadeau KC, Egholm M, Miklos DB, Zehnder JL, Fire AZ: Measurement and clinical monitoring of human lymphocyte clonality by massively parallel VDJ pyrosequencing. Sci Transl Med. 2009, 1 (12): 12ra23-10.1126/scitranslmed.3000540.PubMed CentralView ArticlePubMedGoogle Scholar
- Goodman AL, McNulty NP, Zhao Y, Leip D, Mitra RD, Lozupone CA, Knight R, Gordon JI: Identifying genetic determinants needed to establish a human gut symbiont in its habitat. Cell Host Microbe. 2009, 6 (3): 279-289. 10.1016/j.chom.2009.08.003.PubMed CentralView ArticlePubMedGoogle Scholar
- Hamady M, Knight R: Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 2009, 19 (7): 1141-1152. 10.1101/gr.085464.108.PubMed CentralView ArticlePubMedGoogle Scholar
- Keijser BJ, Zaura E, Huse SM, van der Vossen JM, Schuren FH, Montijn RC, ten Cate JM, Crielaard W: Pyrosequencing analysis of the oral microflora of healthy adults. J Dent Res. 2008, 87 (11): 1016-1020. 10.1177/154405910808701104.View ArticlePubMedGoogle Scholar
- McCaig AE, Glover LA, Prosser JI: Molecular analysis of bacterial community structure and diversity in unimproved and improved upland grass pastures. Appl Environ Microbiol. 1999, 65 (4): 1721-1730.PubMed CentralPubMedGoogle Scholar
- Schloss PD, Handelsman J: Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl Environ Microbiol. 2005, 71 (3): 1501-1506. 10.1128/AEM.71.3.1501-1506.2005.PubMed CentralView ArticlePubMedGoogle Scholar
- Sogin ML, Morrison HG, Huber JA, Mark WD, Huse SM, Neal PR, Arrieta JM, Herndl GJ: Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci U S A. 2006, 103 (32): 12115-12120. 10.1073/pnas.0605127103.PubMed CentralView ArticlePubMedGoogle Scholar
- Sun Y, Cai Y, Liu L, Yu F, Farrell ML, McKendree W, Farmerie W: ESPRIT: estimating species richness using large collections of 16S rRNA pyrosequences. Nucleic Acids Res. 2009, 37 (10): e76-10.1093/nar/gkp285.PubMed CentralView ArticlePubMedGoogle Scholar
- Weinstein JA, Jiang N, White RA, Fisher DS, Quake SR: High-throughput sequencing of the zebrafish antibody repertoire. Science. 2009, 324 (5928): 807-810. 10.1126/science.1170020.PubMed CentralView ArticlePubMedGoogle Scholar
- Campbell A: Save those molecules: molecular biodiversity and life. Journal of Applied Ecology. 2003, 40 (2): 193-203. 10.1046/j.1365-2664.2003.00803.x.View ArticleGoogle Scholar
- Newton AC: Forest Ecology and preservation: A Handbook of Techniques. 1999, Oxford: Illustarted Edition editionGoogle Scholar
- Human Microbiome Project Consortium: Structure, function and diversity of the healthy human microbiome. Nature. 2012, 486 (7402): 207-214. 10.1038/nature11234.View ArticleGoogle Scholar
- Ho SK, Perez EE, Rose SL, Coman RM, Lowe AC, Hou W, Ma C, Lawrence RM, Dunn BM, Sleasman JW, Goodenow MM: Genetic determinants in HIV-1 Gag and Env V3 are related to viral response to combination antiretroviral therapy with a protease inhibitor. AIDS. 2009, 23 (13): 1631-1640. 10.1097/QAD.0b013e32832e0599.PubMed CentralView ArticlePubMedGoogle Scholar
- Rozera G, Abbate I, Bruselles A, Vlassi C, D’Offizi G, Narciso P, Chillemi G, Prosperi M, Ippolito G, Capobianchi MR: Massively parallel pyrosequencing highlights minority variants in the HIV-1 env quasispecies deriving from lymphomonocyte sub-populations. Retrovirology. 2009, 6: 15-10.1186/1742-4690-6-15.PubMed CentralView ArticlePubMedGoogle Scholar
- Domingo E, Holland JJ: RNA virus mutations and fitness for survival. Annu Rev Microbiol. 1997, 51: 151-178. 10.1146/annurev.micro.51.1.151.View ArticlePubMedGoogle Scholar
- Eigen M: On the nature of virus quasispecies. Trends Microbiol. 1996, 4 (6): 216-218. 10.1016/0966-842X(96)20011-3.View ArticlePubMedGoogle Scholar
- Lauring AS, Andino R: Quasispecies theory and the behavior of RNA viruses. PLoS Pathog. 2010, 6 (7): e1001005-10.1371/journal.ppat.1001005.PubMed CentralView ArticlePubMedGoogle Scholar
- Paladin FJ, Monzon OT, Tsuchie H, Aplasca MR, Learn GH, Kurimura T: Genetic subtypes of HIV-1 in the Philippines. AIDS. 1998, 12 (3): 291-300. 10.1097/00002030-199803000-00007.View ArticlePubMedGoogle Scholar
- Los Alamos data base. 2012, http://www.hiv.lanl.gov/content/index.
- Redd AD, Collinson-Streng AN, Chatziandreou N, Mullis CE, Laeyendecker O, Martens C, Ricklefs S, Kiwanuka N, Nyein PH, Lutalo T, Grabowski MK, Kong X, Manucci J, Sewankambo N, Wawer MJ, Gray RH, Porcella SF, Fauci AS, Sagar M, Serwadda D, Quinn TC: Previously transmitted HIV-1 strains are preferentially selected during subsequent sexual transmissions. J Infect Dis. 2012, 206 (9): 1433-1442. 10.1093/infdis/jis503.PubMed CentralView ArticlePubMedGoogle Scholar
- Coberley CR, Kohler JJ, Brown JN, Oshier JT, Baker HV, Popp MP, Sleasman JW, Goodenow MM: Impact on genetic networks in human macrophages by a CCR5 strain of human immunodeficiency virus type 1. J Virol. 2004, 78 (21): 11477-11486. 10.1128/JVI.78.21.11477-11486.2004.PubMed CentralView ArticlePubMedGoogle Scholar
- Ghaffari G, Tuttle DL, Briggs D, Burkhardt BR, Bhatt D, Andiman WA, Sleasman JW, Goodenow MM: Complex determinants in human immunodeficiency virus type 1 envelope gp120 mediate CXCR4-dependent infection of macrophages. J Virol. 2005, 79 (21): 13250-13261. 10.1128/JVI.79.21.13250-13261.2005.PubMed CentralView ArticlePubMedGoogle Scholar
- Schmidt HA, Strimmer K, Vingron M, von HA: TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. Bioinformatics. 2002, 18 (3): 502-504. 10.1093/bioinformatics/18.3.502.View ArticlePubMedGoogle Scholar
- Strimmer K, von Haeseler A: Likelihood-mapping: a simple method to visualize phylogenetic content of a sequence alignment. Proc Natl Acad Sci U S A. 1997, 94: 6815-6819. 10.1073/pnas.94.13.6815.PubMed CentralView ArticlePubMedGoogle Scholar
- Xia X, Xie Z, Salemi M, Chen L, Wang Y: An index of substitution saturation and its application. Mol Phylogenet Evol. 2003, 26: 1-7. 10.1016/S1055-7903(02)00326-3.View ArticlePubMedGoogle Scholar
- Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O: New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010, 59 (3): 307-321. 10.1093/sysbio/syq010.View ArticlePubMedGoogle Scholar
- Swofford DSJ: Phylogeny inference based on parsimony and other methods with PAUP*. The Phylogenetic Handbook-a Practical Approach to DNA and Protein Phylogeny. Edited by: Lemey P, Salemi M, Vandamme A-M. 2003, New York: Cambrige University Press, 160-206. 2Google Scholar
- Gray RR, Veras NM, Santos LA, Salemi M: Evolutionary characterization of the West Nile Virus complete genome. Mol Phylogenet Evol. 2010, 56 (1): 195-200. 10.1016/j.ympev.2010.01.019.View ArticlePubMedGoogle Scholar
- Veras NM, Gray RR, Brigido LF, Rodrigues R, Salemi M: High-resolution phylogenetics and phylogeography of human immunodeficiency virus type 1 subtype C epidemic in South America. J Gen Virol. 2011, 92 (Pt 7): 1698-1709.View ArticlePubMedGoogle Scholar
- Yang Z: PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 1997, 13 (5): 555-556.PubMedGoogle Scholar
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