- Open Access
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.
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