Phylogenetic Analysis of Population-Based and Deep
Sequencing Data to Identify Coevolving Sites in the nef
Gene of HIV-1
Art F.Y. Poon,*,1 Luke C. Swenson,1 Winnie W.Y. Dong,1 Wenjie Deng,2 Sergei L. Kosakovsky Pond,3
Zabrina L. Brumme4 , James I. Mullins,2 Douglas D. Richman,5,6 P. Richard Harrigan,1
and Simon D.W. Frost7
1
British Columbia Centre for Excellence in HIV/AIDS, Vancouver, British Columbia, Canada
Department of Microbiology, University of Washington
3
Department of Medicine, University of California, San Diego
4
Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
5
Department of Pathology, University of California, San Diego
6
San Diego Veteran Affairs Healthcare Systems
7
Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
*Corresponding author: E-mail: [email protected].
Associate editor: Beth Shapiro
2
Rapidly evolving viruses such as HIV-1 display extensive sequence variation in response to host-specific selection, while simultaneously maintaining functions that are critical to replication and infectivity. This apparent conflict between diversifying
and purifying selection may be resolved by an abundance of epistatic interactions such that the same functional requirements
can be met by highly divergent sequences. We investigate this hypothesis by conducting an extensive characterization of sequence variation in the HIV-1 nef gene that encodes a highly variable multifunctional protein. Population-based sequences
were obtained from 686 patients enrolled in the HOMER cohort in British Columbia, Canada, from which the distribution
of nonsynonymous substitutions in the phylogeny was reconstructed by maximum likelihood. We used a phylogenetic comparative method on these data to identify putative epistatic interactions between residues. Two interactions (Y120/Q125
and N157/S169) were chosen to further investigate within-host evolution using HIV-1 RNA extractions from plasma samples
from eight patients. Clonal sequencing confirmed strong linkage between polymorphisms at these sites in every case. We used
massively parallel pyrosequencing (MPP) to reconstruct within-host evolution in these patients. Experimental error associated with MPP was quantified by performing replicates at two different stages of the protocol, which were pooled prior to
analysis to reduce this source of variation. Phylogenetic reconstruction from these data revealed correlated substitutions at
Y120/Q125 or N157/S169 repeated across multiple lineages in every host, indicating convergent within-host evolution shaped
by epistatic interactions.
Key words: coevolution, epistasis, HIV-1, next-generation sequencing, ancestral reconstruction, sequencing error.
Introduction
Many RNA viruses exhibit tremendously high rates of
molecular evolution (Jenkins et al. 2002) that underlie their
facility to outpace the immune responses of their hosts.
How are these viruses able to maintain the functions that
are essential to replication and infectivity in the face of such
extensive divergence in their genome sequences? There is
accumulating experimental (Nijhuis et al. 2001; Poon and
Chao 2005; Poon et al. 2005) and comparative evidence
(Shapiro et al. 2006) that epistatic interactions between different sites of a genome sequence may be sufficiently abundant to provide a resolution of this evolutionary conflict.
Epistasis is the phenomenon in which the contribution of
a given site to one or more phenotypes is dependent on its
genetic context. This context dependence is taken to an extreme under a specific form of epistasis denoted as “compensatory” or “sign” epistasis, in which the combination of
two or more loss-of-function substitutions at different sites
into the same genome can restore function to wild-type levels (Weinreich et al. 2005). The influence of compensatory
epistasis potentially creates a complex many-to-one mapping of sequence variation to phenotype space; in other
words, such interactions can enable the same functions to
be accomplished by highly divergent sequences.
Compensatory epistasis appears to play an important
role in shaping the evolution of HIV-1. For instance, HIV-1
populations rapidly acquire mutations conferring resistance
in the presence of antiretroviral drugs, which are often followed by additional mutations that compensate for the fitness costs incurred by acquiring resistance (Nijhuis et al.
2001). Similarly, escape mutations in HIV-1 that impede
recognition and/or processing of human leukocyte antigen
(HLA) class I–restricted epitopes by the cytotoxic T lymphocyte (CTL)–mediated immune response in exchange for
© The Author 2009. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please
e-mail: [email protected]
Mol. Biol. Evol. 27(4):819–832. 2010 doi:10.1093/molbev/msp289 Advance Access publication December 2, 2009
819
Research article
Abstract
Poon et al. · doi:10.1093/molbev/msp289
reduced viral replicative capacity can induce further compensatory mutations that restore viral fitness (Crawford
et al. 2007; Troyer et al. 2009). Many of these examples
stem from direct observation of sequence evolution over
the course of in vitro passaging or longitudinal samples from
clinical trials. In the absence of a modeling framework, however, we are limited to subjective criteria that identify only
the most unambiguous epistatic interactions and are unable to capitalize on increases in sample size. Hence, the
development of statistical comparative methods to detect
epistatic interactions has flourished alongside the accumulation of extensive sequence data from HIV-1 (Korber et al.
1993; Hoffman et al. 2003; Gilbert et al. 2005; Carlson et al.
2007; Poon et al. 2007; Rhee et al. 2007) and other RNA
viruses as well (Shapiro et al. 2006).
All comparative methods in this context generally rely
on the assumption that correlated evolution among sites
is the product of epistatic interactions. It is also common
practice to disregard the evolutionary relatedness between
sequences for the convenience of applying association test
statistics directly to the sequence alignment (e.g., Hoffman
et al. 2003; Rhee et al. 2007). However, a substantial number of the significantly covariant sets of sites that derive
from this practice may reflect identity by descent, not independently evolved adaptations under the influence of
epistatic interactions (Felsenstein 1973). Phylogenetic comparative methods (PCMs) address this confounding effect
by explicitly modeling the evolutionary history of the extant
sequences (Shapiro et al. 2006; Carlson et al. 2007; Poon et al.
2007). Although PCMs tend to be more computationally demanding, they can also greatly reduce an exceedingly high
false discovery rate when there is substantial phylogenetic
structure in the data (Poon et al. 2007).
A limitation common to both types of comparative
methods is that our ability to detect epistatic interactions is
strongly dependent on the sample size. To date, all studies
of HIV-1 utilizing comparative methods to identify epistatic
interactions have been directed at sequence variation at the
level of the patient population. As a result of the HIV-1
pandemic and routine subtyping and drug resistance genotypic testing, large data sets comprising on the order of
104 sequences have become available through public HIV
sequence repositories (Kuiken et al. 2003). The molecular
evolution of HIV-1 among hosts, however, can be remarkably distinct from its within-host evolution. For instance,
intra-host phylogenies reconstructed from serially sampled
HIV-1 env sequences tend to assume a “ladder-like” shape
that implies an ongoing turnover of sequence variants under immune selection, whereas inter-host phylogenies show
little evidence of a similar mechanism operating in the
patient population (Lemey et al. 2006). Likewise, there is
no guarantee that the epistatic interactions affecting sequence divergence among hosts coincide with the interactions that shape within-host evolution. Whereas selection
by antiretroviral drugs is largely indifferent to host environment, selection by neutralizing antibodies and CTLs is host
specific by definition, and epistatic interactions induced by
the latter may not necessarily emerge at the level of the
820
MBE
patient population. Therefore, we are interested in applying
comparative methods for detecting epistatic interactions
directly to sequence variation at the within-host level. This
has only recently become feasible with the emergence of socalled “next-generation” sequencing (NGS) technologies.
NGS is generally distinguished from conventional
capillary-based (i.e., Sanger) sequencing by its highthroughput capacity for processing potentially millions of
sequence reads in a single run. Current platforms for NGS
are hampered by short sequence read lengths ranging from
35 to 400 bp (Voelkerding et al. 2009). For rapidly evolving
RNA viruses, this limited read length can nonetheless suffice to render an informative sample of sequence diversity
within a narrowly defined portion of the genome, that
is, “deep sequencing.” As a result, the massively parallel
pyrosequencing (MPP) platform distributed by Roche/454
Life Sciences, which offers the longest mean read length
(100–450 bp) at the expense of a reduced throughput
capacity (≈ 105 reads), has been adopted by many investigators in HIV research (Hoffmann et al. 2007; Wang, Ciuffi,
et al. 2007; Wang, Mitsuya, et al. 2007; Rozera et al. 2009),
where NGS is most often used to detect minority variants
in the virus population. Although such data might carry a
sufficiently strong phylogenetic signal to infer within-host
evolutionary processes such as sequence coevolution, this
application has only begun to be pursued (Campbell et al.
2008; Tsibris et al. 2009). Additionally, the reproducibility
and error distribution of NGS has only recently been
assessed on control populations comprising a single HIV-1
clone or a known mixture of clones (Tsibris et al. 2006;
Huse et al. 2007; Wang, Mitsuya, et al. 2007). Even so, there
remain several sources of error (e.g., polymerase chain
reaction [PCR] amplification, alignment uncertainty) to
be quantified in an experimental setting, and there is an
unsettling lack of routine replication in NGS experiments.
In this study, we employ a PCM in concert with MPP
to investigate epistatic interactions in the HIV-1 accessory
protein Nef. HIV-1 Nef enhances virus replication and infectivity via several mechanisms, including downregulating
the expression of cell surface receptors (e.g., CD4, MHC-I)
to facilitate immune evasion and virion release (Roeth and
Collins 2006), interfering with signal transduction pathways
to stimulate T-cell activation and enhance viral replication
(Baur et al. 1994), and reshaping the actin cytoskeleton
(Campbell et al. 2004). Several of these functions are conserved in homologs in other primate lentiviruses despite as
much as 70% amino acid sequence divergence (Munch et al.
2007). Nevertheless, HIV-1 Nef displays extensive genetic
variability both within and among human hosts (Zanotto
et al. 1999; Yusim et al. 2002) that is likely driven by the cellular immune response, given its high density of HLA class
I–restricted epitopes (Yusim et al. 2009), high level of expression at an early stage of the infection cycle (Hewlett
et al. 1991), and high frequency of targeting by CTL in early
disease stages (Lichterfeld et al. 2004). For instance, Noviello
et al. (2007) demonstrated that HIV-1 Nef is able to maintain
two genetically independent functions (downregulation of
CD4 and MHC-I) despite sequence diversification during
MBE
Sequence Coevolution in HIV-1 nef · doi:10.1093/molbev/msp289
sexual transmission. Hence, HIV-1 Nef provides a useful case
study to assess whether the conflict between function and
variability is resolved by compensatory interactions.
To identify targets for deep sequencing analysis, we
analyzed 686 bulk HIV-1 nef sequences sampled from a
patient population in British Columbia, Canada. Two putative epistatic interactions (Y120/Q125 and N157/S169)
identified by PCM were selected for a detailed investigation
of within-host evolution in eight patients. Reproducibility
of deep sequencing was quantified by replicating the experiment at two stages of the protocol: 1) extraction of viral RNA from plasma samples and 2) second-round PCR
amplification of the reverse transcription (RT)–PCR product. We observed substantial variability in allele frequencies stemming from the sample preparation stages of the
parallel pyrosequencing protocol—a source of variance that
cannot be ascertained from sequencing control mixtures
of clonal populations. Nevertheless, phylogenetic inferences
based on these data were robust to experimental error. By
reconstructing within-host phylogenies from these data, we
found that correlated substitutions at putative interacting
sites arose in multiple independent lineages within each
host. These results indicate that a comparative analysis of
sequence variation in the patient population can provide
a convenient and reliable measure of epistatic interactions
shaping within-host evolution. Our analysis also identifies
new functional clusters of residues in HIV-1 Nef and serves
as an informative case study on the practical application
of massively parallel sequencing in the study of RNA virus
evolution.
Materials and Methods
Data Collection
Baseline plasma samples were obtained from 686
antiretroviral-naive subjects, who were enrolled in the
HOMER (HAART Observational Medical Evaluation and
Research) cohort (Hogg et al. 2001) comprising participants
in British Columbia, Canada, predominantly infected by
HIV-1 subtype B and commencing triple combination
therapy. Extraction of HIV-1 RNA from plasma was performed either using an automated QIAGEN viral RNA kit
and BioRobot 9600/9604 workstation or manually using
guanidinium-based buffer followed by isopropanol/ethanol
washes or using automated guanidinium-based methods on
a NucliSens easyMAG (bioMérieux). Full-length sequences
of HIV-1 nef were amplified using nested RT-PCR and
sequenced following the procedure described previously
by Alexander et al. (2001). Further details and GenBank
accession numbers for the bulk sequences can be found in
Brumme et al. (2007).
A total of 85 clonal sequences were obtained from eight
of the plasma samples, which were selected on the basis of mixtures (ambiguous base calls as designated by an
ABI 3730xl DNA Analyzer) present in the corresponding
bulk sequences, thereby indicating within-host polymorphisms. Cloning was performed using a TOPO TA cloning
kit (Invitrogen, Burlington, Ontario, Canada) containing the
PCR 2.1-TOPO vector with chemically competent TOP10F one shot cells, according to the manufacturer’s instructions.
HIV RNA levels for the eight plasma samples were determined using a Roche Amplicor version 1.5 assay (Roche
Diagnostics, Mississauga, Ontario, Canada).
Detecting Sequence Covariation
Automated alignment of HIV-1 nef bulk sequences was carried out using ClustalW and manually refined using Se-Al
(Andrew Rambaut, http://tree.bio.ed.ac.uk/software). Unless noted otherwise, all analyses described in this section
were carried out in the phylogenetic software package HyPhy (Kosakovsky Pond et al. 2005). We reconstructed a
phylogeny from this alignment by a neighbor-joining (NJ)
method with Tamura–Nei (Tamura and Nei 1993) distances and rate variation across sites under a one-parameter
gamma distribution (α = 0.5). Branch length estimates were
refined by fitting a reversible model of nucleotide substitution rates (encoded by PAUP* model specification string
012232), which was determined by an automated model
selection procedure as described in Kosakovsky Pond and
Frost (2005a). Ancestral sequences at internal nodes of this
phylogeny were reconstructed by fitting a codon substitution model (MG94; Muse and Gaut 1994) crossed with the
nucleotide substitution model (012232) using joint maximum likelihood (ML) methods (Pupko et al. 2000). Nonsynonymous substitutions were mapped to specific branches of
the phylogeny wherever ancestral or extant sequences occupying adjacent nodes of the phylogeny encoded different
residues at a given codon site (Kosakovsky Pond and Frost
2005b).
If nonsynonymous substitutions affecting two or more
sites in the gene sequence occurred in the same branches
of the phylogeny significantly more often than expected
by chance, then we would infer an interaction between
those sites. We converted the entire map of substitutions
to the phylogeny into a binary-valued matrix in which
each row corresponded to a branch in the phylogeny,
each column corresponded to a codon site in the alignment, and a “1” entry denoted the assignment of a nonsynonymous substitution to the corresponding site and
branch (Poon et al. 2007). We discarded highly conserved
sites at which nonsynonymous substitutions occurred at
fewer than 1% of the branches in the tree (retaining n =
101 codon sites) to minimize the computational cost of
the Bayesian graphical model analysis (described below).
Additionally, we performed a similar analysis on the distribution of synonymous substitutions, so that we could address the potentially confounding influence of functional
motifs in the nef nucleotide sequence, for example, 3 long
terminal repeat (LTR) region (see Ngandu et al. 2008, and
references therein). To identify correlations in the occurrence of nonsynonymous mixtures among sites, we used
a custom Python script to convert the entire alignment
of nef bulk sequences into another binary-valued matrix
in which “1”s encoded the presence of a nonsynonymous
mixture and each row corresponded to a sequence in the
alignment.
821
MBE
Poon et al. · doi:10.1093/molbev/msp289
Both the substitutions- and mixtures-based matrices
were analyzed separately using Bayesian graphical models
(BGMs), following the procedure described in Poon et al.
(2007). A BGM is a compact graphical representation of a
joint probability distribution such that each random variable is represented by a node (Pearl 1988). Using BGMs
to analyze coevolving sites confers the advantages of 1)
generating a natural graphical representation of interactions; 2) minimization of the number of model parameters,
which makes tractable the analysis of large systems given
a limited number of observations, and 3) the potential to
distinguish indirect correlations from direct causative relationships between sites. Results from BGM analysis were
corroborated against results from a similar analysis of this
data based on a phylogenetic correction for pairwise correlations between sites (Carlson et al. 2008).
Deep Sequencing
To further investigate covariant polymorphisms within patients detected as mixtures, we selected eight plasma samples for MPP using a Genome Sequencer FLX System
(Roche/454 Life Sciences). We performed three replicate
second-round PCR amplifications on the products of the initial RT-PCR product for each isolate, using primers spanning
the HXB2 reference sequence coordinates 9121–9521. Each
second-round PCR product was bar coded using a unique
ten-nucleotide sequence tag for multiplexed pyrosequencing, that is, parallel tagged sequencing (PTS) (Meyer et al.
2008). Performing deep sequencing in triplicate enabled us
to quantify the variability in the sequence composition of
sample populations that was introduced by the secondround PCR and MPP steps. This procedure would not completely eliminate experimental bias, however, as it fails to
address any variability introduced by the preliminary sample extraction and RT-PCR steps. Consequently, we took the
additional measure of replicating the first-round RT-PCR
and second-round PCR steps in triplicate, using a second extraction of viral RNA from the frozen plasma samples, followed by pyrosequencing of the pooled amplicons. Due to
the limited availability of plasma samples, we were only able
to carry out this level of replication for three out of eight
patients.
The MPP data generated by the Genome Sequencer
FLX base calling algorithm were partitioned by patient sequence tags and by forward/reverse primers and aligned
using a set of custom HyPhy batch language and Python
scripts (available upon request). For each data set, a subsample of 100 sequences were aligned pairwise to the
HXB2 reference sequence using an implementation of the
Gotoh algorithm (Myers and Miller 1988) in HyPhy to
generate a patient-specific consensus sequence (J. Archer,
M. Lewis, and D.L. Robertson, presented at the 15th International Workshop on HIV Dynamics & Evolution, Santa Fe,
NM, 27–30 April 2008). Subsequently, all sequences were
aligned pairwise to this consensus sequence. The resulting alignments were post-processed with a Python script
and manually refined in Seaview (Galtier et al. 1996) or
Se-Al (A. Rambaut, http://tree.bio.ed.ac.uk/software/seal).
822
Columns in refined alignments whose nonprefix/suffix gap
frequencies exceeded a threshold of 99% (e.g., singleton
nucleotide insertions) were stripped out. The final MPP
alignments were validated by manually aligning the MPP
consensus sequence against bulk and clonal sequences from
the same plasma sample.
To characterize sequence evolution within the eight
patients, we analyzed each alignment of MPP reads using
phylogenetic methods. Because phylogenetic methods generally rely on the pattern of sequence divergence to infer
rates of molecular evolution, they do not make use of allele frequency information, that is, the number of instances
of a given sequence variant in a population sample. Moreover, the proportion of pyrosequencing reads that are redundant tends to be considerable, reflecting both the real
composition of the viral population as well as the limited
mean read length of the NGS platform. Each alignment was
therefore reduced to a minimal set of longest nonredundant sequences using a custom Python script by matching sequences that were identical over any length of the
longest nonredundant sequence and recording the corresponding number of matches. Sequences containing mixtures or partial sequences were partitioned equally over
matching nonredundant sequence variants as fractional
increments to each total.
A phylogeny was reconstructed from each alignment by
the NJ method with Tamura–Nei distances (Tamura and
Nei 1993). Branch length estimates in the NJ phylogeny
were refined by fitting a full parameterization of the timereversible nucleotide substitution model (REV; Tavaré 1986)
by ML. The resulting phylogeny was used to fit a Muse–Gaut
codon substitution model (Muse and Gaut 1994) crossed
with the REV nucleotide substitution model by ML. These
analyses were carried out on a high-performance computing Linux cluster (comprising 260 processors) using a
message passing interface–enabled version of HyPhy. ML
parameter estimates were used to reconstruct ancestral sequences and map nonsynonymous substitution events to
individual branches of each within-host phylogeny. Correlated substitutions at potentially interacting sites were
visualized by color-annotating a radial tree in PostScript
using a custom HyPhy batch language script. All scripts
used to perform this analysis are available on the Web at
http://www.hyphy.org/pubs/Nef.
Results
Genetic Variation in Nef
We observed extensive genetic variation in HIV-1 nef
bulk sequences isolated from 686 patients enrolled in the
HOMER cohort. The mean Shannon entropy per site was
approximately 0.75, commensurate with the overall level of
amino acid variability across HIV-1 M group nef sequences
(Yusim et al. 2002). In-frame deletions affected 226 (33%)
of the sequences, clustering predominantly in three regions
spanning codon sites 8–12, 48–50, and 151–158. Using a
conservative Bonferroni correction for multiple comparisons (α = 2.43 × 10−4 ), we found a significant excess of
MBE
Sequence Coevolution in HIV-1 nef · doi:10.1093/molbev/msp289
nonsynonymous substitutions above the expected values
(i.e., scaled dN − dS values ranging from 0.82 to 4.2) at 18
different codon sites, namely, V10, V11, P14, A15, A23, V33,
G83, K92, K94, E98, I101, I133, P150, V153, K178, V182, R188,
and L198 (alignment consensus residues and coordinates;
see supplementary table S1, Supplementary Material
online).
Sequencing mixtures (i.e., ambiguous bases due to multiple peaks in the sequence chromatogram) were abundant,
averaging 7.0 mixtures per bulk sequence, suggesting that
genetic variation within patients was commensurate with
the extensive variation in the patient population. Any given
mixture was equally likely to be nonsynonymous (i.e., the
ambiguous codon could encode more than one amino acid)
as synonymous (odds 2450 : 2385 = 1.03). Considering
that the neutral expectation for these odds is closer to 3
(392 : 134 = 2.92 for two-way mixtures given the universal
genetic code), we interpreted the observed odds as a significant depletion of nonsynonymous mixtures, consistent with
purifying selection as a dominant force shaping within-host
polymorphism. The difference in the site-specific frequencies of nonsynonymous and synonymous mixtures, normalized, respectively, by their expected values based on the
genetic code and empirical base frequencies, was significantly correlated with the quantity dN − dS representing diversifying selection at the level of the host population
(Spearman’s ρ = 0.803, P < 10−15 ). This correlation implied that the distribution of nonsynonymous mixtures in
our sequence alignment was predominantly the outcome
of site-specific selection, rather than being an artifact of
sequencing error.
Among-Host Sequence Coevolution
We used BGM methods to infer a network of interactions
between 63 different residues in Nef from the distribution of
nonsynonymous substitutions reconstructed from the sequence alignment (fig. 1). A network is a set of nodes that
each represent a random variable, in our case being the presence or absence of a nonsynonymous substitution at a given
codon site and branch of the phylogeny (Poon et al. 2007).
Connections between nodes, termed “edges,” are used to
represent interactions, that is, a conditional dependence
between variables. Figure 1 depicts a model-averaged network (obtained by sampling from the posterior probability
distribution using a Bayesian Monte Carlo Markov chain
method) comprising 40 edges whose marginal posterior
probabilities exceeded a threshold of 0.95. The majority
(88%) of edges in the network connected residues located in the same domain of the Nef protein (namely, anchor versus core domains) with only five edges connecting
residues located in different domains (P14/Y203, V16/E151,
R19/M173, A50/H116, and T51/Q170; fig. 1). These interdomain edges are consistent with previous observations that
anchor residues that interact with the core tend to occur either in close proximity to the protein N-terminus or within
the interval bounded by residues 49 and 60 (Groesch and
Freire 2007).
RNA virus genomes experience selection on the nucleotide level owing to functional stem-loop structures and
binding motifs. To determine whether coevolution at the
level of the nucleotide sequence (e.g., compensatory substitutions in RNA secondary structures) may have generated
potentially confounding edges between codon sites in this
network, we performed a similar analysis on the distribution
of synonymous substitutions. We obtained the following
significant edges, that is, with marginal posterior probabilities exceeding a cutoff of 0.9: T51/K204, L76/R77, D86/L110,
D108/S187, V148/G119/I133, Q125/P136, D174/F191/A190,
H192/M194, R196/L198, and H199/P200. None of these
edges was present in the network estimated from nonsynonymous substitutions. However, four of these edges connected codon sites within or adjacent to the core negative
response element (overlapping codons A190 to V194) of the
3 LTR region, which suggests that this analysis of synonymous variation was able to detect functional interactions
at the nucleotide level. In addition, we determined whether
the residue–residue interactions presented here coincided
with any of the putative base-pairing interactions recently
reported in a complete structural model of the HIV-1 RNA
genome (Watts et al. 2009). We found no overlap between
these sets of interactions, lending further support to the
conjecture that our results were exempt from confounding
by selection at the nucleotide level.
To select a subset of putative interactions in HIV-1 Nef
that potentially carried over to the within-host level for further investigation by deep sequencing, we applied the same
BGM procedure to the distribution of nonsynonymous
mixtures in the alignment of bulk sequences. A significant
positive correlation in mixtures suggests that the sites coevolve at the within-host level. We identified ten positively
correlated pairs of sites (supplementary table S2, Supplementary Material online), of which four coincided with
edges in figure 1 (K92/E93, Y120/Q125, N157/S169, and
R188/H192). The residue pairs Y120/Q125 and N157/S169
were chosen for further analysis—Y120 and Q125 reside
in several CTL epitopes (Yusim et al. 2009) and the substitutions Y120F and Q125H have been associated with
escape from CTL recognition in vitro (Culmann et al. 1991),
whereas N157 and S169 have been implicated in the rate of
progression to AIDS (Kirchhoff et al. 1999). Moreover, these
pairs fell within the expected range of sequence coverage
for Roche/454 MPP. We selected eight baseline plasma samples in which nonsynonymous mixtures occurred at both
sites for one of the two pairs (denoted P3, P5, and P6 for
Y120/Q125; and P1, P2, P4, P7, and P8 for N157/S169) for
deep sequencing analysis.
Deep Sequencing Analysis
We carried out three replicate second-round PCR amplifications on the RT-PCR products obtained from the
eight plasma samples and ran PTS on a GS-FLX Sequencer
(Roche/454 Life Sciences) for all 24 amplification products.
We also replicated the full experimental protocol for three
patient isolates (P2, P4, and P8), namely, a second extraction of RNA from the plasma sample, followed by RT-PCR
823
Poon et al. · doi:10.1093/molbev/msp289
MBE
FIG. 1. Schematic approximating the anchor and core structures of the HIV-1 Nef protein (thick black lines), annotated with residue–residue
interactions from a BGM analysis of a phylogenetic reconstruction of nonsynonymous substitutions. Amino acid sites implicated in epistatic
interactions (rectangular nodes) are labeled with the alignment consensus residue and position. Thirty-eight sites that were not implicated in
interactions (G3, V10, G12, A15, M20, A23, R35, E38, K39, I43, S46, A49, A53, C55, A56, E62, E63, E65, R71, I101, Y102, Q107, P129, V148, P150,
V153, A156, N161, C163, M168, P176, K178, V182, K184, R196, L198, E201, and D205) are omitted for clarity. Putative interactions are depicted as
connections (solid or dashed lines) between nodes, labeled with marginal posterior probabilities ×100, using a cutoff value of 0.95. Dashed lines
connect sites separated by more than 15 residues in the protein sequence, though the respective amino acids are frequently clustered in the folded
protein structure (e.g., 8.3Å from E98 to F143; 13.1Å from A50 to H116). Thirty-nine conditionally independent sites were omitted for clarity. Node
names in italics are used to indicate long-distance interactions (>100 residue separation). Interactions supported by significant correlations in bulk
sequencing mixtures are indicated by double lines.
amplification in triplicate, second-round PCR amplification
in triplicate, and MPP of the pooled amplification product.
The mean viral load per sample was approximately 2.5 × 105
copies/ml. Individual viral load measurements and mean
forward read depths for the eight baseline plasma samples
are presented in table 1. As the input reverse transcribed
RNA copy number was unknown, there was a significant
possibility that some nucleic acids were resampled by the se824
quencing process. However, because we restricted our analysis of reproducibility (see below) to the upper portion of
the frequency distribution (0.1–70%), resampling was likely
a relatively minor confounding factor.
The average read depth per product was n = 8, 167 sequences (range 5,543–12,180) with an average read length
of 247 nt (range 201–341 nt). Forward reads covered Nef
codon sites 109–192, and the reverse-primed reads covered
MBE
Sequence Coevolution in HIV-1 nef · doi:10.1093/molbev/msp289
Table 1. Characterization of Eight HIV-1 Populations Subjected to MPP Analysis.
Patient
P1
P2
P3
P4
P5
P6
P7
P8
Viral Load
300,000
130,000
750,000
130,000
170,000
200,000
270,000
76,000
Mean Read
Depth
2,797
4,306
2,717
3,181
3,232
3,927
4,377
2,849
Codon Sites
157,169
157,169
120,125
157,169
120,125
120,125
157,169
157,169
COT
Genotype
S,N
N,S
F,H
T,S
F,H
F,H
N,S
T,N
Root
Genotype
S,N
N,S
Y,Q
N,S
F,H
Y,D
N,S
T,N
Correlated
Substitutions
2(2,0)
2(1,1)
2(0,1)
3(2,1)
9(3,6)
6(1,2)
7(5,1)
4(2,2)
Viral load is measured in copies per milliliter of baseline plasma. Mean forward read depth was averaged across three second-round PCR replicates. The number of
correlated substitutions in each phylogeny is followed by the number of instances that the first substitution was mapped at the first or second site (enclosed in
parentheses). If both substitutions were mapped to the same branch, then substitution order could not inferred.
sites 164–206 and approximately 120 nucleotides further
into the 3 LTR as well. Because the reverse reads failed
to extend into the region of interest, we directed our
analysis specifically on the forward reads. Forward reads
comprised about 43.7% of sample depth, averaging approximately 3400 forward reads per plasma sample. As anticipated for this sequencing technology, single nucleotide
insertions occurred abundantly in forward read alignments;
on average, 22% of alignment columns corresponded to insertions present in less than 5 % of sequences and often a
single sequence only. Even after removing spurious gaps attributable to errors in alignment and the expected baseline
indel rate of the MPP platform (≈ 1%; Huse et al. 2007;
Wang, Mitsuya, et al. 2007), an average of 5.5 columns with
low-frequency (< 5% of sequences) insertions remained
(patient-specific means ranged from 1.0 to 9.1 insertions).
The insertions did not discernibly coincide with homopolymeric regions in the alignment. When we mapped the affected alignment columns to an NJ tree reconstructed from
the remainder of the alignment, we found that the putative insertions failed to cluster in the tree. This outcome
suggested that the insertions represented experimental artifacts rather than defective nef sequences that can nevertheless be propagated in viable HIV-1 lineages (Salvi et al.
1998). We omitted these columns from alignments in subsequent analyses such that the resulting sequences remained
in-frame over their entire length.
To quantitatively assess the reproducibility of frequency
estimates by MPP, we calculated the variance-to-mean ratio for each sequence variant shared across replicates. This
dispersion statistic was more robust than the coefficient of
variation to differences in expected values (supplementary
text S1, Supplementary Material online), making it the more
appropriate choice to apply across the entire frequency
distribution of sequence variants to yield a meaningful summary statistic. The average variance-to-mean ratio from
second-round amplification and onward was 3.7 × 10−4 . For
a given sequence variant with expected frequency x, the predicted standard deviation (σ̂ ) in frequency estimates can be
obtained from this ratio by multiplying by x and taking the
square root; for example, a minority variant expected to be
observed in 1% of MPP reads has σ̂ = 0.19%. The average variance-to-mean ratio for fully replicated experiments
was 7.6 × 10−3 ; for example, frequency estimates of a 1 %
variant would have σ̂ = 0.87%. An extended discussion
of this reproducibility analysis for MPP data is provided as
supplementary text S1, Supplementary Material online.
Because phylogenetic analyses do not incorporate frequency information (i.e., identical sequences are not phylogenetically informative) and are therefore robust to
uncertainty in frequency estimates (except for zero versus
nonzero frequencies), we pooled these replicates for each
of the eight baseline plasma samples to maximize our sample of sequence variation. To summarize this variability, we
present the ten most abundant amino acid sequences for
each plasma sample in figures 2 and 3. On average, the most
frequent amino acid sequence from a patient was present
at a frequency of 19.7% (range 10.0–30.5%). We confirmed
that the consensus sequence from each pooled alignment of
MPP reads matched the corresponding bulk sequence. Additionally, we obtained 85 clonal full-length nef sequences
in total from these baseline plasma samples (median 11
clones per sample). Alignments of these clonal sequences
against the MPP consensus nucleotide sequence and of the
five most frequent amino acid sequences from MPP against
the patient bulk sequence are provided in supplementary
text S2 and S3 (Supplementary Material online), respectively. Both the bulk sequence and the composition of clonal
sequences corresponded well with the MPP data. We also
observed that minority variants represented by mixtures
in bulk sequences were present in the corresponding MPP
data at frequencies ranging from 20 % to 50%. For example,
the bulk sequence from patient P1 contained four mixtures
within the interval covered by MPP, namely, R(A/G)375,
R470, R506, and M(A/C)531. At each position, the frequency
of the subdominant (i.e., second-most abundant) allele was
42.2% G375; 43.1% G470; 42.8% G506; and 23.8% C531.
MPP also revealed six other polymorphic nucleotide sites
with subdominant allele frequencies between 10 % and 25%
within this patient population, consistent with 25 % as a
likely limit for detection of minority variants by mixtures in
bulk sequences in our experiment.
Within-Host Sequence Coevolution
In the pooled MPP data, linkage disequilibria between
amino acid site pairs N157/S169 (fig. 2) and Y120/Q125
(fig. 3) was largely consistent with coevolution due to
residue–residue interactions, that is, substitutions from
825
Poon et al. · doi:10.1093/molbev/msp289
MBE
FIG. 3. Alignments of the ten most frequent amino acid sequences
from three patients with polymorphisms at HIV-1 Nef residues Y120
and Q125 (highlighted columns). Alignments are truncated to display
variable residues only. Processing and interpretation are identical to
figure 2.
FIG. 2. Alignments of the ten most frequent amino acid sequences
from five patients with polymorphisms at HIV-1 Nef residues N157
and S169 (highlighted columns). Alignments are truncated to display
variable residues only. Frequencies within each patient data set (combining three replicate RT-PCRs and Roche/454 pyrosequencing runs)
are listed to the right of each sequence. A dot “.” indicates that the
residue is identical to that of the most frequent sequence. An “X”
represents a premature stop codon. During translation, nucleotide
sequences containing an ambiguous character (“N”) or a frame-shift
deletion were discarded. The frequency of any incomplete sequence
(with gap prefix or suffix) was partitioned equally among all sequences
containing the sequence fragment, which was then discarded. Frequencies shown here were rounded to the nearest integer.
consensus residues occurred predominantly in cis-linkage.
On the other hand, intermediate variants containing a mixture of consensus and nonconsensus residues were found
at substantial frequencies (≈ 10–20%) in patient plasma
samples P3 (F120/Q125) and P4 (T157/S169). Because the
complements to these intermediate variants (Y120/H125
and N157/N169, respectively) occurred at very low frequencies (<0.1%) in the corresponding samples, overall linkage
disequilibrium remained high nonetheless. However, linkage disequilibrium does not provide sufficient evidence of
coevolution at these sites within patients as such patterns
may be confounded by the phylogeny, that is, when infrequent substitutions at independently evolving sites are by
chance transmitted jointly or in mutual exclusion to large
numbers of descendents (Felsenstein 1973). A stronger case
can be made for sequence coevolution within patients if
correlated substitutions at the sites occur multiply in independent lineages of the phylogeny.
To assess the extent of sequence coevolution within
patients, we reconstructed the distribution of nonsynony826
mous substitutions throughout each within-patient phylogeny from the respective alignments of MPP nucleotide
sequences using an ML procedure (Kosakovsky Pond and
Frost 2005b). The phylogenies were color-annotated with
respect to residue combinations at the target sites with reference to the ancestral reconstruction at the central node
of the radial tree (i.e., center of the tree [COT]; Nickle et al.
2003) to provide a visual representation of within-host evolution. Annotated phylogenies for patients P1, P5, and P8
are shown in figure 4, whereas the remaining phylogenies
are provided as supplementary figure S4, Supplementary
Material online. We found that substitution maps to the
phylogeny were correlated for the target sites such that a
substitution at one site was quickly followed by another
substitution at the second site. More importantly, we observed multiple instances of correlated substitutions in phylogenetically independent clades in all patient phylogenies, that is, convergent evolution. Overall, the number of
independent lineages carrying correlated substitutions
ranged among patients from 2 to 9 with a mean of 4.6
(table 1). Note that these estimates were conservative because all sites in each alignment of pyrosequencing reads
were used to reconstruct the phylogeny. Otherwise, convergent evolution would tend to collapse independent lineages
into a single clade, leading to an underestimate of the number of instances of correlated substitution.
While the ancestral sequence inferred at the COT provided a convenient reference point, we were cautious about
interpreting this sequence as reconstructing the root ancestor as these phylogenies were unrooted. To illustrate,
we rooted each within-host phylogeny using a bulk sequence from another patient that was closely related in
the among-host phylogeny. Ancestral state reconstructions
at these roots are reported alongside reconstructions at
the COT from the corresponding unrooted trees for each
patient in table 1. Reconstructions at the root matched
Sequence Coevolution in HIV-1 nef · doi:10.1093/molbev/msp289
MBE
FIG. 4. Radial phylogenies reconstructed by ML from nonredundant sequences obtained from MPP of HIV-1 Nef from three patient baseline plasma
samples. Branches are colored with respect to substitutions away from the ancestral genotype reconstructed at the inferred root (which coincides
with the genotype at the COT): grey to indicate no substitutions; red to indicate a substitution at one site only; and blue to indicate substitutions
at both sites. Terminal branches corresponding
√ to more than
√ one identical sequence are labeled with open triangles scaled in proportion to the
number of identical sequences, N (height ∝ 5N , base ∝ 3 5N ). A legend with triangles corresponding to N = {2, 10, 100, 500} is provided in
the figure to facilitate interpretation.
the COT for five out of eight patients, whereas both sites
were unmatched in two patients (P3 and P6). For patient
P4, the COT reconstruction comprised one consensus and
one nonconsensus residue (T157/S169), a combination that
was present at an unusually high frequency in P4 when
compared with other patient samples (fig. 2). In contrast,
the consensus genotype (N157/S169) was reconstructed at
the inferred root in P4, suggesting that the COT genotype
corresponded to a prolonged intermediate stage in the evolution of HIV-1 in this patient. We surmised that the placement of roots in these phylogenies was accompanied with
a high level of uncertainty due to the nature of these data.
Therefore, we have avoided drawing any conclusions from
our results that would be sensitive to uncertainty in rooting
of within-host phylogenies.
Because we considered two target sites in each patient,
there was always more than one mutational pathway to
evolve from one residue combination to another. Previous
investigators have observed that HIV-1 evolution can be
constrained to certain mutational pathways; for example,
substitutions at reverse transcriptase T215 tend to precede
those at M41 in the evolution of resistance to zidovudine
(Kellam et al. 1994). We examined the substitution maps to
determine whether HIV-1 evolution was significantly biased
toward one mutational pathway over another. We found no
evidence of a significant bias: site 120 acquired a substitution
before site 125 in 4 out of 13 cases, and site 157 before site
169 in 14 of 20 cases (table 1; in nearly all cases, the inferred
order of substitutions was unambiguous en route to a terminal sequence). In most cases, evolution proceeded through
one of two intermediates to an end point with paired consensus or nonconsensus residues. For example, in patient
P1, the mutational pathways were comprised as follows:
SN → (NN or SS) → NS (omitting site coordinates for compact notation). Although several other intermediate genotypes emerged in P1 (e.g., GN, AN, SD; fig. 5), those lineages
never acquired a substitution at the second site. However,
we found alternative end points in other patients. For
example, the following pathways were both reconstructed
in patients P4 and P7: 1) NS → (NN or SS) →SN and2) NS
→ (TS or NN) → TN.
To further characterize sequence evolution within patients at the target sites, we calculated the total branch
lengths in each patient phylogeny with respect to residue
combinations at the target sites. These results are summarized in figure 5, where each circle corresponds to a
827
MBE
Poon et al. · doi:10.1093/molbev/msp289
disproportionate selective advantage to the double-mutant
genotype. In patient P3, however, representation of the intermediate genotype FQ was slightly greater than the consensus genotype inferred at the root (YQ). Similarly, the total branch length associated with intermediate genotype TS
in P4 was slightly greater than the predominating nonconsensus genotype SN. These discrepancies indicate that coevolution at these sites by rapid transitions through intermediate genotypes do not necessarily apply to every host,
possibly due to either infrequent host-specific factors or
higher-order interactions involving additional sites outside
of nef.
Discussion
FIG. 5. Circle plots summarizing proportions of tree length by genotype. Each circle corresponds to a unique combination of residues
at the target sites (see labels), with its area proportional to the total
evolutionary time occupied by that genotype in the within-host phylogeny. Circles were arranged in each plot with respect to the minimum height of the genotype in the tree (y axis) and order that the
genotype was first encountered during preorder traversal of the tree
(x axis). The genotype reconstructed at the inferred root of each tree is
indicated in bold text. Evolutionary time was calculated in units of expected substitutions by summing the respective branch lengths in the
phylogeny. Confidence intervals were estimated by resampling ancestral reconstructions from each posterior probability distribution but
were too small to appear in this figure. The majority of evolutionary
time is occupied by the ancestral genotype at the root (bolded circle)
or a single predominant genotype in which both sites have substitutions from the ancestor. In contrast, nearly all genotypes with only one
substitution away from the ancestor occupy relatively little evolutionary time, including those intermediate of the majority genotypes.
given residue combination with its area proportional to the
branch length total. Branch lengths in the phylogenies are
scaled in units of the expected number of substitutions per
site, which is confounded with chronological time. Put another way, these quantities provide a crude estimate of the
proportion of time that any given HIV-1 lineage has assumed a particular configuration of residues, throughout
the entire history of sequence evolution in that patient.
Confidence intervals on the estimates of branch length totals, generated by resampling ancestral sequences from the
posterior probability distribution, were too small to be distinguished in these plots. We observed that, in most cases,
the total branch lengths of genotypes comprising one consensus and one nonconsensus residue (i.e., intermediate
genotypes) were substantially less than either the consensus or the predominating nonconsensus genotypes (fig. 5).
This trend suggested that correlated substitutions occurred
rapidly within patients, either through selection against
intermediate genotypes or with synergistic epistasis accompanying a shift in host environments that conferred a
828
NGS technologies, such as MPP, are a promising development that offer a fine-scale resolution of the genetic composition of a virus population, that is, deep sequencing.
The clinical utility of such methods has been demonstrated with respect to the detection of drug-resistant variants in HIV-1 populations at frequencies well below the
detection threshold of conventional sequencing (Tsibris
et al. 2006; Hoffmann et al. 2007; Wang, Mitsuya, et al.
2007). Because many RNA viruses such as HIV-1 evolve so
rapidly, deep sequencing might also provide valuable insight into the parameters of the molecular evolution of virus
populations. We have demonstrated that MPP can be used
to reconstruct within-host evolution of HIV-1, specifically
to characterize the correlated substitution process affecting sites in the accessory protein Nef. The distribution
of multiple instances of correlated substitutions at the
sites Y120/Q125 and N157/S169 along several independent lineages in the phylogenies provided strong evidence
that putative residue–residue interactions estimated from
population-based (bulk) sequence variation also shaped
variation within hosts. This result is encouraging because
deep sequencing via NGS is not yet capable of spanning the
entire length of most genes; instead, we show that information contained in conventional full-length bulk sequences
(namely, mixtures, phylogenetic analysis of correlated substitutions) can be used to pinpoint the most informative
gene intervals for deep sequencing.
However, NGS is also fraught with commensurately novel
sources of experimental error whose parameters have only
recently been assessed in the context of deep sequencing
of virus populations. Deep sequencing attempts to measure
the genetic composition of a population, which comprises
a set of nucleotide sequences variants and the number of
instances of each variant. Previous studies have focused on
one specific source of error, namely, the surfeit of single nucleotide indels introduced by MPP (approximately 1 for every 100 nucleotides) that can interfere with the estimation
of variant frequencies (Huse et al. 2007; Wang, Mitsuya, et
al. 2007). This must be considered alongside several other
sources of error, some of which are not specific to NGS but
compounded by NGS-specific error:
(i) Sampling error. Sequence variant frequencies in the
plasma sample may not be representative of the
Sequence Coevolution in HIV-1 nef · doi:10.1093/molbev/msp289
true frequencies in the virus population, due to random variation or compartmentalization. Extraction of
HIV-1 RNA is another potential source of sampling
error.
(ii) RT and amplification. These processes contribute two
different types of error. First, misincorporation of nucleotides during primer extension may either introduce
a new spurious variant or misclassify the sequence as
another variant. Second, stochastic variability among
templates in the waiting time until a doubling event
can skew the frequency distribution, particularly during the initial amplification cycles (Peccoud and Jacob
1996). Variability in the observation of mixtures in HIV1 pol bulk sequences has been attributed largely to this
source (Galli et al. 2003). Recombination at the RT-PCR
step may also constitute a significant source of error.
(iii) Sequencing error. The majority (≈ 60% − 90%) of
sequencing errors introduced by MPP are insertions
and deletions, which tend to be associated with homopolymeric regions (Huse et al. 2007; Wang, Mitsuya,
et al. 2007). Some of these errors are attributed to
optical signal bleed between the picoliter wells that
separate emulsion PCR products derived from single
nucleic acids (Margulies et al. 2005). (Other NGS platforms such as Solexa or SOLiD are reported to yield
more accurate homopolymer run lengths.) Because
of this high error rate, investigators have proposed
null models that define allele frequency significance
cutoffs, ranging in complexity from a Poisson distribution (Wang, Mitsuya, et al. 2007) to population
genetic models (Johnson and Slatkin 2008).
In addition, the frequency of indel and mismatch errors
can be exacerbated by poor read quality, though this
can be prescreened to some extent by automated algorithms (Brockman et al. 2008).
(iv) Alignment error. An alignment is a hypothesis of evolutionary homology relating sites in nucleotide or protein sequences. In the presence of a high indel rate
due to pyrosequencing error, accurate alignment of
reads has become a highly active area of bioinformatics research. Genetic analyses based on frequency
information (e.g., characterization of “mutation spectra”; Wang, Mitsuya, et al. 2007) are especially prone to
misclassification of sequence variants due to alignment
error, such as nucleotides that toggle on either side of
a gap. This source of error is arguably the least understood, but it can have severe consequences on largescale sequence analyses (Wong et al. 2008). We found
that results based on phylogenetic analyses (e.g., number of correlated substitutions, branch length distribution of genotypes) were quite robust to uncertainty in
sequence alignment, whereas results based on variant
frequencies were more sensitive.
A potentially powerful approach to deal directly with
alignment error would be to integrate alignments
over the posterior probability distribution. Bayesian
methods such as MCMC sampling make joint estimation of the alignment and phylogeny attainable
MBE
(Redelings and Suchard 2005), but such approaches remain computationally costly and impractical for deep
sequencing data.
Due to its current prohibitive cost, MPP experiments are
rarely replicated in practice. Instead, sequencing error rates
have been estimated by pyrosequencing control populations (Huse et al. 2007; Wang, Mitsuya, et al. 2007); however, this procedure is unable to quantify other sources of
error such as PCR amplification variance. To address this
we have performed replicate pyrosequencing experiments
at two different steps of the protocol. We found that the RT
and amplification steps contributed substantial error variation to frequency estimates, which is consistent with previous findings in the context of mixtures (Galli et al. 2003).
Additionally, replication of the sample extraction step
revealed disproportionately greater reductions in reproducibility of frequency estimates, relative to contributions
from RT-PCR and second-round PCR amplifications (supplementary text S1 and fig. S3, Supplementary Material online). Such discrepancies have critical implications for the
clinical use of MPP and other NGS platforms to screen
for minority HIV-1 variants. We therefore propose that a
minimum of two replicates from the RNA extraction step
become standard procedure in the application of NGS technology to genotypic resistance testing.
The MPP experiments presented here provide an important source of validation for phylogenetic comparative
methods that predict epistatic interactions between protein residues. Specifically, we observed patterns of correlated substitution within patients that was consistent with
an interaction between the residue pairs Y120/Q125 and
N157/S169. Our analysis of HIV-1 nef bulk sequences from
686 patients predicted 38 other interactions (fig. 1), many
of which are consistent with prior observations in the experimental HIV-1 literature. First, Nef-induced activation
of p21-activated kinase 2 (PAK-2) is one of the most intensively investigated functions of Nef. PAK-2 participates
in the regulation of cellular processes that reshape the cytoskeleton and modulate gene transcription, apoptosis, and
more. O’Neill et al. (2006) used site-directed mutagenesis to demonstrate that the subtype B consensus residues
L85, H89, and F191 were collectively involved in PAK-2 activation. Our finding that these sites coevolve is consistent
with this observation (fig. 1). Second, posttranslational ubiquitination of multiple lysine residues in Nef has been implicated in the Nef-induced downregulation of CD4 (Jin
et al. 2008). We observed interactions among many of these
lysine-enriched sites (K82, K92, K94, and K105; fig. 1), and
K144, on which this putative function is conditioned, is
highly conserved in our study population. Alternatively, K92
and K94 coincide with the polypurine tract (where DNA
synthesis of the plus strand is selectively initiated) so that
this putative lysine cluster may be confounded by selection
on the nucleotide sequence. However, synonymous sites
within these codons were completely conserved in the study
population. Finally, several interactions described here are
consistent with CTL epitope polymorphisms identified by
829
MBE
Poon et al. · doi:10.1093/molbev/msp289
Brumme et al. (2007) as significantly associated with HLA
class I variation. The residues and corresponding HLA allele
(in brackets) are as follows: D28/V33 (A11), A50/T51/H116
(B58), G83/L85 (C07), and R188/M194 (A31).
Though the results from our comparative analysis of
bulk HIV-1 nef sequences are consistent with experimental
data, we caution that the phylogenetic method employed
here does not account for recombination. Indeed, not one
of the currently available comparative methods for inferring epistatic interactions from sequence data explicitly addresses recombination, and the effect of recombination on
such estimates has not yet been quantified. This may be
problematic because the HIV-1 genome undergoes frequent
recombination as reverse transcriptase switches between
genomic templates during replication, resulting in one of
the highest rates of recombination found in nature (Zhuang
et al. 2002). Recombination has the effect of partitioning a
sequence alignment into two or more segments with discordant phylogenies (Posada and Crandall 2002). Because our
analysis takes substitutions mapped to the joint tree (i.e., a
tree estimated from the entire alignment) as data, recombination will induce some degree of error almost surely. However, the net effect of recombination is to break up statistical
associations between loci (i.e., restore linkage equilibrium);
on average, recombination should make sequence coevolution more difficult to detect. It is therefore less likely that recombination has generated spurious associations between
sites in our data. Nevertheless, incorporating recombination
into phylogenetic comparative methods remains a critical
objective for future research.
In summary, deep sequencing can provide valuable information about the molecular evolution of RNA virus
proteins at the within-host level. Our phylogenetic analysis of deep sequencing data targeting HIV-1 nef from a
selection of baseline plasma samples substantiated the existence of epistatic interactions shaping within-host evolution, which were predicted from an analysis of among-host
sequence variation. More importantly, our application of
NGS revealed intriguing patterns of convergent evolution in
sequence within hosts. In this study, we have developed analytical methods to exploit the information-rich data generated by NGS of an RNA virus beyond the detection of
minority variants. Future work will be directed on developing models to infer parameters of molecular evolution (e.g.,
directional selection) and population dynamics from NGS
data.
Supplementary Material
Supplementary tables S1 and S2, figures S1–S4, and text S1–
S3 are available at Molecular Biology and Evolution online
(http://www.mbe.oxfordjournals.org/).
Acknowledgments
This work was supported by grants AI69432 (ACTG),
AI043638 (AIEDRP), MH62512 (HNRC), MH083552 (Clade),
AI077304 (Dual Infection), AI36214 (the Viral Pathogenesis Core of the UCSD Center for AIDS Research), AI047745
830
(Dynamics), AI57167, and AI74621 (Transmission) and
AI27757 (Computational Biology Core of the University
of Washington Center for AIDS Research) from the
National Institutes of Health and the California HIV/AIDS
Research Program RN07-SD-702. S.D.W.F. and S.L.K.P. received support from University of California San Diego Centers for AIDS Research/National Institute of Allergy and
Infectious Disease developmental awards AI36214. A.F.Y.P.
is supported by Canadian Institutes of Health Research
Fellowships Award in HIV/AIDS Research (200802HFE).
S.D.W.F. is supported in part by a Royal Society Wolfson Research Merit Award. P.R.H. is supported by a Canadian Institutes of Health Research/GlaxoSmithKline research chair
in clinical virology. Z.L.B. is supported by a CIHR New Investigator Award. Our computing cluster is funded in part by
National Science Foundation award 0714991.
References
Alexander CS, Dong W, Chan K, Jahnke N, O’Shaughnessy MV,
Mo T, Piaseczny MA, Montaner JS, Harrigan PR. 2001. HIV protease and reverse transcriptase variation and therapy outcome
in antiretroviral-naive individuals from a large North American
cohort. AIDS 15:601–607.
Baur AS, Sawai ET, Dazin P, Fantl WJ, Cheng-Mayer C, Peterlin BM.
1994. HIV-1 Nef leads to inhibition or activation of T cells depending on its intracellular localization. Immunity 1:373–384.
Brockman W, Alvarez P, Young S, Garber M, Giannoukos G, Lee WL,
Russ C, Lander ES, Nusbaum C, Jaffe DB. 2008. Quality scores and
SNP detection in sequencing-by-synthesis systems. Genome Res.
18:763–770.
Brumme ZL, Brumme CJ, Heckerman D, et al. (14 co-authors). 2007. Evidence of differential HLA class I-mediated viral evolution in functional and accessory/regulatory genes of HIV-1. PLoS Pathog. 3:e94.
Campbell EM, Nunez R, Hope TJ. 2004. Disruption of the actin cytoskeleton can complement the ability of Nef to enhance human
immunodeficiency virus type 1 infectivity. J Virol. 78:5745–5755.
Campbell PJ, Pleasance ED, Stephens PJ, Dicks E, Rance R, Goodhead I,
Follows GA, Green AR, Futreal PA, Stratton MR. 2008. Subclonal
phylogenetic structures in cancer revealed by ultra-deep sequencing. Proc Natl Acad Sci USA. 105:13081–13086.
Carlson J, Kadie C, Mallal S, Heckerman D. 2007. Leveraging hierarchical population structure in discrete association studies. PLoS ONE.
2:e591.
Carlson JM, Brumme ZL, Rousseau CM, et al. (8 co-authors). 2008.
Phylogenetic dependency networks: inferring patterns of CTL escape and codon covariation in HIV-1 Gag. PLoS Comput Biol.
4:e1000225.
Crawford H, Prado JG, Leslie A, et al. (16 co-authors). 2007. Compensatory mutation partially restores fitness and delays reversion
of escape mutation within the immunodominant HLA-B*5703restricted Gag epitope in chronic human immunodeficiency virus
type 1 infection. J Virol. 81:8346–8351.
Culmann B, Gomard E, Kiény MP, Guy B, Dreyfus F, Saimot AG, Sereni
D, Sicard D, Lévy JP. 1991. Six epitopes reacting with human cytotoxic CD8+ T cells in the central region of the HIV-1 NEF protein.
J Immunol. 146:1560–1565.
Felsenstein J. 1973. Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Syst Zool. 22:240–249.
Galli RA, Sattha B, Wynhoven B, O’Shaughnessy MV, Harrigan PR.
2003. Sources and magnitude of intralaboratory variability in a
sequence-based genotypic assay for human immunodeficiency
virus type 1 drug resistance. J Clin Microbiol. 41:2900–2907.
Sequence Coevolution in HIV-1 nef · doi:10.1093/molbev/msp289
Galtier N, Gouy M, Gautier C. 1996. SEAVIEW and PHYLO WIN: two
graphic tools for sequence alignment and molecular phylogeny.
Comput Appl Biosci. 12:543–548.
Gilbert P, Novitsky V, Essex M. 2005. Covariability of selected amino
acid positions for HIV type 1 subtypes C and B. AIDS Res Hum
Retroviruses. 21:1016–1030.
Groesch TD, Freire E. 2007. Characterization of intramolecular interactions of HIV-1 accessory protein Nef by differential scanning
calorimetry. Biophys Chem. 126:36–42.
Hewlett IK, Geyer SJ, Hawthorne CA, Ruta M, Epstein JS. 1991. Kinetics
of early HIV-1 gene expression in infected H9 cells assessed by PCR.
Oncogene 6:491–493.
Hoffman NG, Schiffer CA, Swanstrom R. 2003. Covariation of amino
acid positions in HIV-1 protease. Virology 314:536–548.
Hoffmann C, Minkah N, Leipzig J, Wang G, Arens MQ, Tebas P,
Bushman FD. 2007. DNA bar coding and pyrosequencing to identify rare HIV drug resistance mutations. Nucleic Acids Res. 35:e91.
Hogg RS, Yip B, Chan KJ, Wood E, Craib KJ, O’Shaughnessy MV,
Montaner JS. 2001. Rates of disease progression by baseline CD4
cell count and viral load after initiating triple-drug therapy. JAMA
286:2568–2577.
Huse SM, Huber JA, Morrison HG, Sogin ML, Welch DM. 2007. Accuracy and quality of massively parallel DNA pyrosequencing.
Genome Biol. 8:R143.
Jenkins GM, Rambaut A, Pybus OG, Holmes EC. 2002. Rates of molecular evolution in RNA viruses: a quantitative phylogenetic analysis.
J Mol Evol. 54:156–165.
Jin Y-J, Cai CY, Zhang X, Burakoff SJ. 2008. Lysine 144, a ubiquitin
attachment site in HIV-1 Nef, is required for Nef-mediated CD4
down-regulation. J Immunol. 180:7878–7886.
Johnson PLF, Slatkin M. 2008. Accounting for bias from sequencing error in population genetic estimates. Mol Biol Evol. 25:
199–206.
Kellam P, Boucher CA, Tijnagel JM, Larder BA. 1994. Zidovudine treatment results in the selection of human immunodeficiency virus
type 1 variants whose genotypes confer increasing levels of drug
resistance. J Gen Virol. 75(Pt 2):341–351.
Kirchhoff F, Easterbrook PJ, Douglas N, Troop M, Greenough TC,
Weber J, Carl S, Sullivan JL, Daniels RS. 1999. Sequence variations
in human immunodeficiency virus type 1 Nef are associated with
different stages of disease. J Virol. 73:5497–5508.
Korber BT, Farber RM, Wolpert DH, Lapedes AS. 1993. Covariation of
mutations in the V3 loop of human immunodeficiency virus type
1 envelope protein: an information theoretic analysis. Proc Natl
Acad Sci USA. 90:7176–7180.
Kosakovsky Pond SL, Frost SDW. 2005a. A simple hierarchical approach to modeling distributions of substitution rates. Mol Biol
Evol. 22:223–234.
Kosakovsky Pond SL, Frost SDW. 2005b. Not so different after all: a
comparison of methods for detecting amino acid sites under selection. Mol Biol Evol. 22:1208–1222.
Kosakovsky Pond SL, Frost SDW, Muse SV. 2005. HyPhy: hypothesis
testing using phylogenies. Bioinformatics 21:676–679.
Kuiken C, Korber B, Shafer RW. 2003. HIV sequence databases. AIDS
Rev. 5:52–61.
Lemey P, Rambaut A, Pybus OG. 2006. HIV evolutionary dynamics
within and among hosts. AIDS Rev. 8:125–140.
Lichterfeld M, Yu XG, Cohen D, et al. (14 co-authors). 2004. HIV-1 Nef
is preferentially recognized by CD8 T cells in primary HIV-1 infection despite a relatively high degree of genetic diversity. AIDS 18:
1383–1392.
Margulies M, Egholm M, Altman WE, et al. (53 co-authors). 2005.
Genome sequencing in microfabricated high-density picolitre reactors. Nature 437:376–380.
Meyer M, Stenzel U, Hofreiter M. 2008. Parallel tagged sequencing on
the 454 platform. Nat Protoc. 3:267–278.
MBE
Munch J, Rajan D, Schindler M, et al. (11 co-authors). 2007.
Nef-mediated enhancement of virion infectivity and stimulation of viral replication are fundamental properties of primate
lentiviruses. J Virol. 81:13852–13864.
Muse SV, Gaut BS. 1994. A likelihood approach for comparing
synonymous and nonsynonymous nucleotide substitution rates,
with application to the chloroplast genome. Mol Biol Evol. 11:
715–724.
Myers EW, Miller W. 1988. Optimal alignments in linear space.
Comput Appl Biosci. 4:11–17.
Ngandu NK, Scheffler K, Moore P, Woodman Z, Martin D, Seoighe C.
2008. Extensive purifying selection acting on synonymous sites in
HIV-1 Group M sequences. Virol J. 5:160.
Nickle DC, Jensen MA, Gottlieb GS, Shriner D, Learn GH, Rodrigo AG,
Mullins JI. 2003. Consensus and ancestral state HIV vaccines. Science 299:1515–1518; author reply 1515–1518.
Nijhuis M, Deeks S, Boucher C. 2001. Implications of antiretroviral
resistance on viral fitness. Curr Opin Infect Dis. 14:23–28.
Noviello CM, Pond SLK, Lewis MJ, Richman DD, Pillai SK, Yang OO,
Little SJ, Smith DM, Guatelli JC. 2007. Maintenance of Nefmediated modulation of major histocompatibility complex class
I and CD4 after sexual transmission of human immunodeficiency
virus type 1. J Virol. 81:4776–4786.
O’Neill E, Kuo LS, Krisko JF, Tomchick DR, Garcia JV, Foster JL. 2006.
Dynamic evolution of the human immunodeficiency virus type 1
pathogenic factor, Nef. J Virol. 80:1311–1320.
Pearl J. 1988. Probabilistic reasoning in intelligent systems: networks
of plausible inference. San Mateo (CA): Morgan Kaufmann
Publishers. 552 p.
Peccoud J, Jacob C. 1996. Theoretical uncertainty of measurements
using quantitative polymerase chain reaction. Biophys J. 71:
101–108.
Poon A, Chao L. 2005. The rate of compensatory mutation in the DNA
bacteriophage ϕX174. Genetics 170:989–999.
Poon A, Davis BH, Chao L. 2005. The coupon collector and the suppressor mutation: estimating the number of compensatory mutations
by maximum likelihood. Genetics 170:1323–1332.
Poon AFY, Lewis FI, Kosakovsky Pond SL, Frost SDW. 2007. An
evolutionary-network model reveals stratified interactions in
the V3 loop of the HIV-1 envelope. PLoS Comput Biol. 3:
e231.
Posada D, Crandall KA. 2002. The effect of recombination on the accuracy of phylogeny estimation. J Mol Evol. 54:396–402.
Pupko T, Pe’er I, Shamir R, Graur D. 2000. A fast algorithm for joint
reconstruction of ancestral amino acid sequences. Mol Biol Evol.
17:890–896.
Redelings BD, Suchard MA. 2005. Joint Bayesian estimation of alignment and phylogeny. Syst Biol. 54:401–418.
Rhee S-Y, Liu TF, Holmes SP, Shafer RW. 2007. HIV-1 subtype B protease and reverse transcriptase amino acid covariation. PLoS Comput Biol. 3:e87.
Roeth JF, Collins KL. 2006. Human immunodeficiency virus type 1 Nef:
adapting to intracellular trafficking pathways. Microbiol Mol Biol
Rev. 70:548–563.
Rozera G, Abbate I, Bruselles A, Vlassi C, D’Offizi G, Narciso P,
Chillemi G, Prosperi M, Ippolito G, Capobianchi MR. 2009.
Massively parallel pyrosequencing highlights minority variants in
the HIV-1 env quasispecies deriving from lymphomonocyte subpopulations. Retrovirology 6:15.
Salvi R, Garbuglia AR, Di Caro A, Pulciani S, Montella F, Benedetto A.
1998. Grossly defective nef gene sequences in a human immunodeficiency virus type 1-seropositive long-term nonprogressor. J
Virol. 72:3646–3657.
Shapiro B, Rambaut A, Pybus OG, Holmes EC. 2006. A phylogenetic
method for detecting positive epistasis in gene sequences and its
application to RNA virus evolution. Mol Biol Evol. 23:1724–1730.
831
Poon et al. · doi:10.1093/molbev/msp289
Tamura K, Nei M. 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans
and chimpanzees. Mol Biol Evol. 10:512–526.
Tavaré S. 1986. Some probabilistic and statistical problems in the analysis of DNA sequences. Lect Math Life Sci. 17:57–86.
Troyer RM, McNevin J, Liu Y, et al. (11 co-authors). 2009. Variable fitness impact of HIV-1 escape mutations to cytotoxic T lymphocyte
(CTL) response. PLoS Pathog. 5:e1000365.
Tsibris AMN, Korber B, Arnaout R, et al. (14 co-authors). 2009. Quantitative deep sequencing reveals dynamic HIV-1 escape and large
population shifts during CCR5 antagonist therapy in vivo. PLoS
One. 4:e5683.
Tsibris AMN, Russ C, Lee W, Paredes R, Arnaout R, Honan T, Cahill P,
Nusbaum C, Kuritzkes DR. 2006. Detection and quantification of
minority HIV-1 env V3 loop sequences by ultra-deep sequencing:
preliminary results. Antivir Ther. 11:S74.
Voelkerding KV, Dames SA, Durtschi JD. 2009. Next-generation sequencing: from basic research to diagnostics. Clin Chem. 55:
641–658.
Wang C, Mitsuya Y, Gharizadeh B, Ronaghi M, Shafer RW. 2007.
Characterization of mutation spectra with ultra-deep pyrosequencing: application to HIV-1 drug resistance. Genome Res. 17:
1195–1201.
Wang GP, Ciuffi A, Leipzig J, Berry CC, Bushman FD. 2007. HIV integration site selection: analysis by massively parallel pyrosequencing
832
MBE
reveals association with epigenetic modifications. Genome Res.
17:1186–1194.
Watts JM, Dang KK, Gorelick RJ, Leonard CW, Bess JWJ, Swanstrom R,
Burch CL, Weeks KM. 2009. Architecture and secondary structure
of an entire HIV-1 RNA genome. Nature 460:711–716.
Weinreich DM, Watson RA, Chao L. 2005. Perspective: Sign epistasis and genetic constraint on evolutionary trajectories. Evolution
59:1165–1174.
Wong KM, Suchard MA, Huelsenbeck JP. 2008. Alignment uncertainty
and genomic analysis. Science 319:473–476.
Yusim K, Kesmir C, Gaschen B, Addo MM, Altfeld M, Brunak S,
Chigaev A, Detours V, Korber BT. 2002. Clustering patterns of cytotoxic T-lymphocyte epitopes in human immunodeficiency virus
type 1 (HIV-1) proteins reveal imprints of immune evasion on HIV1 global variation. J Virol. 76:8757–8768.
Yusim K, Korber BTM, Brander C, Haynes BF, Koup R, Moore JP,
Walker BD, Watkins DI. 2009. HIV Molecular Immunology 2009.
Los Alamos (NM): Los Alamos National Laboratory, Theoretical
Biology and Biophysics.
Zanotto PM, Kallas EG, de Souza RF, Holmes EC. 1999. Genealogical
evidence for positive selection in the nef gene of HIV-1. Genetics
153:1077–1089.
Zhuang J, Jetzt AE, Sun G, Yu H, Klarmann G, Ron Y, Preston
BD, Dougherty JP. 2002. Human immunodeficiency virus type
1 recombination: rate, fidelity, and putative hot spots. J Virol.
76:11273–11282.