Daily Sampling of an HIV-1 Patient with Slowly
Progressing Disease Displays Persistence of Multiple env
Subpopulations Consistent with Neutrality
Helena Skar1,2,3., Ryan N. Gutenkunst4., Karin Wilbe Ramsay1,2, Annette Alaeus5, Jan Albert1,2, Thomas
1Department of Virology, Swedish Institute for Infectious Disease Control, Solna, Sweden, 2Department of Microbiology, Tumor and Cell Biology, Karolinska Institute,
Stockholm, Sweden, 3Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America, 4Department of Molecular
and Cellular Biology, University of Arizona, Tucson, Arizona, United States of America, 5Department of Medicine, Karolinska Institute, Stockholm, Sweden
The molecular evolution of HIV-1 is characterized by frequent substitutions, indels and recombination events. In addition, a
HIV-1 population may adapt through frequency changes of its variants. To reveal such population dynamics we analyzed
HIV-1 subpopulation frequencies in an untreated patient with stable, low plasma HIV-1 RNA levels and close to normal CD4+
T-cell levels. The patient was intensively sampled during a 32-day period as well as approximately 1.5 years before and after
this period (days 2664, 1, 2, 3, 11, 18, 25, 32 and 522). 77 sequences of HIV-1 env (approximately 3100 nucleotides) were
obtained from plasma by limiting dilution with 7–11 sequences per time point, except day 2664. Phylogenetic analysis
using maximum likelihood methods showed that the sequences clustered in six distinct subpopulations. We devised a
method that took into account the relatively coarse sampling of the population. Data from days 1 through 32 were
consistent with constant within-patient subpopulation frequencies. However, over longer time periods, i.e. between days
1…32 and 522, there were significant changes in subpopulation frequencies, which were consistent with evolutionarily
neutral fluctuations. We found no clear signal of natural selection within the subpopulations over the study period, but
positive selection was evident on the long branches that connected the subpopulations, which corresponds to .3 years as
the subpopulations already were established when we started the study. Thus, selective forces may have been involved
when the subpopulations were established. Genetic drift within subpopulations caused by de novo substitutions could be
resolved after approximately one month. Overall, we conclude that subpopulation frequencies within this patient changed
significantly over a time period of 1.5 years, but that this does not imply directional or balancing selection. We show that the
short-term evolution we study here is likely representative for many patients of slow and normal disease progression.
Citation: Skar H, Gutenkunst RN, Wilbe Ramsay K, Alaeus A, Albert J, et al. (2011) Daily Sampling of an HIV-1 Patient with Slowly Progressing Disease Displays
Persistence of Multiple env Subpopulations Consistent with Neutrality. PLoS ONE 6(8): e21747. doi:10.1371/journal.pone.0021747
Editor: Fabrizio Mammano, INSERM, France
Received April 4, 2011; Accepted June 6, 2011; Published August 2, 2011
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This work was supported by grants from the National Institutes of Health (NIH) [grant 1R01AI087520-01A1], the Swedish Research Council, and the
Swedish International Development Cooperation Agency [grant no. SWE-2006-018]. The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
. These authors contributed equally to this work.
The HIV-1 envelope gene (env) displays the largest genetic
diversity in the HIV-1 genome. The evolutionary rate (nucleotide
substitution rate) of env is affected by the strength of the pressure of
the immune system [1,2] so that both the immune pressure and
the evolutionary rate are higher during the chronic, asymptomatic
phase than during end-stage disease. Similarly, the immune
pressure in long-term non-progressors lasts longer and is often
stronger than in typical patients. Thus, HIV-1 genetic evolution in
env during the chronic disease stage has been characterized by
positive selection for escape mutants due to continuous immune
surveillance [3,4,5,6]. However, other studies have found HIV-1
evolution during chronic infection to be consistent with a neutral
model of evolution, characterized by small effective population
sizes (Ne) strongly influenced by random genetic drift [7,8].
Whether the mutation process will be deterministic or stochastic is
generally believed to be dependent on the population size.
Deterministic models assume an infinite population size, which
given the large amount of HIV-1 particles produced daily in an
infected individual (1010virions/day) is not unreasonable .
However, it has been proposed that the Ne of HIV-1 during
[7,10,11,12], which would suggest that stochastic processes could
influence HIV-1 evolution. To date, a few models have tried to
unify the estimated small Ne and the strong positive selection
believed to act on HIV during chronic infection. A meta-
population model, where a large collection of small subpopulations
is subject to frequent migration, extinction, and recolonization,
was shown to agree with the low effective population sizes seen in
chronic HIV infection . Another example is a combination of
both directional and neutral forces acting on the HIV population,
where random genetic drift of neutral mutations predominates
combined with brief episodes of directional selection . A
orders ofmagnitude lower
PLoS ONE | www.plosone.org1August 2011 | Volume 6 | Issue 8 | e21747
combination of the two, where the meta-population model and
selective sweeps both are factors that act together to reduce the
intra-host effective population size of HIV-1 has been proposed to
be the most likely explanation of the reduced Ne. Thus, it is
still unclear how HIV diversity is affected by selection in an
infected individual, and furthermore on which time scale selection
Here we compare short-term (days, weeks, months) and long-
term (years) HIV-1 evolution in a treatment naı ¨ve, asymptomatic
patient with low plasma HIV-1 RNA levels (viral load) and
fluctuating, often close to normal CD4+ T-lymphocyte (CD4)
counts. In patients like this the immune system generally puts a
strong pressure on the virus for a longer time than in typical
patients that, in the absence of antiretroviral drugs, develop AIDS
quicker. We find that multiple distinct subpopulations persist over
years, but that their frequencies fluctuate over time. The
fluctuations during the time period of days to months showed no
significant signature of variable selection across sequence sites, and
the fluctuations were consistent with a neutral model of evolution.
Hence, we find no need for balancing selection to explain the
persistence of the subpopulations over these time intervals.
However, over the period of years, we could detect a signal of
positive selection, especially at potential N-linked glycosylation
sites (PNGS), which may have shaped the subpopulation structure.
Finally, we show that it is important to correctly handle
subpopulation fluctuations when using genetic distances to
estimate the number of de novo mutations.
Seventy-seven individual virus sequences of approximately 3100
nucleotides covering vpu, env and the first half of nef were analyzed.
The sequences were sampled by limiting dilution from plasma
samples obtained at 9 different time points spanning a period of 3
years (Table 1). The limiting dilution sequencing methodology (aka.
SGA and SGS) applied here ensures that PCR and sequencing
artifacts are virtually absent in the sequences [14,15]. The majority
of sequences were sampled during a time period of 32 days, where
the first time point was called day 1. In addition, two samples were
drawn approximately 1.5 years before (day 2664) and 1.5 years
after (day 522) the main sampling period. At each time point 7 to 11
sequences were generated with exception for the earliest time point
from which only 3 sequences could be amplified. As this patient was
a slow progressor with low virus load it was difficult to obtain
(at day 18), whereas all other sequences were unique.
In total, 18unique deleterious mutations were observed,including
9 nucleotide substitutions that caused stop codons and 9 that caused
frame shifts. Because deleterious mutations are unlikely to survive to
the next generation, this suggests a minimum rate of 7.6761025
deleterious substitutions per site per generation, i.e., in the same
order of magnitude as other point mutations and recombination
occur [16,17,18]. In addition to point mutations, sequence 2664.2
had two large deletions, one of 149 nucleotides (nts) in the beginning
of gp120 and another of 435 nts in the end of gp41. Sequence 1.10
had two large deletions of 54 and 60 nts in the middle of gp120, and
522.1 had a large deletion of 48 nts in the middle of gp120. In
thus resulted in amino acid insertions or deletions.
Putative recombinants were identified using the PHI-NNet test.
Two sequences (s4.2664.3 and s5.522.10) were classified as
putative recombinants within subpopulations and six sequences
were identified as putative recombinants with ancestors derived
from at least two subpopulations (s2. 2664.2, s4. 2664.1, s4.18.7,
s4.32.9, s4.522.9, s5.1.7) (Figure S1). If these sequences were
removed no recombination signal remained in the dataset
according to the c-AIC criteria in a GARD Single Breakpoint
analysis. To confirm that the identified putative recombinants
were robustly identified, we performed 100 iterations of removal of
8 random sequences. None of these iterations rendered the dataset
free from recombination signal according to the PHI-NNet test.
The general time reversible model with variable rates among
sites and a proportion of invariable sites (GTR+G+I) was the best
substitution model for our data according to a Modeltest analysis.
This model was used to infer a maximum likelihood (ML) tree of
the HIV population (Figure 1). The tree displayed six phylogenetic
clades, designated as subpopulations s1 through s6, which were all
supported by ML bootstrap values 61–100%. Independent of the
inferred tree, and thus less affected by any remaining recombi-
nation signal, Hudson’s population subdivision test supported all
subpopulations except s2 at p[K*s]#0.0005 (s2 p[K*s]=0.0668). A
majority of the subpopulations (4 of 5) persisted over the entire
study period, if we excluded day 2664 that was insufficiently
sampled. Thus, at the last time point (day 522), representatives of
subpopulations s3, s4, s5 and s6 were still present.
Subpopulation selection pressure detection is time scale-
To test if the different subpopulations were under different
potential selection pressures, we investigated dN/dS ratios on all
sites in the sequences and on all branches in the phylogeny. Four
dN/dS categories (0, 0.42, 1.24, 10000) were found to best explain
the data according to the c-AIC criterion and the GAbranch
model available in Hyphy to test lineage specific selection on
branches (Figure S2). A majority (79%) of the branches in the tree
fell into dN/dS categories 0.42 and 1.24 and of these 72%
suggested positive selection, but there was no clear pattern of
where in the tree they occurred. The deep branches that
connected the subpopulations displayed selection in either
direction, i.e., 0.42#dN/dS#1.24. Branches displaying either no
synonymous or non-synonymous mutations (dN/dS categories 0
and 10000) occurred exclusively within the subpopulations, where
the total number of mutations on most of the branches was very
In agreement with the branch analyses, codon models could not
identify any sites under selection within the subpopulations,
suggesting neutral evolution over the time of this study (days
1…522). Furthermore, there was clear evidence of variable
selection pressure over sites when we analyzed all subpopulations
together in a single phylogenetic tree (p,0.01, likelihood ratio tests
with M0 vs. M3: df=4, and M1a vs. M2a: df=2). This indicates
that individual sites may have been under selection when the
subpopulations were established, i.e., over a time much longer
than 522 days as the subpopulations already existed and were
defined by relatively long branches at day 1 (see further below for
an evaluation of how much branches grew over the study period).
Interestingly, potential N-linked glycosylation sites (PNGS) were
significantly overrepresented among positively selected sites in
analyses with the variable selection model M3 (p,0.001, Chi-
square test). Further, while both amino acid substitutions and
PNGS replacements correlated well with positive selection
strength, the response to positive selection was stronger on
branches separating the subpopulations than on branches within
subpopulations (Figure S3).
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Overall, these results suggest that natural selection has little or
no impact over short time periods (#1.5 years), but over long
time periods (.3 years) positively selected sites, especially those
that involve PNGS, may contribute to the subpopulation
Subpopulation frequency fluctuations
Figure 2 shows the count at which the different subpopulations
were observed at each time point. We were interested in
understanding the persistence of subpopulations over time. Could
these experimental data be expected under a neutral process, or
would the data be better explained by selection, and in particular
We first asked whether the experimental data provided
evidence that the frequencies of the different subpopulations
were changing within the patient. To do so, we used a x2statistic
to assess whether the frequencies observed at each day J were
consistent with the frequencies observed for the prior days 1…J-
1. To account for the relatively few samples per time point we
accounted for the number of sequences per sample (to limit
stochastic sampling effects) and pooled days together when no
frequency differences were observed (to increase the power of our
analysis). Hence, statistical significance was assessed by simulating
both the multinomial sampling of day J’s observation, and the
inference of the within-patient frequencies for days 1…J-1. The
resulting p-values for constant within-patient subpopulation
frequencies are shown in Figure 2. From this analysis, we
observed no statistically significant changes in within-patient
subpopulation frequencies over the first 32 days, but at day 522
the frequency fluctuations became
(p,0.05). As detailed in Supporting Information, repeated
analyses that excluded the putative recombinant sequences
yielded similar results (Table S1), although the fluctuations at
day 522 had a p-value of 0.064.
Subpopulation frequency fluctuations are consistent
with neutral drift
Under a neutral model, the Ne controls the strength of
fluctuations in within-patient subpopulation frequencies. Large
Ne results in small fluctuations, while small Ne results in large
fluctuations. Eventually, if no new subpopulations arise (as was
observed in days 1…522), under a neutral model one subpopu-
lation would eventually take over the entire population, eliminat-
ing all subpopulation diversity. Thus, we now ask whether the
observed diversity at day 522 is consistent with a neutral model,
given realistic values for Ne.
Since no significant frequency changes occurred during days
1…32, we pooled those sequences together (n=64) to derive more
accurate subpopulation frequencies (Table 2). Given these inferred
frequencies, we compared expected and observed subpopulation
frequencies on day 522. We noted a number of potentially unlikely
events under a neutral model. Some of these events indicated small
frequency fluctuations, and thus large Ne. One was that the
observed frequency of subpopulation s6 on day 522 was 5, exactly
the expected value. Also, we still observed subpopulations s3 and
s4 on day 522, at frequencies similar to that expected from
constant within-patient frequencies. Additionally, we observed 4
subpopulations present on day 522, indicating that not much
diversity had been lost. Thus, we asked which values of Neare large
enough to be consistent with these observations. On the other
hand, other aspects of the data suggested large frequency
fluctuations. In particular, subpopulation s1 was not observed on
day 522, while our expectation was 2 observations. Hence, we also
asked which values of Neare small enough to be consistent with
this observation. Finally, subpopulation s5 was observed at much
higher frequency than expected at day 522. For small Ne, we would
have expected subpopulation s5 to go extinct, whereas for very
large Neit would have been unlikely to rise to high frequency. Thus
we also asked whether any value of Nemakes the observed count of
Table 1. Sequence data.
clones RNA load
19832 0.0370.0365375310.05158.2744 0.036 56.0526 1.34E-07
3712200.0330.0334874870.03837.9 5590.03844.0565 2.28E-02
118563 0.0240.0243583580.025123620.0232.8337 2.40E-07
25 114500.0330.033492492 0.03521.85120.03628.05262.18E-02
32 116000.0340.0334944820.03632.95320.04142.8 6083.86E-02
STD1.5 264.6 0.0070.010103143 0.01124.6162 0.00925.11391.29E-02
aMean Pairwise Distance, as measured by PAUP* using a GTR substitution model.
bGenetic diversity (substitutions/site).
cExponential growth rate.
dEffective population size determined from h=2Nem with m=3.461025substitutions site21generation21.
fNot analyzed because of the small sample size.
gRecombination rate, C/m, where C is the rate of recombination per inter-site link per generation, and m is the substitution rate per site per generation.
Neutral Subpopulation Fluctuations in HIV-1
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subpopulation s5 plausible. Nearly identical results were observed
when putative recombinants were removed (Table S2). Again,
these calculations accounted for the stochastic effects of the
sampling size at day 522.
Independently, the genetic diversity (h) and Newere estimated
using three methods (Table 1). The results were similar and
unaffected by the exclusion of putative recombinants. The
estimates based on Fluctuate showed that h ranged from 0.015
to 0.051 substitutions/site during the study period corresponding
to?N Ne=512 with 2s range 6162 (pink region in Figure 3).
Figure 3 plots the likelihood versus Ne of the scenarios
considered above, under a neutral model. While we investigated
a large range of possible Ne’s (range 5–50,000), all the aspects of
the data we have tested are not significantly unlikely under a
neutral scenario within the Nerange consistent with our other
analyses (Table 1). In particular, all the likelihoods for these
individual aspects of the data are larger than 0.05 for Ne<800.
Thus, we cannot reject a neutral model for these data, even though
some uncertainty may remain because of the low likelihood
(p,0.1) of observing $3 taxa of subpopulation s5 at day 522. The
modeling results were robust to whether potential recombinants
were included or excluded (Figure S4).
These results were consistent with classical tests (Tajima’s D,
and Fu and Li’s D* [19,20,21]) that showed no significant
deviation from neutrality when the whole dataset was analyzed.
Subpopulation frequency fluctuations may affect the
observed evolutionary rate
Because the subpopulations were present at different frequen-
cies over time we were interested in the potential impact of such
fluctuations on the measured evolutionary rate. Clearly, the
apparent substitution rate varied greatly over time (Figure S5).
Thus, naively measuring the genetic difference between time
points may mislead the estimation of the de novo substitution rate.
However, the fluctuations may be another mechanism that HIV-1
uses to adapt and evolve its population structure. Hence, to
accurately estimate the substitution rate one must take the
phylogeny into account.
We used a Bayesian coalescent method to infer the de novo
substitution rate within each subpopulation. To account for the
frequency variation of each subpopulation we used the Bayesian
skyline demographic model, which allows Neto vary over time in a
non-parametric way. The evolutionary rate was inferred as a
hyper-parameter using separate, independent trees to describe
each of the subpopulations. A relaxed clock model was used to
infer a hyper-parameter with individual distributions for each
subpopulation. The mean estimated within-subpopulation substi-
tution rate was 2.3361023substitutions site21year21, with a 95%
highest posterior density (HPD) interval of 0.94–3.7461023
substitutions site21year21. In agreement with our frequency
analysis above (Figure 2), no significant deviation from a constant
Necould be observed as the Bayesian skyline could contain a
constant Newithin the 95% HPD.
The genetic divergence (nucleotide substitution rate) was further
analyzed in subpopulation s6, which constituted the largest group
of sequences (Figure 4). The mean pairwise distance (MPD)
between clones sampled at the same time, i.e. the population
diversity (0 days), was then compared to the divergence at later
sampling times. Interestingly, we found that the MPD of sequences
separated by about one month’s interval (and longer) differed
significantly from the intra-sample diversity (p,0.01, Wilcoxon
rank sum test), but no significant divergence was seen in shorter
time intervals. Hence, this HIV-1 subpopulation had moved
significantly in sequence space after about a month.
HIV-1 evolves by introducing mutations (substitutions, indels,
recombination) through a ‘‘sloppy’’ replication mechanism, mainly
due to the unfaithful replication by the viral reverse transcriptase.
These mutations are often deleterious  or otherwise detrimen-
tal to virus fitness [22,23]. However, some mutants have an
advantage as they may allow escape from immune surveillance
[24,25] or more effective infection of certain tissue compartments
or cell types, such as cells in the brain or the genital tract
[26,27,28] or naı ¨ve CD4+ T-cells, which express CXCR4 [29,30].
Here we show that in addition to the mutational processes, HIV-1
can alter its population structure by frequency shifts among
subpopulations. Because we analyzed a relatively small number of
sequences per time point, we were careful to include the sampling
into our analysis method. Over short time (days, weeks, months)
Figure 1. Maximum likelihood tree of the phylogenetic
relationships of the viral subpopulations. Sequences from the
different time points (in days from day 1) are indicated with different
symbols and colors as shown. The subpopulations are labeled with
letters s1–s6 and the corresponding bootstrap values are shown as
ratios of 1000 replicates.
Neutral Subpopulation Fluctuations in HIV-1
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these fluctuations were consistent with a constant population size,
and most mutations that occurred at this time scale were neutral or
only weakly selected. On longer time scales we noticed that the
fluctuations became significant movements.
Here, we focused on short-term evolutionary processes (days,
weeks, and months), whereas earlier studies as well as the
generation of, and escape from, neutralizing antibody responses
involve time frames of months to years [31,32,33,34]. Clinically,
our patient was classified as a slow disease progressor. Genetically,
the virus population in our patient was described by co-existing
subpopulations. Thus, it is interesting to compare the HIV
population genetics of our patient to previously published patients
with normal and slow disease progression. In a study by
Shankarappa et al, 5 patients had slow disease progression (p2,
p3, p7, p9, p11) and 5 had normal progression (p1, p5, p6, p8) .
These patients were followed over many years, but interestingly
over a sampling period equivalent to ours (522 days, but with
fewer samples), patients in both clinical groups showed subpop-
ulation structure qualitatively similar to our patient (Figure S6).
Thus, the short-term evolution we study here is likely represen-
tative for many patients regardless of disease progression rate.
One might have expected that the persisting subpopulations
found in this patient were controlled by balancing selection
[35,36,37]. Directional selection would have favored the fittest of
the subpopulations and it would have been unexpected to see them
coexist for so long, let alone to have several well separated
subpopulations, which implies that they have existed for longer
than the study period. Hence, some type of frequency-dependent
selection, where the fitness of a variant/subpopulation is
dependent on its relative frequency, would be the alternative
hypothesis to neutral drift. Here we show that although the
immune system partly controls virus replication during the chronic
phase of the disease, particularly well in a slow progressor, and
where one would expect escape mutants to dominate in env, the
genetic evolution is consistent with a neutral process, at least over
the time period studied here. In agreement with this, it was
recently shown that genetic drift was a main contributor to HIV
evolution in culture . Similarly, in several other virus systems
Figure 2. Bar chart showing the observed within-patient frequency fluctuations of the genetic subpopulations during the study
period. Subpopulations as defined in Figure 1 are shown in respective colour and recombinant sequences are marked with diagonal stripes. P-values
for tests of constant within-patient subpopulation frequencies are shown above the histogram for each day. Thus for each day J, subpopulation
frequencies wiof days 1…J-1 are compared to the wifrequencies of day J. See text for details.
Table 2. Subpopulation frequencies: inferred, expected, and
(out of 10)Observed fi,522
s1 0.20360.100 2.030
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with large population sizes and high mutation rates, where
deterministic processes are expected, genetic drift was shown to
have a larger effect than expected [39,40,41,42]. Furthermore,
stochastic evolution during drug treatment of HIV-1 has
previously been demonstrated . Thus, also in vivo evolution
of HIV-1 during the chronic phase may be largely described by
neutral and stochastic processes. We speculate that this might be
due to that the immune system ‘‘hits’’ all subpopulations with near
Our tests for neutrality of the subpopulation frequency
fluctuations are of necessity informal. A more formal procedure
would assess the likelihood of the data under neutral models with
varying Neand compare with models that additionally include
either balancing or directional selection. There exist methods for
estimating Nefrom multi-allele temporal data (e.g. ), as well as
methods for inferring directional selection from two-allele
temporal data (e.g. ). However, we are not aware of likelihood
methods that include balancing selection and multiple alleles, and
their development is beyond the scope of the present study. Hence,
we have relied on a less formal method that may not have optimal
power, but nevertheless is informative. In addition, our Neestimate
from sequence data were in the order of previously estimated Neof
HIV-1 in chronic infection [8,13,43], however, subpopulation
structure or non-neutral evolution may bias these estimates,
therefore we included a large range of plausible Nein our test of
We have sampled free HIV-1 viral particles in plasma but we do
not know where these virions were produced. The degree of
compartmentalization of HIV-1 replication is uncertain; some
researchers have found evidence of compartmentalization whereas
others have not [29,44,45,46,47,48,49]. However, in untreated
patients most virus in plasma is produced by short-lived activated
CD4+ T-lymphocytes [50,51] and there is no or limited
compartmentalization between virus in plasma and lymphocytes
[44,52,53]. Thus, the plasma virus population should be
competing for the same resources, which would justify our analysis
of whether balancing selection exists. However, we cannot exclude
that the frequency fluctuations we see may be due to differential
production from different compartments. The subpopulations
Figure 3. Likelihoods of various aspects of the data under neutral evolution. The pink shaded region denotes the 2s range of Ne
(5126162) inferred using Fluctuate (Table 1) and the dotted line denotes a cut-off at p=0.05.
Figure 4. Genetic divergence in subpopulation s6. Mean pairwise
distances were calculated between sequences sampled with different
time intervals. At an interval of one month or more, the genetic
distances were significantly greater than the intra-sample diversity (0
days interval) (p,0.01, Wilcoxon rank sum test). Sampling intervals of
1–2 days and 3–4 weeks were estimated together and are named 1 day,
and 1 month, respectively.
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were present in actively replicating virus since 1) the subpopula-
tions were detected over time at high frequencies (i.e., detected in
7–11 single molecules per time point), 2) the molecules sequenced
must represent virions which were replication competent at least in
the previous generation, and 3) subpopulation s6 evolved at a
measurable evolutionary rate.
It was interesting to note that PNGS were significantly over-
represented among positively selected sites. Glycosylation and
movement of glycans have been suggested to be an important
immune escape mechanism of HIV-1 [31,32,34,54]. Our data are
compatible with such a scenario, which suggests that immune
escape and positive selection on PNGS may have contributed to
the evolution of the genetic subpopulations in our patient.
Previously, it was demonstrated quantitatively that a wide range
in strength of the autologous neutralizing antibody response
between patients and corresponding differences in the impact on
the viral population . The fact that we observed positive
selection on PNGS, and earlier studies have shown consecutive
replacement HIV-1 env sequences [2,55] and continuous neutral-
ization escape [33,34], do not contradict our observation that
evolution in our patient overall was consistent with a neutral
model of evolution.
The estimation of the evolutionary rate may be misled if
phylogenetic relationships are not accounted for. This may occur
in a naı ¨ve analysis where genetic distances between two (or more)
time points are directly compared. Without accounting for the
phylogeny, especially when the population is divided into clear
subpopulations, frequency shifts between existing variants will
masquerade as de novo mutations. We show that in env, which has
the highest evolutionary rate in the HIV-1 genome, the number of
de novo mutations accumulated a significant distance after about
one month within a subpopulation. Hence, this suggests that
sampling more frequently than this may not be useful to estimate
the evolutionary rate in patients during the chronic phase. This is
in good agreement with previous estimates of significant temporal
changes at 22 months based on genomic sequences of similar
length (,1100 nt) in gag-pol , which evolves much slower than
env. Note, however, that selection during drug treatment may
potentially act upon existing variants/subpopulations at a much
faster rate [56,57], but, as we show here, during chronic,
asymptomatic, untreated viral infection evolution proceeds mostly
by neutral drift over shorter time frames.
In conclusion, we have performed high-frequency sampling of
HIV-1 evolution in a chronically infected, untreated patient with
slowly progressing disease. We shown that multiple well-separated
subpopulations may persist for years, and over weeks and possibly
months their frequencies remained constant. Over the time period
of years, however, their fluctuations became significant, but were
still consistent with a neutral model of evolution. However,
sequence-based methods showed that individual sites had
experienced positive selection, possibly as the subpopulations were
being formed over several years. While the subpopulation
frequencies fluctuated consistent with neutrality, the divergence
within a subpopulation showed a temporal trend that was resolved
at about one month’s time.
Materials and Methods
Patient and samples
The patient was a treatment naive, asymptomatic man that had
been HIV-1 infected for approximately 7 years at the start of the
study (day 1). The plasma viral load had been stable and relatively
low for several years and ranged from 450 to 1220 copies per ml
during the main study period. The CD4 count was around 600 at
the time point for the first sample, but with previous CD4
fluctuations including values below 500. Thus, the patient did not
fulfill the definition of a long-term non-progressor (CD4 counts
.500 for more than ten years without antiretroviral), and instead
we classify the patient as a slow progressor . In support of this,
the patient was put on treatment after more than 15 years of
Blood samples were collected each morning for 12 consecutive
days (day 1 through day 12) and then once every week for 3 weeks
(day 18, 25 and 32). One later sample, that was collected 1.5 years
after the first sample (day 522) was also analyzed as well as an
earlier sample that was collected 1.5 years before the start of the
study (day 2664). Plasma was prepared by centrifugation at
2000 rpm for 10 min at room temperature and stored at 270uC
until analysis. Viral RNA was extracted from plasma using the
Nuclisense RNA extraction kit (NASBA Diagnostics, Organon
Teknika, Boxtel, The Netherlands) according to the manufactur-
er’s instructions, and cDNA synthesis was performed using the
First-Strand cDNA synthesis kit (Amersham Pharmacia Biotech,
The patient gave written consent and the study was approved
by the regional ethics committee (Karolinska Sjukhuset, Lokal
forskningsetikkommitte Nord) in Stockholm (Dnr: 98–336).
Amplification, cloning and sequencing
Single viral molecules were obtained by limiting dilution of the
cDNA . The method was selected to minimize the influence of
PCR errors in the sequences and to allow sequencing of the entire
env gene. According to the Poisson distribution, the likelihood that
a positive PCR reaction originates from a single molecule is 0.95 if
the fraction of positive reactions is 1:3. After a dilution series, we
determined the template load for each PCR and diluted our
template accordingly. Hence, positive PCR samples from dilutions
containing less than 1:3 of positive reactions were sequenced and
analyzed. The single molecule status was confirmed by screening
for mixed nucleotide positions in the final sequence chromato-
grams and sequences with mixed positions were excluded. Hence,
this procedure will identify PCR errors after the cDNA synthesis as
they would be seen in the chromatograms at frequencies #25%.
In addition, bidirectional sequencing was performed. In one
sequence only one mixed position (at 50% in overlapping
sequence fragments) was detected (A/G) and in this case both
possible sequences were included. A 3.1-kb region covering vpu,
env, and one-half of nef was amplified and sequenced as previously
described . A nested amplification was used with outer primers
GG-39) and JL89 (59-TCCAGTCCCCCCTTTTCTTTTAA-
AAA-39), and inner primers ED3 (59-TTAGGCATCTCCTA-
TGGCAGGAAGAAGCGG-39) and JL88 (59-TAAGTCATTG-
GTCTTAAAGGTACCTG-39). The Expand Long Template kit
(Boehringer Mannheim, Indianapolis, IN) was used according to
the manufacturer’s recommendations and a hot start was achieved
by separating the primers and the template from the enzymes (Tgo
DNA polymerase and Taq DNA polymerase) with a wax layer
(DynaWax; Finnzymes, Espoo, Finland). The PCR program was
94uC for 10 sec, 55uC for 30 sec, and 68uC for 4 min for a total of
30 cycles. Concentrations of 0.4 mM primer and 0.2 mM total
dNTP in a final volume of 50 ml were used, and 2 ml of first-round
product was transferred to the second-round reaction. Positive
reactions were purified using the GFX purification kit (Amersham
Biosciences Corp, Piscataway, NJ) and directly sequenced with a
walking primer approach using standard dideoxy-terminator
Neutral Subpopulation Fluctuations in HIV-1
PLoS ONE | www.plosone.org7August 2011 | Volume 6 | Issue 8 | e21747
fluorescent automated sequencing methodology (Applied Biosys-
tems, Foster City, CA) on ABI 310 or 3100 sequencing machines.
Sequencing primers were designed so that each nucleotide of the
PCR fragment was detected by at least two separate primers.
Hence, all nucleotide calls were made based on at least two
sequencing reactions, ensuring high base-call accuracy. The
sequences were evaluated and assembled into contigs using
Sequencher software (Genecodes Inc, Ann Arbor, USA). The
sequences are deposited in Genbank under the accession numbers:
Sequences were manually aligned to HIV-1 reference sequences
using the Se-Al software . The program Modeltest v 3.7 
was used to search for the substitution model that best described
the evolution of the dataset. ML trees were inferred using PhyML
3.0  using 5 random starting trees with SPR and NNI tree
search algorithms. Substitution model parameters were estimated
from the data. Topological uncertainty was estimated using
maximum likelihood evaluated non-parametric bootstrap analysis
with 1000 replicates. Whether the sequence data generally
supported a neutral model of evolution was tested using Tajima’s
D-test, Fu and Li’s D*-test and Fay and Wu’s H-test [19,20,21].
Coalescent estimation of Ne was made with the coalescent-
likelihood programs Recombine and Fluctuate implemented in the
Lamarc 2.1.3 package  as well as by calculating the mean-
pairwise distance (MPD) using the program PAUP* .
To exclude possible laboratory contamination and sample mix-
up, a phylogenetic tree was constructed where other subtype B env
sequences from the HIV sequence database  were included
together with the current dataset. This analysis showed that all our
sequences formed a monophyletic cluster (not shown).
In order to assess the extent of recombination in our dataset,
and possibly identify the recombinants, we applied a procedure
that has been shown to be able to identify intra-host
recombination . Conflicting phylogenetic signals in the
dataset are visualized using the Neighbor Net (NNet) algorithm
 implemented in SplitsTree version 4.10  and the
presence for recombination signal is then specifically tested with
the pairwise homoplasy index (PHI) statistic . The PHI
statistic measures the similarity between closely linked sites and
the significance of the observed test statistic is obtained using a
permutation test. If there is no recombination in the data the
genealogical correlation of adjacent sites is invariant to
permutation . But in the presence of finite recombination,
the order of the sites is important, and distant sites will tend to
have less genealogical correlation than adjacent sites. [69,70]
Subpopulations were screened one at a time by the PHI-NNet
test. Intra-subpopulation recombinants were removed before
screening for putative inter-subpopulation recombinants. As the
identification process of putative recombinants may be subjective
we wanted to control for human bias in selecting putative
recombinants. We therefore randomly removed an equal
number of sequences as were determined recombinant and
calculated the PHI p-value. This randomized reduction was
performed a hundred times.
To verify that the removal of the putative recombinants as
determined by the PHI-NNET analysis rendered the dataset free
from recombination signal we tested the two alternative datasets
with the single breakpoint analysis available at www.datamonkey.
Lineage- and site-specific selection analysis
Recombinant sequences, as determined by the PHI-NNet test,
were removed and the alignment stripped so that only single-frame
coding regions were present, i.e., only env without vpu/rev. A few
spurious stop-codons were conservatively changed to the weighted
nucleotide in the corresponding column of the alignment. This will
reduce diversity and will not lead to false positive selection
detection. We tested whether the identified subpopulations had
evolved under different selection pressures by using GAbranch
 available at the www.datamonkey.org website . GAb-
ranch automatically partitions all branches in the tree into several
selective regimes and performs multi-model inference enabling us
to infer dN/dS rates for each branch in the tree without
subjectively choosing which branches to test for differential
selection. In addition, we tested the subpopulations for site-specific
selection or variation using Nielsen & Yang’s hierarchical model-
pairs (M0, M1a, M2a, M3,) in HyPhy [6,74]. Individual amino
acid changes were identified over the ML tree within or between
subpopulations using MacClade .
HIV-1 population subdivision
Putative subpopulations were identified by high bootstrap values
as above. To test whether these subpopulations were statistically
significant, we conducted a test for population subdivision
originally developed by Hudson et al. and further developed to
test HIV-1 intra-patient evolution by Achaz et al. [13,70]. The
method calculates matrices of pairwise sequence differences for the
putative subpopulations as well as for the whole dataset. To assess
the significance of the structure the sequences are randomly
relabeled into new subsets of populations (keeping n1 and n2
constant), which generates a p-value for the probability that the
structure observed was due simply to chance. The test does not
rely upon a common genealogy for all sites, which makes it robust
to the presence of recombination .
Significance of subpopulation frequency fluctuations
Our significance test includes stochastic effects due to limited
sample sizes. To determine whether subpopulation frequencies
were significantly fluctuating within the patient, we asked whether
the sample frequencies observed on each day J were consistent
with the within-patient frequencies inferred from days 1…J-1. On
each day J we have NJtotal observed sequences, within which
subpopulation i has frequency count fiJ. If we assume the within-
patient frequencies wiare constant, then given the observations
from days 1…J-1 the maximum-likelihood estimate^w wiJof the
within-patient frequency of subpopulation i is simply the fraction
of all previously observed sequences that are from subpopulation i.
To assess whether the observed^w wiJfrequencies fiJon a given day J
are consistent with the inferred patient frequencies, we use
The x2statistic sums over all subpopulations i the deviation
between the observed counts fiJand the expected counts^w wiJNJ.
In assessing the significance of the observed x2values, we
account for our uncertainty in estimating the within-patient
frequencies from the day 1…J-1 data using simulations of the
estimation process. To do so, we begin with our maximum-
likelihood estimate^w wiJof the within-patient frequencies from the
observed data. We then generate simulated data sets using
Neutral Subpopulation Fluctuations in HIV-1
PLoS ONE | www.plosone.org8August 2011 | Volume 6 | Issue 8 | e21747
constant within-patient frequencies of^w wiJ, matching the number of
samples NJfrom each day. For each of these simulations, we
calculate x2for day J using population frequencies estimated from
simulated days 1…J-1. One subtlety is that x2will be divergence if
a subpopulation is first observed on day J. To avoid this, when
calculating x2for both the real and simulated data, we introduce
one addition count (a pseudocount) for each subpopulation on the
first day. Our p-values are then the proportion of simulated data
sets that yield a larger x2than the real data.
Simulation and likelihoods of neutral subpopulation
Our neutral simulations begin at day 1 with a population of Ne
individuals, divided amongst the 5 subpopulations based on their
inferred within-population frequencies^w wi32from days 1…32. In
each generation a new population of Neindividuals is created by
sampling with replacement from the prior generation, i.e., a
Wright-Fisher model of reproduction. The generation time for
HIV-1 is assumed to be 2 days , so that 260 generations
separate our samples at day 1 and day 522. For each simulation,
based on the final subpopulation frequencies after 260 generations,
we calculate the likelihood of observing particular aspects of the
real day 522 data. The likelihood of sampling a given number of
sequences from population i can be calculated directly from the
multinomial probabilities. The likelihood of observing n subpop-
ulations out of T possible is calculated by summing the likelihood
of obtaining 0 observations for T-n subpopulations, carefully
accounting for the number of ways to do this. Our overall
likelihoods represent the average over 104population simulations.
Also, our results are unchanged if we incorporate our uncertainty
in estimating^w wi32by initializing each simulation with different
within-patient frequencies consistent with our observations from
Evolutionary Rate Estimations
The programs TreeRate [76,77] and BEAST v1.5.1  were
used to infer evolutionary rates. TreeRate optimizes the root and
the evolutionary rate for a given tree by minimizing tip-height
variances at two specified sampling times. The given tree was
inferred by PhyML 3.0 as described above, thereby not
preconditioned on a molecular clock. In addition we used
Bayesian analysis (BEAST) assuming a relaxed molecular clock
(uncorrelated lognormal) and a non-parametric population growth
model (Bayesian skyline).
tionary relationships in the viral population including
incompatible signals. Clones from the different time points are
indicated with different symbols and colors as shown. The
subpopulations are labeled with letters s1–s6 A. All 77 taxa with
8 putative recombinants as determined by the PHI test and
indicated with a star. B. The resulting network when these 8
putative recombinants were removed.
Neighbor-Net diagrams showing the evolu-
es where the dN/dS values inferred through GAbranch
are shown. Taxa labels are colored according to genetic
Cladogram of the non-recombinant sequenc-
amino acid (aa) substitutions (A) and normalized
Correlation of the normalized frequency of
potential N-linked glycosylation site (PNGS) replace-
ments (B) to the probability of strong positive selection
pressure. The frequencies of aa and PNGS replacements were
normalized by the number of tree branches between subpopula-
tions (N=8) or within subpopulations (N=130). The selection
pressures were partitioned into 3 rate classes (dN/dS=3.92, dN/
dS=0.55, dN/dS=0.13), optimized using the Nielsen-Yang
model M3 in HyPhy [6,74]. The probability of each site of
belonging to the dN/dS=3.92 class was used to measure the
strong positive selection pressure. The correlations to strong
positive selection were R=0.78 (between subpopulation aa
substitutions), R=0.69 (within subpopulation aa substitutions),
R=0.40 (between subpopulation PNGS replacements), and
R=0.38 (within subpopulation PNGS replacements). The re-
sponse to strong positive selection, as measured by OLS regression
slopes, was 23 times stronger to between than within subpopula-
tion aa substitutions and 25 times stronger to between than within
subpopulation PNGS replacements (p,,0.001, F-statistic, in both
of data with putative recombinants excluded.
Likelihood under a neutral model of aspects
substitutions per site and year, measured between all
time points with TreeRate. Each arrow begins at the first
time-point, and the end of each shaded area represents the second
time point. The impact of inclusion or exclusion of putative
recombinant is shown; the upper part of each arrow represents
exclusion of recombinants (Figure S1), and lower part of arrow
represents the results when recombinants were included.
The evolutionary rates given in percent
our patient and previously published patient data. Our
patient (Study patient) was sampled over approximately 3 years,
with most samples days, weeks and months apart up to 522 days
(,1.5 years). Comparing our results to an equivalent sampling
period of patients in a study by Shankarappa et al , shows that
regardless of disease progression rate similar subpopulation
structure as in the study patient occurs in at least 5/9 Shankarappa
patients (3, 5, 7, 8, 9). Shankarappa patients 2, 3, 7, 9, 11 had slow
disease progression, as our patient, and the others normal disease
progression. All trees are on the same scale (see scale bar) and the
sampling time intervals are also on the same scale, 120 evenly
divided colors over 12 years (12 colors shown in legend).
Genetic diversity and divergence over time in
ing recombinant sequences from our analysis, we obtain the results
shown in Table S1 for the significance of within-patient frequency
Subpopulation frequency fluctuations. Exclud-
consistent with neutral drift. Table S2 shows, excluding
recombinants, the subpopulation frequencies inferred from our
data, along with the expected and observed counts in day 522.
Again, we observe several aspects of the data that are informative
about potential deviations from neutrality. We test for the
likelihood of 1) s1 not being observed, 2) s3 being observed, 3)
s4 not being observed, 4) s5 being observed at frequency 2 or
greater, 5) s6 being observed at frequency between 3 and 5
(indicating a fluctuation of less than 1 from expected), and 6)
observing 3 or more populations. Figure S1 shows the results that
Subpopulation frequency fluctuations are
Neutral Subpopulation Fluctuations in HIV-1
PLoS ONE | www.plosone.org9August 2011 | Volume 6 | Issue 8 | e21747
none of these aspects of the data are significantly unlikely (p,0.05)
under a neutral model.
The authors would like to thank Sergei Kosakowski Pond for help with the
Conceived and designed the experiments: TL HS KWR JA AA. Performed
the experiments: KWR AA. Analyzed the data: HS RNG TL. Wrote the
paper: TL HS RNG JA. Conceived and designed the population genetics
modeling: RNG TL.
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PLoS ONE | www.plosone.org11 August 2011 | Volume 6 | Issue 8 | e21747