Evolution of Drug-resistant Viral Populations during Interruption of
Running title: Evolution and fitness of drug-resistant viruses
Dongning Wang1, Charles B. Hicks2, Neela Goswami2, Emi Tafoya3, Ruy M.
Ribeiro3, Fangping Cai1, Alan S. Perelson3 and Feng Gao1*
1Duke Human Vaccine Institute and 2Department of Medicine, Duke University
Medical Center, Durham, NC 27710, USA
3Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los
Alamos, NM 87545, USA.
3072B MSRB II, DUMC 103020
Duke Human Vaccine Institute
Duke University Medical Center
Durham, NC 27710, USA
E-mail: [email protected]
Analysis of a large number of HIV-1 genomes at multiple time points after
antiretroviral treatment (ART) interruption allows determination of the evolution of
drug-resistant viruses and viral fitness in vivo
in the absence of drug selection
pressure. Using a parallel allele-specific sequencing (PASS) assay, potential
primary drug-resistant mutations were studied according to drugs used in five
individual patients by analyzing over 18,000 viral genomes. A three-phase
evolution of drug-resistant viruses was observed after termination of ART. In the
first phase, viruses carrying various combinations of multiple drug-resistant
(MDR) mutations predominated with each mutation persisting in relatively stable
proportions while the overall number of resistant viruses gradually increased. In
the second phase, viruses with linked MDR mutations rapidly became
undetectable and single-drug-resistant (SDR) viruses emerged as minority
populations while wild-type viruses quickly predominated. In the third phase, low-
frequency SDR viruses remained detectable as long as 59 months after
treatment interruption. Mathematical modeling showed that the loss in relative
fitness increased with the number of mutations in each viral genome, and that
viruses with MDR mutations had lower fitness than viruses with SDR
No single viral genome had seven or more drug-resistant mutations, suggesting
that such severely mutated viruses were too unfit to be detected or that the
resistance gain offered by the seventh mutation did not outweigh its contribution
to the overall fitness loss of the virus. These data improve understanding of
evolution and fitness of drug-resistant viruses in vivo
and may suggest strategies
for improved management of ART-experienced patients.
Despite significant advances in antiretroviral therapy (ART), some HIV-
infected patients still fail treatment due to drug resistance, poor adherence, drug
toxicity, and suboptimal drug metabolism (4, 35). Among these causes,
emergence of drug-resistant mutations plays a central role in ART failure (14,
35). In addition, the presence of pre-existing drug-resistant viruses correlates
with poor responses to ART (18, 28, 36-39). Multiple drug-resistant (MDR)
mutations have been shown to exist in various complex linkage patterns in viral
genomes (3, 26, 34) and these MDR viruses can play an important role in ART
failure. Discontinuation of ART in patients who have developed drug resistance
leads to disappearance of drug-resistant mutations in the viral population within
weeks and replacement with wild-type (WT) viruses (7-9, 20, 40). However,
when a more sensitive allele-specific PCR (ASPCR) method was used, minority
drug-resistant viruses (0.01% - 20%) could be detected in most patients for
months or even years following ART discontinuation (12, 33). The evolution of
both majority and minority linked MDR virus populations after treatment
interruption, however, has only been characterized previously in a limited number
of viral genomes in HIV-1-infected persons.
Fitness of drug-resistant viruses has been extensively studied in vivo
(6, 10, 11, 13, 15, 23-25, 27). An improved understanding of viral fitness
among various drug-resistant viruses may have implications for development of
better treatment strategies and may be a factor in antiretroviral drug discovery.
However, only a limited set of mutations and small number of viral genomes have
been studied previously in HIV-1-infected individuals. A more thorough
evaluation of drug-resistant mutations in the absence of drug selection pressure
by analyzing large viral populations in patients who discontinued failing ART
provides an opportunity to investigate evolution, fitness and reservoirs of the
drug-resistant viruses, especially those with linked MDR mutations.
We recently developed a highly sensitive parallel allele-specific
sequencing (PASS) assay that can detect minority drug-resistance populations
present at frequencies as low as 0.01% to 0.1% of the viral population by
simultaneously analyzing thousands of viral genomes in a single assay (3). In
addition, this new technology allows identification of linkages among MDR
mutations on individual viral genomes. In this study, we characterize the
evolution of MDR mutations and their linkage relationships among a large viral
population from longitudinal samples collected from individual patients following
treatment interruption. These data are then used to model the evolution and
fitness of various drug-resistant viruses, and to estimate the time to emergence
of the WT virus following treatment interruption.
Materials and Methods
Patient plasma samples.
The study population consisted of HIV-1-
infected persons followed at the Duke University Medical Center between 2001
and 2003. Residual plasma remaining after clinical viral load testing were stored
at -80o C. Patients selected for this study were identified from the HIV Patients
Sample Repository database after reviewing characteristics of their treatment
history. To be included in the study, patients had to have been followed in the
Duke HIV/AIDS Clinic for at least one year, during which time they underwent
ART interruption and thus had both on-treatment and off-treatment samples
available at different time points to be studied. Based on patient responses to
previous ART regimens and/or results from population-based genotypic drug-
resistance testing, samples were selected from patients experiencing treatment
failure with viral load rebound. Patients were excluded if fewer than two blood
samples after treatment interruption were available for analysis. Written informed
consent was obtained from all the individuals whose blood samples were
collected. The study was approved by the Duke University Institutional Review
Viral RNA extraction and cDNA synthesis.
One milliliter of each sample
was then concentrated by ultra-centrifugation at 32,000 rpm for 3 hours at 4o C.
The virus pellet was then resuspended with 400 µl of PBS and the viral RNA
(vRNA) was extracted using PureLink viral RNA/DNA mini kit (Invitrogen,
Carlsbad, CA). The vRNA was eluted into 17 µl of the elution buffer and used for
cDNA synthesis using Superscript III (Invitrogen, Carlsbad, CA) and primer
RTuni1 5'-CCAATCCCCCCTTTTCTTTTAAAATTGTG-3' in a 40 µl reaction. An
appropriate amount of cDNA was used for the PASS assay to obtain an optimal
number of viral genomes in each assay.
Detection of drug-resistant mutations by PASS.
The PASS assay was
performed as previously described (3). Briefly, 20 µl of 6% acrylamide gel mix,
containing 1 µM acrydite-modified reverse primer, 5'Acr-
AATCCCTGCATAAATCTGACTTGCCCAAT-3', cDNA template (5 ul to 18.5 ul),
0.3% diallyltartramide, 5% rhinohide, 0.1% APS, 0.1% TEMED and 0.2% BSA,
was used to cast a gel on a bind-saline (Amersham Biosciences, Piscataway,
NJ) treated glass slide. The in-gel PCR amplification was then performed in a
PTC-200 Thermal Cycler with a mix of 1 µM forward primer, 5'-
TTAGCTTCCCTCAGATCACTCTTTGGCA-3', 0.1% Tween-20, 0.2% BSA, 1x
PCR buffer, 250uM dNTP mix, 3.3 units of Jumpstart Taq DNA polymerase
(Sigma, St. Louis, MO), and H2O (up to 200ul) under a sealed SecurSeal
chamber (Grace Bio-Labs, Inc., Bend, OR). The PCR condition was: 94 o C for
After PCR amplification, single-base extension (SBE) was performed with
mutant and wild-type (WT) bases distinctively labeled with Cy3 and Cy5,
respectively, using the primers that annealed just upstream of the mutation sites
of interest. To detect multiple drug-resistant mutations on the same viral genome
for linkage analysis, the amplified viral genomes (polonies) in each gel were then
sequentially interrogated by 6-12 SBE reactions for targeted drug-resistant
mutations. Mutation sites were selected for analysis based on the treatment
history of the patients and genotypic test results when they were available.
Between 6 and 12 drug-resistant mutation sites were assayed for the amplified
viral genomes in each sample by repeating SBEs. After each SBE, the gel was
scanned with a GenePix 4000B Microarray Scanner (Molecular Devices,
Sunnyvale, CA) to acquire images.
PASS data analysis.
The two channel images (Cy5 for WT and Cy3 for
mutant) acquired from each PASS assay were first cropped with Picture Window
Pro3.5 to remove the edge area containing no signal. The cropped images were
then analyzed with the Progenesis PG200 (Nonlinear Dynamics, Durham, NC)
software. After background subtraction, normalization and spot filter setting, only
the unambiguous spots at either channel were included for further analysis. The
normalized pixel count data at multiple mutation sites for each spot were
exported into an Excel file with a unique number. By comparing each spot's
normalized values at both channels, the position was classified as WT or mutant.
Finally, the linkage pattern of all mutations on each viral genome was determined
by compiling mutation information at all analyzed sites with the Linksys program
Frequency of mutants in the population.
At each sampling time a
number of sequences, N, are analyzed. If as in Fig.1, we find 9 out of 9
sequences all have the M184V mutation, the frequency of M184V mutants in the
sample is 100%, but other sequences not containing M184V may be present in
the population and just not sampled. For example, if the frequency of non-M184V
sequences is 1/(N+1), i.e. 10% in this case, then the probability of observing 9
out of 9 sequences with M184V, computed from the binomial distribution,
Binom(9,9,p=0.9) is 0.39. One can also use the binomial distribution to
determine with what frequency non-M184V sequences would need to be present
in order to obtain with 95% probability no non-M184V mutants in a sample of size
N. As discussed in more detail in reference (21), this is the solution of
Binom(0,N,p) = 0.95. For N=9, if non-M184V sequences were present at p=0.5%
or less one would expect 9 out of 9 sequences to be M184V with >95%
Viral fitness comparison.
A conventional method (11, 15, 41) of
analyzing the difference in growth rates (or fitness) between mutant and WT was
used to evaluate the relative fitness between strains. In this method, one
assumes the wild type (W) and mutant (M) virus grow according to
Here the number of cells infected by WT and mutant virus was assumed to be
proportional to the amount of wild type and mutant virus, respectively (i.e., the
virus and infected cells were assumed to be in quasi-steady state (31)). Cells
infected with WT virus were assumed to grow at rate r
, and to be cleared at rate
δ. Cells infected with mutant virus were assumed to grow at rate r'
and to be lost
at rate δ'
. The solution for these equations, which gives the evolution of the viral
levels for each strain, between times t1
The ratio of mutant to WT virus at the two times was used to calculate d
difference in the net growth rates, where d
This difference, d
been called the log-relative fitness (41), and is computed here by the formula
where f(t)= M(t)/W(t)
and ln denotes the natural logarithm. For simplicity and to
be consistent with much of the virology literature, d
will simply be called the
relative fitness rather than the more precise log relative fitness. The relative
fitness can also be related to the selection coefficient, s, by the formula d=
(41). Note that when s
is approximately equal to s
approximation has been used to calculate s
(11). Equation (1) is written such that
we are assessing the fitness of the mutant relative to the WT. Since our
measurements were made after interruption of therapy, we expect the drug-
resistant mutants to be less fit than WT and d
will be negative, and its value can
be viewed as the fitness loss of the mutant relative to the WT.
For each patient, the relative fitness was calculated over the time frame
where both WT and mutant virus were observed. For this time frame, we write
which is the equation of a straight line with Δt=t2-t1
as the independent variable.
Using linear regression one can then estimate d
(41). Note that if the fitness of
the mutant or WT changes over time, Eq. (2) can be interpreted as the mean
relative fitness of the mutant averaged over the time interval, i.e.,
We analyzed the data by first grouping all sequences that had the same
number of mutations to calculate the relative fitness of a "strain" with i
=1, 2, 3… versus the WT. Since we had the most data for strains with one or
two mutations in PID811, we also computed the relative fitness of these strains
individually for this patient.
We calculated confidence intervals for the relative fitness estimates using
a bootstrap method. In each case, we generated 1000 new datasets, with the
same number of total genomes as the data and with a distribution of WT and
mutants as given by the data. That is, we used a multinomial distribution with the
probabilities given by the data to calculate for each bootstrap replicate the new
number of WT, 1-point, 2-point, etc. mutant genomes. In those few cases where
the number of genomes for a given strain in the bootstrap dataset was chosen as
zero, we imputed 1 genome (note that Eq. 2 cannot be applied when one of the
frequencies is zero). Implicitly this is the same as assuming that the lowest
frequency of detection is 1/N (where N is the total number of genomes analyzed
at a given time point) and that the next genome sampled would be of that strain
(1). We then used Eq. 2, as before, to calculate the relative fitness in these
bootstrap datasets. We picked the 2.5-percentile and the 97.5-percentile of these
estimates as the limits of the 95% confidence interval.
Estimation of the time for WT virus appearance after treatment
The time at which the wild type strain emerged was estimated by a
method introduced by Asquith and Mclean (1) in the context of determining the
time that CTL escape mutants arise. The fraction of the wild type strain, p(t)
= M(0)/W(0), and d
is the difference of viral growth rates, as before.
Equation 3 can be transformed to a linear equation as
). To estimate the parameters d
, we used the data after
treatment interruption in Supplementary Table 1. From this data we computed
the fraction of WT virus, p(t)
, at various times and then used linear regression
(Eq. 4) to estimate d
. We then define the time when the WT strain
emerges as the time its frequency reaches 1% of the population. We can
determine this time by choosing p
)=0.01 and calculating t
in Eq. (4), using the
best-fit parameters d
from the linear regression described above.
Clinical characteristics of the study subjects
Five patients who interrupted antiretroviral therapy, either per physician
recommendation or by self-choice, were selected for analysis (Table 1). All were
followed regularly in the HIV/AIDS clinic and had periodic viral load testing during
the duration of their treatment interruption. In all cases, plasma viral loads quickly
increased following treatment interruption. Multiple off-therapy samples and one
on-therapy sample (0-61 weeks before treatment interruption) were available for
assessment in each individual. All patients had received therapy with three or
more antiretroviral agents over a period of 37 to >260 weeks prior to treatment
interruption. Drug-resistant mutations were identified in a clinical population-
based HIV genotyping resistance test in two patients (PID811 and PID908) either
while still on a failing ART regimen or just after treatment interruption
commenced, confirming the presence of drug-resistant mutations in both
individuals. Clinical data including prior ART regimens, duration of ART, dates of
samples collection, viral load test results, and the results from clinical HIV
resistance testing are summarized in Table 1.
Dynamic population changes of individual drug-resistant mutations
Viral RNA was extracted from longitudinal samples in each patient and
drug-resistant mutations were assayed by PASS. A total of 18,451 viral genomes
(an average of 802 per time point) were analyzed. In general, samples collected
during non-suppressive ART that only partially suppressed viral replication had a
low viral load and only few or no viral genomes were detected in four patients
(PID811, PID908, PID268 and PID295). In each of two cases (PID895 and
PID295) with low viral loads (1,111 copies/mL and 213 copies/mL, respectively),
only one viral genome was detected. These samples were not included in the
Six to twelve mutation sites in each patient were selected for analysis
based on potential resistance patterns that may have been selected by the
particular agents being used during the time of treatment failure. When
available, clinical genotypic resistance test results were also used to identify
mutations of interest. Four to eight drug-resistant mutations were detected in four
of the five patients; no drug-resistant mutations were detected in the fifth patient
PID295 (Table 1).
Drug-resistant mutations were first analyzed individually for each patient.
Patient PID811 was treated with zidovudine (AZT), lamivudine (3TC) and
abacavir (ABC). The patient had also received nevirapine (NVP) in an earlier
regimen. The viral load was 2,104 copies/mL in an on-therapy sample collected
10 weeks before treatment interruption, and it continuously increased to 178,017
copies/mL at week 32 after ART interruption. Eight primary and one secondary
drug-resistant mutations were detected by a clinical genotype resistance test in a
sample obtained three weeks prior to treatment interruption (Table 1). The PASS
assay was completed on one on-therapy sample and five off-therapy samples
collected within 32 weeks after treatment interruption. On average, 304 viral
genomes (range 9 to 822) were analyzed at each time point. Based on the
treatment history and available genotypic resistance tests, eleven primary drug-
resistant mutations to reverse transcriptase inhibitors (RTIs) and one to protease
inhibitor (PI) were assessed. Of these, eight (M41L, D67N, K65R, K70R, K103N,
M184V, L210W and K219Q/E) were detected and four (L74V, Y115F, T215Y/F
and I84V) were not (Table1). K103N (RTI mutation) and the I84V (PI mutation)
were included for analysis based on the genotypic test result and the previous
treatment history, respectively. While the patient was still on therapy, the viral
load was low and only nine viral genomes were analyzed. At this time, all viruses
had the M184V mutation (Fig. 1). The proportion of observed viruses with the
M184V mutation was relatively stable (89% -100%) for the first nine weeks
following treatment interruption, and then dramatically declined over the next 23
weeks during which time WT viruses predominated (Fig. 1 and Fig. 2A). After
week 24, the M184V mutation became undetectable. Similar results were
observed for the other seven identified mutations, although they were present in
varying proportions in the viral population. The exception to this pattern was the
K103N mutation, which initially decreased to 5% at week 24 but then became a
higher proportion (26%) at week 32 (Fig. 2A). The K013N substitution can be
caused by either AAC or AAT allele. Since the AAC allele is about 100 fold
higher than the AAT allele in patients who have developed the K103N mutation
(32), we only analyzed the AAC allele. Thus, the total percentage of the K103N
mutation might be higher in this patient. The K103N mutation has been detected
in patients months after treatment termination and the percentage in the viral
population generally decreased over the time (17, 33). The cause of the sudden
increase of the K103N virus at one time point in PID811 was unclear.
Fewer time point samples from three other patients were available for
analysis. However, evolution of drug-resistant mutations in these patients was
very similar to what was observed in PID811. Two samples in the early weeks
after treatment interruption were analyzed in PID908 (Table 1 and Fig. 2C).
Three mutations in the reverse transcriptase gene (M184V, D67N and K70R) and
four in the protease gene (M46I, G48V, V82A, I84V) were detected. M184V was
present in all detected viral genomes while other mutations accounted for various
fractions of the viral population when the patient was on therapy. Similar to the
pattern seen in PID811, the proportions of the various mutations either persisted
at the same level (M184V) or modestly decreased (others) at week 6 but then all
declined substantially by week 11 when WT virus began to emerge (Fig. 2C).
In PID268, the first sample was collected on the day the patient
discontinued ART. Fifteen viral genomes were analyzed at this time point and all
had the RTI-resistance mutation M184V and the PI-resistance mutation V82A
(Fig. 2E). Interestingly, neither the M184V mutation nor the V82A mutation was
detected in other three samples obtained later during the course of treatment
interruption (34-52 weeks). Instead, three RTI-resistance mutations (D67N, K70R
and K219QE) were detected at week 32 and, as noted in PID811, they
decreased to very low levels by week 52.
Samples were only available 61 or 39 weeks before treatment interruption
in PID895 and PID295, respectively. Both samples had low viral loads (1,111
copies/mL in PID895 and 213 copies/mL in PID295), suggesting that viral
replication was well controlled when ART was stopped. Four drug-resistant
mutations were detected in PID895 and all were present at very low frequencies
(<0.6%) even at nine weeks after treatment interruption. The sum of all detected
resistant viruses accounted for less than 1% of the total viral population at any
time point during treatment interruption (Fig. 2G). No mutations were detected at
any time points (between week 13 and week 58) after treatment interruption in
PID295 (Table 1). Of note, three RTI-resistance mutations (D67N, K70R,
M184V) and one PI-resistance mutation (V82A) were consistently detected in
other patients (Table 1 and Fig. 2) but not in this patient. The infrequent detection
of minority resistant mutations in these two patients suggests that selection of
drug-resistant mutations was limited at the time treatment was discontinued.
Linkage analysis of MDR mutations.
The ability of the PASS assay to detect different mutations present in each
viral genome allows determination of the linkage patterns of MDR mutations
among a large population of individual viral genomes. Viral genomes with linked
MDR mutations were identified in three individuals (PID811, PID908 and
PID268), all of whom had higher levels of viral replication at or shortly before
treatment interruption. In all three patients, nearly all detected on-therapy viruses
carried two or more drug-resistant mutations (Tables 2-4). Linkage analysis of
eight drug-resistant mutations in PID811 revealed 35 different linkage patterns
(Table 2). During the first 15 weeks following treatment interruption, viruses with
as many as 6 linked MDR mutations were identified but at very low frequencies,
while the majority of detected viruses had 2, 3 or 4 linked MDR mutations. Nearly
all these MDR viruses became undetectable by weeks 24 and 32. In contrast,
single drug-resistant (SDR) viruses were rare in the first 9 weeks after treatment
interruption (only M184V mutants were consistently detected). After week 15,
nearly all drug-resistant viruses had only a single mutation, almost always the
K103N or M41L mutation. Similar results were observed in PID908. A total of 28
linkage patterns were identified, and the MDR viruses predominated in the viral
population from on-therapy to week 6 after interruption (Table 3). These viruses
then rapidly decreased in frequency and were replaced by WT viruses and
minority SDR viral populations (<2%) by week 11. Interestingly, viruses with only
the M184V mutation accounted for 39% of the viral population at week 6 and
increased further to 57% of the viral population by week 11.
In the third patient PID268, 15 viral genomes were analyzed at the time of
treatment discontinuation, and all had both M184V and V82A mutations (Table
4). However, neither M184V nor V82A were detected in subsequent samples
(weeks 34, 41 and 52), perhaps because no samples were available during the
early time period following ART interruption. Instead, low frequencies of linked
MDR mutations (D67N/K219QE and K70R/K219QE) were detected at week 34,
but all viruses had only SDR mutations which continuously decreased over the
Most linked MDR mutation patterns were present as minority populations
(<2%), but a few linkage patterns were more common. For example, four linkage
patterns (M184V/K103N/K70R/K219QE, M184V/K103N/M41L, M184V/K103N
and M184V/K219QE) in PID811 accounted for more than 25% of the population
at week 3 and week 9 (Table 2). Similarly, three linkage patterns
(M184V/V82A/G48V/I84V, M184V/V82A/G48V and M184V/V82A) accounted for
much higher proportions of viruses than others in PID908 (Table 3). These
results indicate that some drug resistant viruses with particular linked MDR
mutation patterns might have a significant fitness advantage. In all three patients,
viruses with 7 or more mutations were not found at any time points, suggesting
such viruses are too unfit to survive, i.e., that the resistance gain offered by the
seventh mutation did not outweigh its contribution to the overall fitness loss of the
virus, or do not exist in sufficient quantity for detection.
In the three patients who carried linked MDR viruses, those with
combinations of four or five linked mutations were noted to gradually decrease
over time after therapy interruption, but viruses with one or two mutations
transiently increased during the first 6-10 weeks and then decreased thereafter
(Fig. 3). Viruses with five or six mutations occurred too infrequently for
conclusions to be drawn. However, some linkage patterns, for example,
M184V/K103N/K70R/K219QE and M184V/K103N/M41L in PID811 continued to
increase in the viral population during the first 9 weeks after ART interruption and
persisted at relatively high frequency (19% and 4%, respectively) at week 15
(Fig. 3D and Table 2). Interestingly, predominant viruses with linked MDR
mutations shared the same mutations (M184V, K103N and M41L in patient
PID811; M184V and V82A in both patients PID908 and PID268 who were treated
with RTIs and PIs). The mutations present in the dominant viruses with linked
MDR mutations were generally those present at higher frequencies as SDR
mutations (Tables 2-3). These results suggest that some drug-resistant viruses
with different combinations of MDR mutations existed in higher frequencies,
probably because they could be easily selected under these particular ART
regimens and had greater fitness.
Three-phase evolution of drug-resistant mutations during treatment
Three patients with higher viral loads (PID811, PID908 and PID268) had
samples available at the time of or shortly before treatment interruption. When
the combined data from these three patients were analyzed, a three-phase
evolution of drug-resistant mutations was observed following treatment
interruption. In the first phase (the first 9 weeks after treatment interruption), the
proportions of drug-resistant viruses present were stable or slightly decreased
(Fig. 2A and 2C; Tables 2 and 3) while the absolute number of viruses with
resistant mutations increased (Fig. 2B and 2D). The predominant viruses have
MDR mutations. Viruses with only SDR mutations were uncommon. In this
phase, WT viruses were very rare or undetectable. In the second phase (9-32
weeks after treatment interruption), the proportion and number of drug-resistant
viruses rapidly decreased and they were quickly replaced by WT viruses (Fig.
2A-2D; Tables 2 and 3). In this phase, viruses with MDR drug-resistant mutations
decreased markedly while viruses with minority SDR mutations became the
major drug-resistant population. In the third phase (32-34 weeks after treatment
interruption), drug-resistant viruses became very rare in the viral populations
(<1%) and only viruses with SDR mutations were detectable (Fig. 2A and 2E).
Fitness of drug-resistant viruses
To determine the fitness of various groups of drug-resistant viruses, we
first classified them into seven groups based on the number (0 through 6) of
mutations each virus carried (Supplementary Table 1). Each mutation was
counted as one, so viruses in each group might contain different combinations of
mutations. For each patient, t1
was chosen as the first time for which data was
available for both WT and mutant strains, and t2
chosen as a subsequent time.
Then, we estimated the relative fitness (d
) by fitting Equation 2 to the data. Viral
genomes with 5-6 mutations were not included for analysis since there were too
few of them for comparison. The estimated relative fitness (d
) of viral strains with
different numbers of mutations was computed, together with the respective 95%
confidence intervals (Figure 4). In patients PID811 and PID908, from whom
viruses with MDR mutations were detected at multiple time points, we can
compare the cost of increased number of mutations in relation to the WT virus
(Figure 4). The loss in relative fitness increased linearly in PID811 (p=0.00016),
whereas in PID908 the three point mutant did not lose as much fitness as
predicted by a linear loss (i.e., a positive quadratic term for fitness loss was
significant, p=0.02) (Figure 4).
Various combinations of drug-resistant mutations at different time points
were observed in PID811 (Table 6). This allowed for calculation of the relative
fitness by taking into account the different combinations of mutations. Equation 1
was applied to determine the relative fitness based on which drug-resistant
mutations were present in the viral genome. Among 41 strains of drug-resistant
viruses detected in PID811, the majority of them carried MDR mutations (2-6)
while only six had SDR mutations (Table 2). Over time, the frequency of the
fittest strains increased (Table 6). For instance, from day 63 to 105, the M184V
virus was present with low fitness (d
=-0.104) relative to the WT; however, SDR
mutants with M41L or K103N emerged between days 105 and 168, and they
were more fit (d
=-0.016 or -0.012) than the M184V mutant, which was lost in this
time window. This also was seen for the double mutant population, where from
days 105 to168, all double mutants except K103N/M41L were lost, and the
K103N/M41L double mutant was at least six times more fit than any other double
mutants in the previous time period.
Estimation of timing for WT virus emergence during treatment interruption
WT viruses gradually increased and became dominant after treatment
interruption in patients PID811, PID908 and PID268 (Tables 2-4). It would be
informative to estimate the time at which the WT viruses emerged in each
patient. Using the values of d
in Eq. 4, the time for WT viruses to emerge
after treatment was estimated. Here we define the time of emergence as the time
the WT virus reaches 1% of the population, when it was undetectable before.
The time for WT virus to reach 1% of the population was 56 days in PID811
(based on data collected until week 24), 50 days in PID908, and 44 days in
PID268. On average, WT viruses emerged 50 days after treatment interruption.
However, these times depend on our definition of "emergence". If instead of a
frequency of 1%, we define it as the time it takes the WT to reach 0.1%, then the
estimated times are 23 and 35 for PID811 and PID908, respectively. For PID268,
we cannot calculate the time it takes WT to reach 0.1%, because the estimated
proportion of WT at time 0 is already 0.3%. Note that for PID268, we have no
data between weeks 0 and 34, i.e. day 238, which leads to high uncertainty in
frequency estimates early after therapy interruption.
Interrupting antiretroviral therapy in HIV-infected persons whose treatment
regimens are not achieving viral suppression provides an opportunity to study the
evolution of drug-resistant mutations and viral fitness in vivo
during the period
after drug selection pressure is removed. In this study, WT, SDR and linked MDR
mutations were analyzed from over 18,000 viral genomes at multiple time points
in five treatment-interrupted patients using the PASS assay. These data
demonstrated a three-phase evolution of drug-resistant mutations and
complicated patterns of linked MDR mutations. Mathematical modeling showed
that the relative loss of viral fitness increased with the number of mutations in the
viral genome, and that WT viruses reached an estimated frequency of 1% after
1-2 months post treatment interruption.
Understanding of the evolution of viral populations following treatment
interruption has been hindered by inability to characterize a large number of
viruses and the limited availability of samples from appropriate time points.
Analysis of thousands of viral genomes by PASS from three patients at multiple
time points (up to 59 weeks) following treatment discontinuation demonstrated a
three-phase evolution pattern of drug-resistant mutations. In the first phase, HIV
viral load quickly rebounded shortly after ART interruption. These early rebound
viruses were almost exclusively those with linked MDR mutations, although the
proportion of drug-resistant viruses within the population remained stable or
decreased slightly. WT viruses were generally undetectable at this time, although
our model predicted they started to accumulate. In the second phase, the
proportions and the numbers of viruses with MDR mutations rapidly decreased
while viruses with SDR mutations gradually increased. In this phase, WT viruses
quickly increased and became predominant. In the third phase, the vast majority
of viruses are wild type and the only detected drug-resistant viruses were those
with SDR mutations present at very low frequencies (<1%). This phase was
observed to last more than a year.
Our data indicated that the relatively stable proportion of drug-resistant
mutations observed in the first phase was maintained by a continuous increase
of drug-resistant viruses in blood following ART interruption, while the rapid
disappearance of these drug-resistant viruses was caused by the increase of
more fit WT viruses and possible decreased release of drug-resistant viruses into
the blood in the second phase. Similar evolution patterns of drug-resistant
viruses have been reported previously (7, 8). In these studies, the drug
susceptibility of the viral populations was determined by a phenotypic resistance
assay after ART discontinuation. Viruses in these patients were fully resistant to
ART over the first 15 weeks following ART discontinuation, but they were quickly
replaced by drug susceptible viruses over a very short period and remained drug-
susceptible thereafter. However, drug-resistant mutations were not determined in
Although viruses with linked MDR mutations are less fit than the WT virus,
they continuously replicate early on when drug-selection pressure has been
removed since initially they are the only detectable viruses. This results in the
observed early increase of the number of drug-resistant viruses and the
persistence of the proportion of drug-resistant viruses in the first stage. However,
due to the impaired fitness of these viruses in the absence of drug selection
pressure, they are subsequently outcompeted by WT viruses, which become the
dominant population beginning about 10 weeks after treatment interruption. The
drug selection pressure declines relatively quickly after ART interruption since
the half-lives of antiretroviral drugs are only a few days even for long lasting
NNRTIs (2, 5, 19, 29). Therefore, the presence of mostly drug-resistant viruses
during the first nine weeks after treatment interruption cannot be due to the
continued suppression of WT viruses by residual antiretroviral effect in these
patients, but rather may reflect the fitness costs of resistance, as calculated here,
and the low frequency of WT when treatment is interrupted.
An area of considerable potential importance is the effect of linked MDR
mutations present in a single viral genome as opposed to various drug-resistant
mutations being present as single mutations in different virions within a patient.
This phenomenon has not been well studied to date due to the difficulty of
assessing a large number of viral genomes within any one HIV-1 infected
individual (26, 34). Using the recently developed novel PASS assay (3), we were
able to perform linkage analysis of drug-resistant mutations from thousands of
viral genomes in each patient. Many different genetic variants with various
combinations of linked MDR mutations were identified in patients in whom MDR
mutations were detected. Viruses with 5-6 mutations were detected but they
were present at much lower frequencies, suggesting that they had impaired
fitness. In contrast, viruses with 2-4 mutations were the majority viruses at the
time of ART failure and during the early stage of treatment interruption.
Population genetic analysis also indicated that a few virus populations with
specific combinations of some drug-resistant mutations were more predominant
than others. The precise reason why some mutations predominate with certain
combinations is not clear, but likely is the result of pathways of improved viral
fitness and the interplay between mutations best suited for resistance to
particular drugs that are being administered or have been previously used.
Although hundreds of viral genomes at each time point were analyzed by PASS
during treatment failure and treatment interruption, no viral genomes were found
to have 7 or more drug-resistant mutations. There may be two possibilities for
this. Firstly, the resistance gain offered by the 7 or more mutations did not
outweigh its contribution to the overall fitness loss of the virus. The fitness cost
for such viruses was too high for them to outcompete viruses with fewer
mutations to be detected when the drug selection pressure was not present.
Secondly, they may not be developed because viruses didn't need to have as
many mutations in their genomes to be fully resistant to the combinations of
Analysis of a large number of virus genomes after removal of drug
selection allows us to evaluate virus fitness in vivo
. Our mathematical model
showed a positive correlation between the number of mutations in the viral
genome and the loss of viral fitness. This is in agreement with our observation
that viruses with 5-6 drug-resistant mutations were rare and viruses with more
than 6 mutations were not detected in any patients. Viruses with more drug-
resistant mutations can theoretically be more advantageous for resistance to
combinations of antiretroviral drugs, but the severe fitness cost render such
viruses incapable to replicate and/or compete with other more fit viruses with
fewer mutations. When viruses with multiple drug-resistant mutations were
analyzed, we found that viral fitness was lower in viruses with MDR mutations
compared to viruses that only carried SDR mutations. Our model also showed
that the timing of the emergence WT viruses varies somewhat from patient to
patient but the average time to reach 1% WT across the three most closely
studied patients (PID811, PID908 and PID268) was 50 days.
Our study has some limitations, reflecting the source of the patient
population as a clinical cohort. Treatment interruption is no longer considered
clinically acceptable so the total number of such patients that can be studied is
limited. Patients were treated with different drug combinations and had various
previous treatment histories. The samples were collected at different time points
for each subject. The analysis was performed for select drug-resistant mutations
and the effect of potential compensatory mutations (16, 30, 39) was not
assessed. A well-controlled cohort in which a larger number of patients are
treated with the same drugs and samples are collected at similar schedules
would be ideal, but such a population is no longer feasible.
In summary, a comprehensive study of the dynamics of drug-resistant
virus populations following treatment interruption and determination of the
reservoirs for the linked MDR viruses represent important opportunities for
improved understanding of ART resistance and its management.
This work was supported by grants from the National Institutes of Health
(GM065057, AI64518, AI067854, AI28433, RR06555, P20-RR18754, and K24-
AI01608), the National Science Foundation (Grant No. NSF PHY05-51164)
portion of the work were done under the auspices of the U. S. Department of
Energy under contract DE-AC52-06NA25396.
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Detection of the M184V mutation after treatment interruption. The M184V
mutation was determined by PASS in plasma samples collected on therapy
(week -10) and off therapy (week 3 to week 32) in patient PID811. Green and red
dots indicate mutant and WT bases detected in amplified individual viral
genomes, respectively. Viral loads (VL), numbers of viral genome detected,
numbers of mutants in the viral population, and percentages of mutant viruses in
the sequenced genomes are shown above and underneath of the PASS images.
Due to limited sampling of the viral population the percentages of mutants
observed may differ from the percentage of mutants in the population (see
Evolution of drug-resistant mutations following treatment interruption.
Detected individual drug-resistant mutations were plotted as percentages (A, C,
E and G) and absolute numbers (B, D, F and H) in the viral population at each
time point in each individual. Drug-resistant mutations are indicated by unique
symbols as shown at the far right for each patient. The RTI mutations are shown
as solid lines and the PI mutations are shown as dotted lines. The WT virus and
viral load data are indicated as red triangles and black crosses, respectively. WT
viruses were not included in patient PID895 in order to show the differences
among minority drug-resistant virus populations (G and H). The G48V mutation
was not analyzed at week 11 and is not shown in the plots (C and D)
Dynamic changes of linked MDR viruses following treatment interruption.
The viral population changes over time were compared in PID811 (A), PID908
(B) and PID268 (C). The numbers of linked drug-resistant mutations in viruses
are indicated by different symbols and colors as indicated in each graph. The
population changes of select predominant viruses with linked MDR mutations
were compared in PID811 (D).
Relationship between relative fitness loss of drug-resistant mutants
compared to the WT and the numbers of drug-resistant mutations. The vertical
bars indicate the 95% confidence interval range. Note that for PID268, the error
bars are so small that they cannot be easily visualized. The dashed lines
represent the best fit for the trend of fitness loss with higher number of mutations.
Multicenter Validation of the Lupus Activity Scoring Tool (LAST) as Compared to the SELENA SLEDAI (SS) Modification M. KHRAISHI, R. ASLANOV , S. DIXIT , K. FUDGE , V. AHLUWALIA, S. KHRAISHI NEXUS Clincal Research/NL Research Technologies (NLRT), St. John's, NL; Memorial University of Newfoundland (MUN), St. John's, NL; 1960 Appleby Line, Burlington, ON;
Hematological OncologyHematol Oncol (2012)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hon.2014 Original Research Article High-dose chemotherapy in relapsed or refractory Hodgkinlymphoma patients: a reappraisal of prognostic factors E Cocorocchio1*, F Peccatori1, A Vanazzi1, G Piperno2, L Calabrese1, E Botteri3, L Travaini4, L Preda5 and G Martinelli11Haematoncology Division, European Institute of Oncology, Milan, Italy