Standardized Comparison of the Relative Impacts of HIV-1 Reverse
Transcriptase (RT) Mutations on Nucleoside RT Inhibitor
George L. Melikian,aSoo-Yon Rhee,aJonathan Taylor,bW. Jeffrey Fessel,cDavid Kaufman,dWilliam Towner,ePaolo V. Troia-Cancio,f
Andrew Zolopa,aGregory K. Robbins,gRon Kagan,hDennis Israelski,aand Robert W. Shafera
Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, California, USAa; Department of Statistics, Stanford University, Stanford, California,
USAb; Kaiser Permanente Medical Care Program—Northern California, San Francisco, California, USAc; Mount Sinai Medical Center, New York, New York, USAd;
Department of Infectious Diseases, Kaiser Permanente Los Angeles, Los Angeles, California, USAe; Division of Infectious and Immunologic Diseases, UC Davis Medical
Center, Davis, California, USAf; Division of Infectious Diseases, Harvard University, Boston, Massachusetts, USAg; and Quest Diagnostics Incorporated, San Juan Capistrano,
Each of the initial treatment regimens recommended by the De-
partment of Health and Human Services (34) and the World
an ARV belonging to a second drug class.
In a previous study, we applied several data-mining ap-
proaches to quantify associations between NRTI-associated
HIV-1 drug resistance mutations and in vitro susceptibility
data (24). About 630 susceptibility test results were available
for abacavir (ABC), didanosine (ddI), lamivudine (3TC), sta-
vudine (d4T), and zidovudine (AZT), and 350 were available
for tenofovir (TDF). In that study, we used a predefined list of
nonpolymorphic NRTI-selected mutations to reduce the number
analyze a data set that is about twice as large and uses two regres-
sion methods in tandem: one to identify genotypic predictors of
NRTI susceptibility from the many RT mutations present in the
data set (rather than relying on a predefined list of mutations, as
we did previously) and one to quantify the impact of RT muta-
tions on NRTI susceptibility. In addition, we used several ap-
proaches to determine whether models that included statistical
diction of reductions in NRTI susceptibility.
ucleoside/nucleotide reverse transcriptase (RT) inhibitors
(NRTIs) are the backbone of antiretroviral (ARV) therapy.
MATERIALS AND METHODS
HIV-1 isolates. We analyzed HIV-1 isolates in the HIV Drug Resistance
been performed by the PhenoSense (Monogram, South San Francisco,
previously by other laboratories; 65% were from studies by our research
group or from data recently contributed by one of several collaborating
the published literature previously (for a copy of the data set, see the
supplemental material). The Stanford University Human Subjects Com-
mittee approved this study.
Drug susceptibility results were expressed as the fold change in sus-
ceptibility, defined as the ratio of the 50% effective concentration (EC50)
for a tested isolate to that for a standard wild-type control isolate. EC50
results for 3TC and emtricitabine (FTC) with a fold change in suscepti-
bility of ?200 were censored (i.e., reported as ?200) by the PhenoSense
as well as for AZT, for samples which had fold change results of ?200.
The subtype of each isolate either was determined by using the REGA
subtyping algorithm (5) and the NCBI viral genotyping resource (26) or
was identified directly from the phenotype report. Mutations were de-
Received 9 August 2011 Returned for modification 16 September 2011
Accepted 3 February 2012
Published ahead of print 13 February 2012
Address correspondence to George L. Melikian, email@example.com.
Supplemental material for this article may be found at http://aac.asm.org/.
Copyright © 2012, American Society for Microbiology. All Rights Reserved.
The authors have paid a fee to allow immediate free access to this article.
0066-4804/12/$12.00Antimicrobial Agents and Chemotherapyp. 2305–2313aac.asm.org
(available at http://hivdb.stanford.edu/pages/documentPage/consensus
_amino_acid_sequences.html). Nonpolymorphic mutations were de-
fined as mutations that occur at a prevalence of ?0.5% in the absence of
ARV selective pressure (1).
To minimize bias, we excluded susceptibility results obtained when
more than one virus from the same individual contained the same muta-
tions at the following influential NRTI resistance positions: 65, 74, 115,
151, 184, and 215. Because the presence of mixtures may confound gen-
otype-phenotype correlations, we also excluded viruses with sequences
containing electrophoretic mixtures at these positions.
tibility. To identify mutations that decrease susceptibility to one or more
NRTIs, we used the LASSO (least absolute shrinkage and selection oper-
ator) procedure to examine all mutations occurring in 10 or more virus
samples. LASSO is particularly useful for selecting a subset of predictors
by fitting a least-squares solution with the added constraint that ? |?i|1
(the L1norm of the parameter vector) be ?s, where s is a regularization
parameter determined by cross-validation. During cross-validation,
LASSO used four-fifths of the data for selecting a model and one-fifth for
5-fold cross-validation was repeated 10 times to estimate the variance in
to decide when to stop adding variables to the model. The regularization
parameter—the LASSO penalty used to identify the optimal number of
explanatory features—was chosen as the smallest parameter whose mean
cross-validation error was less than or equal to the minimum cross-vali-
dation error plus 1 standard deviation of the cross-validation error at the
minimum. The dependent variable was the log10fold change in HIV sus-
ceptibility. Each of the regression coefficients represented an HIV-1
amino acid mutation. LASSO coefficient means that were more than 3
standard deviations above or below zero after 10 repeated runs of 5-fold
ceptibility to NRTIs.
To quantify the effect of the LASSO-selected mutations on NRTI sus-
ceptibility, we used least-squares regression (LSR). For this regression
analysis, we also used 5-fold cross validation and 10-fold repetition to
estimate the variance among the fitted coefficients. Seven LSR models—
one for each NRTI—were created. In these models, each of the selected
mutations was an explanatory variable and the log of the fold change in
susceptibility was the response variable. For each 5-fold cross-validation,
80% of data was used for learning regression coefficients and 20% was
zero in the 10 repeated runs of 5-fold cross-validation were considered
statistically significant predictors of susceptibility to NRTIs.
Regression analyses (for both the LASSO and LSR models) were stan-
dardized by scaling the log fold distributions for each of seven NRTIs to a
distribution with a standard deviation of 1. Standardizing the regression
tibility ranges among the NRTIs. Consequently, the regression coeffi-
cients reflect the standard deviation change in log fold associated with
each specific mutation (rather than the actual log fold difference).
Contribution of NRTI mutations to decreased susceptibility. Pre-
diction accuracy was evaluated using continuous and categorical ap-
proaches. The continuous approach involved calculating the mean
squared error (MSE) between the actual and predicted standardized log
fold change in susceptibility. The categorical approach involved deter-
mining how often the predicted phenotype correlated with one of three
predefined susceptibility categories: susceptibility, low/intermediate re-
sistance, and high-level resistance. The predefined susceptibility catego-
(24). They were chosen to approximate the geometric mean of the pub-
lished estimated clinical cutoffs provided with the PhenoSense reports.
For AZT, 3TC, and FTC, an isolate with ?3-fold-decreased susceptibility
was considered susceptible; an isolate with 3- to 25-fold-decreased sus-
ceptibility was considered to exhibit low/intermediate resistance; and an
isolate with ?25-fold-decreased susceptibility was considered highly re-
indicate susceptibility; 1.5- to 3.0-fold resistance was considered low/in-
termediate resistance; and ?3.0-fold resistance was considered a high
susceptibility; 2- to 6-fold resistance was considered low/intermediate re-
sistance; and ?6-fold resistance was considered a high level of resistance.
Mutational interactions. We used four approaches to investigate
whether models with interactions improved the prediction of in vitro
plored interactions among the mutations identified by LASSO (31). (ii)
Multivariate adaptive regression splines (MARS) progressively tune the
maximum allowed interaction constraint parameter mi from 1 to 3 (8).
(iii) We extended our LSR by including the stepwise addition of interac-
tified as significantly covarying in a previous study (23). (iv) We con-
ducted an exhaustive search of all potential two-way interactions among
the LASSO-identified mutations by constructing a variable interaction
matrix that included all possible two-way interactions in addition to each
specific regression model using this larger interaction matrix. Cross-vali-
dation was used in both stages to minimize overfitting.
Summary of NRTI susceptibility analysis results. Phenotypic
susceptibility results were available for 1,739 HIV-1 isolates from
1,478 individuals. These included 1,687 clinical isolates and 52
laboratory clones or site-directed mutants. To reduce bias result-
ing from individuals who had more than one virus tested, we
excluded from our analysis 228 viruses from individuals with
more than one virus having the same mutations at each of the
following NRTI resistance positions: 65, 74, 115, 151, 184, and
215. To reduce the confounding effect of virus populations con-
taining mixtures of two or more residues at the same position, we
excluded 256 isolates with electrophoretic mixtures at the same
Among the 1,273 isolates included in our analysis, more than
1,100 susceptibility results were available for 3TC, ABC, AZT,
d4T, and ddI, 952 for TDF, and 577 for FTC. Overall, 45% of
results met the predefined criteria for susceptibility; 28% met
those for low/intermediate resistance; and 26% met those for
each susceptibility category for each of the seven NRTIs. Of the
1,273 isolates, 98.2% belonged to subtype B. Isolates were ob-
tained between 1995 and 2011 (median year: 2003; interquartile
range, 2000 to 2007).
of NRTIs by showing the correlation of the standardized log fold
change in susceptibility for each pair of NRTIs. The two cytidine
second and third highest correlations were those between the
two thymidine analogs AZT and d4T (r ? 0.83) and between
AZT and TDF (r ? 0.83). Extremely low correlations were
present between the standardized log fold susceptibilities to
TDF and 3TC (0.02), TDF and FTC (0.04), AZT and 3TC
(0.11), and AZT and FTC (0.22).
NRTI resistance mutations and their effects on specific
Melikian et al.
aac.asm.orgAntimicrobial Agents and Chemotherapy
positions as significant predictors of decreased susceptibility to
K43E, K64H, K65R, D67N, T69ins, K70R, L74V, V75T, F77L,
R83K, A98G, K102Q, Y115F, V118I, I135T, Q151M, M184V/I,
E203D, H208Y, L210W, T215F/Y/D, D218E, and K219R. To
quantify the contribution of the LASSO-identified mutations to
each of the seven NRTIs. M184I, which was present in 16 patient
samples, was combined with M184V in our analysis. T69ins in-
cludes a variety of different double amino acid insertions at this
nificantly associated with reduced susceptibility to at least one
NRTI in the LSR model. The complete list of regression coeffi-
in Table S1 in the supplemental material.
The median number of LASSO-identified mutations per sam-
highly correlated with the prevalence of these mutations in se-
in the Stanford HIV Drug Resistance Database (Pearson’s r, 0.99;
P, ?0.001) (22) (Fig. 3).
LSR models) were those for K65R, T69ins, Y115F, Q151M, and
K70R, L74V, V75T, F77L, and T215F/Y (between 0.5 and 1.0).
K64H, which was present in only 16 and 13 isolates undergoing
d4T and TDF susceptibility testing, had standardized regression
coefficients for these two drugs of 0.63 (95% confidence interval
[95% CI], 0.629 to 0.631) and 1.17 (95% CI, 1.164 to 1.176),
M184V/I, and T215F/Y each had coefficients of ?0.5 for four
susceptibility to TDF and AZT; and K65R was associated with
increased susceptibility to AZT.
Four of the 28 mutations associated with decreased NRTI sus-
ceptibility were polymorphic in one or more group M subtypes,
including K43E, V118I, I135T, and E203D.
Least-squares regression prediction performance. Table 2
summarizes the categorical and continuous prediction perfor-
The categorical performance, or classification accuracy, was the
proportion of isolates for which the regression model correctly
the three predefined susceptibility categories: susceptible, exhib-
iting low/intermediate resistance, or highly resistant. The classifi-
cation accuracies ranged from 0.77 for ddI, 0.78 to 0.82 for ABC,
AZT, TDF, and d4T, and 0.92 to 0.94 for 3TC and FTC. The
predictions and actual results were completely discordant (i.e.,
susceptible versus highly resistant) for about 0.5% of tests (range,
0.26% for ABC to 0.96% for TDF) and partially discordant (i.e.,
intermediate versus susceptible or intermediate versus highly re-
sistant), on average, for 13% of tests (range, 5.3% for FTC to
22.5% for ddI) (see Table S2 in the supplemental material).
The standardized log fold MSE of 50 trials (5-fold cross-vali-
dation performed 10 times) per NRTI ranged from 0.08 (FTC) to
(TDF) (Table 2).
NRTI mutation interactions. None of the four approaches
that incorporated mutational interactions (that is, evaluation for
nonlinear effects, such as synergy or antagonism, in NRTI resis-
(DSA) partitioning algorithm, multivariate adaptive regression
splines (MARS), extension of LASSO to include subsets of previ-
ously identified covarying mutations, and extension of LASSO to
include all pairwise interactions—improved the accuracy of pre-
diction of reductions in NRTI susceptibility over that with their
respective noninteraction versions. Although several models
identified pairs of mutations (e.g., T69ins plus T215Y, F77L plus
Q151M, and K65R plus Q151M) that interacted synergistically to
reduce NRTI susceptibility, these isolated effects did not result in
an overall improvement in prediction accuracy and therefore did
not justify the use of a complex interaction model.
poration into the HIV-1 primer DNA strand and those that pro-
mote the excision of chain-terminating NRTIs via ATP-mediated
pyrophosphorolysis. K65R, K70E, L74V, F115Y, M184V/I, and
Q151M plus the Q151M-associated mutations (A62V, V75I,
F77L, and F116Y) inhibit NRTI incorporation; whereas M41L,
D67N, K70R, L210W, T215Y/F, K219Q/E, and the amino acid
T69ins promote NRTI excision. M41L, D67N, K70R, L210W,
T215Y/F, and K219Q/E are called thymidine analog mutations
(TAMs) because they are selected primarily by the thymidine an-
alogs AZT and d4T. The TAMs have been subclassified into two
overlapping clusters: type I (M41L, L210W, and T215Y) and type
II (D67N, K70R, T215F, and K219Q/E) TAMs. The mechanisms
of action of two additional mutations, T69D and V75T, which
were reported in the 1990s to reduce susceptibility to ddC and
d4T, respectively (6, 14, 29), have been less well characterized.
With the analysis of increasingly large databases, many addi-
TABLE 1 Numbers of HIV-1 isolates with genotype-phenotype
correlations for each of the seven NRTIs by predefined resistance
No. (%) of isolatesb:
Total no. of
3,460 (45.5) 2,155 (28.3)
aNRTI, nucleoside reverse transcriptase inhibitor; 3TC, lamivudine; ABC, abacavir;
AZT, zidovudine; d4T, stavudine; ddI, didanosine; TDF, tenofovir; FTC, emtricitabine.
HIV-1 Reverse Transcriptase Mutations
May 2012 Volume 56 Number 5aac.asm.org 2307
FIG 1 Phenotypic correlation matrix showing standardized HIV-1 log fold cross-resistance between each pair of the seven NRTIs. The Pearson correlation
coefficients (r) for each of the 21 NRTI pairs are shown. ***, P ? 0.3; in all other cases, P ? 0.0001. Drug abbreviations: 3TC, lamivudine; ABC, abacavir; AZT,
zidovudine; D4T, stavudine; DDI, didanosine; TDF, tenofovir; FTC, emtricitabine.
Melikian et al.
aac.asm.orgAntimicrobial Agents and Chemotherapy
FIG 2 Regression coefficients of the RT mutations found to be significantly associated with decreased susceptibility to at least one NRTI in the least-squares
regression models. The mutations shown occurred at least 10 times in the data set. Positive coefficients represent mutations that decrease drug susceptibility;
in standard deviation units) for the log fold distribution of the respective NRTI. The error bars indicate the standard deviation of the mean generalized error,
deviations from zero are blue; other coefficient bars are gray, indicating a lack of statistical significance after cross-validation. Drug abbreviations: 3TC,
lamivudine; ABC, abacavir; AZT, zidovudine; D4T, stavudine; DDI, didanosine; TDF, tenofovir; FTC, emtricitabine.
HIV-1 Reverse Transcriptase Mutations
May 2012 Volume 56 Number 5aac.asm.org 2309
tional NRTI-selected mutations have been identified and in some
cases have been shown to decrease NRTI susceptibility. Several of
D/H (2, 4, 11, 25, 30, 37). Others are at novel positions in the 5=
polymerase coding domain: E40F, K43E/Q/N, E44D/A, V118I,
E203K, H208Y, D218E, K223Q/E, and L228H/R (9, 12, 33) (13,
30). Finally, several mutations 3= to the polymerase coding do-
main facilitate nucleotide excision, presumably by slowing enzy-
matic translocation, allowing more time for nucleoside reverse
of these mutations, N348I (10, 39), was not evaluated in our
study, because it lies outside the RT region that is tested by the
Methodological innovations and prediction accuracy. It has
been difficult to determine the phenotypic impact of RT muta-
TABLE 2 Predictive accuracy and standardized MSE of LSR modelsa
a3TC, lamivudine; ABC, abacavir; AZT, zidovudine; d4T, stavudine; ddI, didanosine;
TDF, tenofovir; FTC, emtricitabine.
bProportion of isolates for which the regression model correctly predicted whether the
phenotype was within the bounds of one of the three predefined NRTI susceptibility
categories: susceptible, with low/intermediate resistance, or highly resistant. Values in
parentheses denote standard deviations.
cMean squared error between actual and predicted phenotypes. Phenotypes have been
standardized to zero mean and unit variance, such that predicted values reflect standard
deviation units. Values represent means (with standard deviations in parentheses)
derived from 10 repeated and independent runs of 5-fold cross-validation.
set) and that in the genotype-phenotype data set.
Melikian et al.
aac.asm.orgAntimicrobial Agents and Chemotherapy
terns. Moreover, the NRTIs have highly variable in vitro dynamic
susceptibility ranges (i.e., the fold difference in EC50between
highly drug resistant and wild-type viruses). The EC50s of AZT,
3TC, and FTC for highly resistant viruses are usually more than
100 times higher than those for wild-type viruses. In contrast, the
EC50s of d4T, ddI, and TDF for highly resistant viruses are rarely
more than 5 times higher than those for wild-type viruses. None-
theless, reductions in susceptibility with EC50s as low as 1.5 times
higher than that of the wild type are clinically significant for d4T,
ddI, and TDF. The dynamic range for ABC is slightly higher than
that for d4T, ddI, and TDF.
To facilitate the comparability of a mutation’s effect on differ-
able (log fold change in HIV susceptibility) by its variance. This
provides the ability to assess the relative influences of mutations
on decreased susceptibility even for those NRTIs with narrow dy-
namic ranges. We also chose to study only those phenotypes per-
formed by PhenoSense because of the greater reproducibility of
this assay for NRTIs with narrow dynamic ranges (40).
The overall classification accuracy for 3TC, ABC, AZT, d4T,
ddI, and TDF was 81.5%, compared with 80.0% in our previous
2006 analysis (24). The classification accuracy improved by
?3.0% for 3TC and ABC and by about 1.0% for the remaining
NRTIs. The standardized MSE for these six NRTIs also improved
0.24 to 0.20 over all NRTIs. The rather modest improvement in
prediction accuracy despite the increase in the number of geno-
vious 2006 study most likely resulted from the ways in which the
variables by including nonpolymorphic mutations that had pre-
viously been shown to be selected by NRTI therapy. In this study,
we made no prior assumptions about the mutations and used the
LASSO algorithm—which is particularly useful for selecting a
Although the LASSO algorithm is parsimonious, 18 muta-
tions—particularly those with the greatest regression coeffi-
to one or more NRTIs in the current and 2006 studies: M41L,
K43E, K65R, D67N, T69ins, K70R, L74V, V75T, Y115F, Q151M,
M184V/I, H208Y, L210W, T215F/Y, D218E, and K219R. In con-
trast, K43N/Q, V75M, F116Y, E203K, and L228H were signifi-
cantly associated with decreased susceptibility only in the 2006
study, whereas E40F, K64H, F77L, A98G, V118I, I135T, and
E203D were significantly associated with decreased susceptibility
only in the current study.
The fact that regression models containing interaction terms
did not significantly improve prediction accuracy suggests that
most interactions among NRTI resistance mutations are additive
rather than multiplicative. Although a small number of muta-
F77L, and Q151M), we did not test models that used only prese-
lected mutation pairs. Models that include interactions may not
highly correlated mutations may have multiplicative effects, the
numbers of samples in which each of the two mutations occurs
alone may be insufficient to demonstrate an interaction. Interac-
tions may also be difficult to observe if some of the independent
For example, as noted in the following section, several additional
mutations frequently occurred in combination with M41L,
it difficult to identify multiplicative effects among the three type I
(i) Known NRTI resistance associations.Our results are consistent
therapy trials that reported associations between preexisting NRTI
mutations and the virological response to a new NRTI (15, 17, 18),
and numerous studies of individual NRTI resistance mutations. We
3TC or FTC, ABC, and ddI and increases susceptibility (in descend-
ing order) to TDF, AZT, and d4T. We showed that D67N and
K219Q/E are the TAMs with the least effect on NRTI susceptibility.
K219E yielded small regression coefficient values. In contrast, the
Y115F, a mutation discovered for its contribution to ABC re-
sistance, was also found to decrease susceptibility to TDF signifi-
cantly—a finding that has been reported previously (32, 35) but
that V75T reduced susceptibility to d4T noted that V75T reduced
susceptibility to ddI (14). However, this association has not gen-
erally been cited. In contrast, our results indicate that V75T ap-
pears to contribute as much to reduced susceptibility to ddI as it
does to reduced susceptibility to d4T.
Despite the finding that most mutations were associated with
levels of resistance between AZT and 3TC, AZT and FTC, TDF
which was reported previously by Whitcomb et al. (36), results
from the fact that the most common NRTI resistance mutation,
M184V, which causes reduced susceptibility to 3TC and FTC, in-
creases susceptibility to AZT and TDF. This mutational interac-
tion likely explains the clinical efficacy of NRTI backbones con-
3TC or FTC. However, not all efficacious dual NRTI backbones
highly effective under most circumstances despite the fact that
M184V decreases susceptibility to both NRTIs. The effectiveness
of this combination may result from the fact that ABC has the
greatest antiviral activity except for the cytidine analogs (27).
loads in a recent large clinical trial (28).
previously reported but poorly characterized NRTI-associated
mutations, were associated with significantly decreased suscepti-
bility to six and seven NRTIs, respectively. This association ap-
pears to be the result of each mutation’s strong correlation with
HIV-1 Reverse Transcriptase Mutations
May 2012 Volume 56 Number 5aac.asm.org 2311
11 (84%) also had M41L, L210W, and T215Y. Among the 49 pa-
tients with viruses containing K219R, 41 (84%) also had the same
three type I TAMs. In contrast, 26% of all viruses in the study had
each of the three type I TAMs.
K64H, K64N, and K64Y are nonpolymorphic mutations that
are strongly selected by NRTI therapy (22, 30). Each of these K64
variants was recently reported to occur in ?0.1% of 12,730 ARV-
a history of receiving NRTIs but not NNRTIs (30). In the current
study, K64H was significantly associated with decreased suscepti-
bility to d4T (16 patients; regression coefficient, 0.63) and TDF
(13 patients; regression coefficient, 1.2). K64H occurred in com-
clones with the less frequently detected mutations K64N and
K64Y from one isolate each. Susceptibility testing of the six iso-
(range, 1.3- to 1.6-fold) and 1.3-fold (range, 1.2- to 1.8-fold) de-
creased susceptibility to d4T and TDF, respectively. K64N in-
duced 2.4-fold and 1.4-fold decreased susceptibility to d4T and
TDF, respectively. K64Y induced 2.1-fold and 1.8-fold decreased
susceptibility to d4T and TDF, respectively. Further studies of
viruses with these mutations are ongoing.
A98G was first reported to reduce susceptibility to several
A98G was selected by NRTIs as well as NNRTIs, because it oc-
curred in 25 (0.2%) of 12,370 ARV-naïve patients, 97 (2.1%) of
4,598 patients treated with NRTIs but not NNRTIs, and 711
(8.5%) of 8,367 patients treated with NNRTIs (usually in combi-
nation with NRTIs) (30). The most likely explanation for the as-
sociation with slightly decreased susceptibility to AZT and TDF
was that 42/68 (62%) of viruses with A98G also had M41L,
L210W, and T215Y. The only other NNRTI mutation shown to
influence NRTI susceptibility was Y181C, which, as previously
Conclusion. Initial and salvage ARV therapies have become
increasingly effective in well-resourced countries. Potent ARVs
from five mechanistic classes are now routinely used in combina-
tion with NRTIs. It has therefore become increasingly difficult to
assess the impact of baseline NRTI resistance mutations on the
response to an NRTI used as part of a salvage therapy regimen.
Therefore, correlations between RT mutations and in vitro NRTI
susceptibility are increasingly important for quantifying the ef-
fects of NRTI mutations on susceptibility to NRTIs.
Our study provides a comprehensive yet fine-grained view of
were standardized by the variance in the log fold resistance levels
for each NRTI, we provide the first analysis that quantifies the
relative phenotypic effect of each mutation across each of the
assess the potential roles of many different RT mutations, the
NRTI resistance mutations we identified with the greatest effect
on NRTI susceptibility were for the most part known nonpoly-
morphic treatment-selected mutations. Although one of these
selection pressure (30), and site-directed mutagenesis experi-
ments were consistent with our regression model. For several
other mutations, novel associations with decreased susceptibility
to specific NRTIs were identified and in some cases explained by
their association with other, more common NRTI resistance mu-
The study was supported by funding from two grants: AI068581 (NIH)
and CHRP D08-ST-033.
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HIV-1 Reverse Transcriptase Mutations
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