Nucleic Acids Research Advance Access originally published online on November 15, 2006
Nucleic Acids Research 2007 35(Database issue):D371-D375; doi:10.1093/nar/gkl855
Nucleic Acids Research, 2007, Vol. 35, Database issue D371-D375
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
The HIV positive selection mutation database
Calvin Pan1,
Joseph Kim,
Lamei Chen2,
Qi Wang1 and
Christopher Lee1,2,*
Center for Computational Biology, University of California Los Angeles, CA, USA
1 Molecular Biology Institute, Institute for Genomics and Proteomics, University of California Los Angeles, CA, USA
2 Department of Chemistry and Biochemistry, University of California Los Angeles, CA, USA
*To whom correspondence should be addressed. Tel: +1 310 825 7374; Fax: +1 310 206 7286; Email: leec{at}chem.ucla.edu
Received August 16, 2006. Revised October 9, 2006. Accepted October 10, 2006.
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ABSTRACT
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The HIV positive selection mutation database is a large-scale
database available at
http://www.bioinformatics.ucla.edu/HIV/ that provides detailed selection pressure maps of HIV protease
and reverse transcriptase, both of which are molecular targets
of antiretroviral therapy. This database makes available for
the first time a very large HIV sequence dataset (sequences
from
50 000 clinical AIDS samples, generously contributed by
Specialty Laboratories, Inc.), which makes possible high-resolution
selection pressure mapping. It provides information about not
only the selection pressure on individual sites but also how
selection pressure at one site is affected by mutations on other
sites. It also includes datasets from other public databases,
namely the Stanford HIV database [S. Y. Rhee, M. J. Gonzales,
R. Kantor, B. J. Betts, J. Ravela and R. W. Shafer (2003)
Nucleic Acids Res.,
31, 298–303]. Comparison between these datasets
in the database enables cross-validation with independent datasets
and also specific evaluation of the effect of drug treatment.
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INTRODUCTION
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The HIV-1 virus is the causative agent of AIDS, a growing worldwide
epidemic and also a fascinating system for studying fundamental
scientific questions. For example, one major clinical problem
in the treatment of AIDS is HIV's ability to develop resistance
to antiviral drugs rapidly, often within weeks of introduction
of a new drug (
1–
3). Foremost among the factors responsible
for this are the virus' extremely high mutation rate (
4,
5) and
replication rate (
3,
6–
8). For this reason, there is great
medical interest in understanding both the specific causes of
drug resistance, and predicting fast versus slow evolutionary
pathways to multiple drug resistance. At the same time, HIV
provides an extraordinary wealth of data about fundamental scientific
questions such as the fitness landscape for protein evolution
(
9,
10).
Evolutionary biology has developed a powerful and general approach for investigating such problems: metrics of selection pressure that measure whether a particular genetic change is selected for or against during evolution. Such metrics can reveal important selection forces either constraining or driving evolution of a protein, directly from raw sequence variation data (11,12). One very widely used metric of selection pressure on amino acid mutations is known as Ka/Ks or dn/ds (13,14) and measures the ratio of observed amino acid mutations over observed synonymous mutations, normalized by the ratio expected under a neutral model. Thus a Ka/Ks = 1 value indicates neutral selection. Ordinarily Ka/Ks is << 1, indicating negative selection against amino acid mutations (far fewer observed than expected under a neutral model). Ka/Ks > 1 is referred to as positive selection (i.e. amino acid mutations increase reproductive fitness) and is observed in rare cases where new evolutionary challenges create strong pressure for rapid evolution of a protein (e.g. immune system genes like MHC that are involved in recognizing pathogenic antigens). Ordinarily, a single Ka/Ks value is calculated for a whole gene, but with very large datasets it becomes possible to estimate distinct Ka/Ks values for individual codon positions or amino acid mutations. This yields a ‘selection pressure map’ of a gene, revealing its detailed functional constraints and in rare cases positive selection peaks that signal important new evolutionary pressures such as drug treatment. We used Ka/Ks because it provides a powerful tool for detecting positive selection. Phylogenetic analysis of our HIV sequence dataset using Phylip (15) shows a star-like topology (data are available at www.bioinformatics.ucla.edu/HIV/topo.png, but will be presented in detail elsewhere), in agreement with previous studies (16,17).
We have assembled a large-scale database that provides researchers detailed selection pressure maps of HIV proteins involved in drug resistance. These data have many possible applications, including prediction of mutations contributing to drug resistance, distinguishing primary drug resistance mutations from accessory mutations, rate measurements of fast versus slow evolutionary pathways to multiple drug resistance, and the evolutionary dynamics of different types of mutations as the virus moves from untreated to drug-treated conditions and back. This database makes available for the first time a very large HIV sequence dataset (sequences from 50 000 clinical AIDS samples), which makes possible high-resolution selection pressure mapping, as well as smaller datasets from other public databases. The methods and most of the data described herein have been published previously (12,18).
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DATABASE CONTENT, INTERFACE AND APPLICATIONS
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Datasets
The primary dataset consists of sequences for HIV protease and
reverse transcriptase (RT) for
50 000 clinical AIDS patient
samples from the United States, collected during 1999–2003
(
12), and mostly under drug treatment. These data cover 1.4
kb each [300 000 chromatograms; six overlapping reads per sample,
including both strands; see (
12) for details] and were generously
contributed by Specialty Laboratories Inc. Owing to HIV's high
mutation rate, on average each sequence contains 32 mutations/kb
[with respect to the Los Alamos reference sequence (
12)], for
a total of more than 2 million mutation observations in the
dataset (
12). Over 5000 distinct codon mutations were observed,
each with an average count of 364 samples (
12). For comparison,
this density of polymorphism information is equivalent to sequencing
1 million people. This very large dataset, made available publicly
for the first time, has made detailed selection pressure mapping
possible. Of the samples, 99.3% are subtype B; non-subtype-B
samples were excluded from the analysis (
12). The dataset is
fully HIPAA-compliant; all information concerning the source
patients was removed by Specialty.
The database currently includes two additional datasets, also covering HIV protease and RT. These datasets were obtained from the Stanford HIV database (19). The Stanford-Treated dataset consists of 1797 subtype B samples with known drug treatments. This dataset provides a useful comparison with the Specialty results, for validating whether a specific mutation is reproducibly selected by drug treatment. The Stanford-Untreated dataset consists of 2628 subtype B samples not under drug treatment. By comparing results from this dataset with Specialty and Stanford-Treated, users can assess whether a specific mutation is more likely to be associated with drug resistance or other types of phenotypic fitness effects (e.g. interactions with the immune system).
The Specialty raw sequence data are available as a gzip'ed FASTA file at http://www.bioinformatics.ucla.edu/HIV/Specialty_sequences.fasta.gz.
Amino acid selection pressure mapping
The first aspect of the database is mapping of Ka/Ks selection pressure at each codon position in HIV protease and the first 381 codon positions of RT (Figure 1). Further positions in RT were not sequenced in this dataset. Codon-specific selection pressure (12) was calculated using the following formula:
where
Na and
Ns are the number of amino acid
mutations and synonymous mutations observed at the codon,
na,t is the number of possible transition mutations in the codon
that would change the resulting amino acid,
ns,t is the number
of possible transition mutations that are synonymous,
na,v and
ns,v are the equivalent numbers for transversions, and
ft and
fv are the transition and transversion frequencies, respectively.
We calculated an LOD confidence score for a codon to be under
positive selection pressure according to the following formula:
where
N is the total number
of mutations observed in the codon and
q is calculated as follows:
This analysis includes
Ka/
Ks values for 2946 individual amino acid mutations (
12) at 399
codon positions with LOD scores >2. These data have many
applications. For example, strong positive selection (
Ka/
Ks > 1) indicates drug-resistance mutations or important fitness
effects. Experimental validation data in HIV protease (where
causes of drug resistance are well characterized) showed that
19 of 23 known drug resistance codons were correctly predicted
by our database, which also accurately predicts the mutant enzyme's
activity phenotype (
12,
20). Of the 47 positively selected sites
found in the Specialty dataset, 28 were also found in the Stanford-Untreated
dataset, possibly indicating that those sites can harbor fitness
mutations (
18). The database has a simple graphical interface
(
Figure 1): users can peruse the codon-position selection pressure
map directly, click on a position, and inspect detailed tabular
results grouped either by codon position, individual amino acid
mutations or individual nucleotide mutations (e.g. to see whether
two different nucleotide mutations producing the same amino
acid replacement show the same
Ka/
Ks value).
View larger version (11K):
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[in a new window]
[Download PowerPoint slide]
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Figure 1 The interface to the positive selection mutation database is a clickable imagemap. Clicking on any codon position performs a query and returns the results in an easy-to-read format. (Specialty dataset is shown.)
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Selection pressure interaction mapping
The massive size of the Specialty dataset makes it possible
to measure how selection pressure for one amino acid mutation
Y is affected by amino acid mutations at other sites
X. Specifically,
the database computes
Ka/
Ks for mutation
Y conditioned on the
presence of amino acid mutations at site
X versus the absence
of any mutation at site
X. This ‘conditional
Ka/
Ks’
(
18) calculation is performed as follows:
where
and
are the numbers of amino acid mutations
and synonymous mutations at site
Y observed in the presence
of amino acid mutations at site
X and all other variables retain
their previous definitions. Dividing this result by the one
obtained in the absence of any mutation at site
X to arrive
at the ‘conditional selection ratio’ (
18) results
in the following expression:
where
and
are the numbers of samples containing either an
amino acid mutation or synonymous mutation at
Y and no mutation
at
X. The LOD score by which we evaluated the significance of
apparent positive conditional selection was calculated using
the following:
where
and
q as defined above. For
experimental validation, this database correctly predicted 80
of 92 known mutation positive interaction pairs identified in
HIV protease by independent experimental studies (
P-value =
10
–70) (
11,
18). The database again provides a graphical
interface (
Figure 2) as a 2D heatmap showing all pairwise interactions,
which users can click at any position to inspect detailed tabular
results.
View larger version (28K):
[in this window]
[in a new window]
[Download PowerPoint slide]
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Figure 2 Selection pressure interaction map. The degree to which a mutation at one site X (horizontal axis) affects the selection pressure at another site Y (vertical axis) is shown as the condtional selection ratio for all amino acid mutations at site Y conditioned on any amino acid mutation at site X. The color coding scale indicates increasing values of positive conditional selection ratio. Interactions showing conditional selection ratios >1 (positive conditional selection) with LOD scores >3 are shown, with blue indicating stronger interactions and yellow indicating weaker ones. Clicking any particular square provides details on the numbers used in the calculation.
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These data can yield useful insights into HIV drug resistance.
For example, the data show a significant interaction between
protease site 90 (a known drug resistance mutation site) and
site 10 (
Figure 3). Amino acid mutations at 90 displayed strong,
unconditional positive selection, indicating that they directly
cause drug resistance. In contrast, mutations at 10 are negatively
selected in the absence of the 90 mutation, but become positively
selected in the presence of the 90 mutation (
Figure 3). These
results closely match previous experimental studies showing
that mutations at 90 cause drug resistance, while mutations
at 10 have an accessory effect of compensating for the destabilizing
effect of mutations at 90 (
21). Thus, our database can help
users by providing information that can distinguish primary
drug-resistance mutations from accessory mutations (
18). Users
can navigate through links on every result page, to see mutations
that strongly select for a given mutation, mutations that are
strongly selected for by this mutation, or links to the Stanford
(
22) and Los Alamos HIV databases (
23) giving further information
about mutations at this site.
View larger version (11K):
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[in a new window]
[Download PowerPoint slide]
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Figure 3 For the two possible pathways from wild-type protease to the 10/90 double mutant, we computed the conditional Ka/Ks values for each mutation conditioned on the presence or absence of the other mutation (shown as numbers next to each edge in the figure). For example, in the absence of the 10 mutation, the 90 mutation shows strong positive selection in both the Specialty and Stanford-Treated datasets, but was negatively selected in the Stanford-Untreated dataset. Since the steady-state speed of a multistep path is determined by its slowest step, we highlighted the rate-limiting step in each path (boldface). For example, in the Specialty dataset, the steady-state rate of the upper pathway appears to be 10-fold faster than that of the lower pathway. (a) Specialty dataset, (b) Stanford-Treated dataset and (c) Stanford-Untreated dataset.
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Comparison between the independent datasets in the database
can shed additional light on such questions. For example, users
can assess whether positively selected mutations in the Specialty
dataset are really due to drug resistance, by comparing with
the Stanford-Treated and Stanford-Untreated datasets. As shown
in
Figure 3b and c, the Stanford-Treated data strongly corroborate
the Specialty result, while the Stanford-Untreated data show
that 90 is indeed involved in drug resistance; it becomes strongly
negatively selected in the absence of drug treatment. These
data can help users distinguish genuine drug-treatment mutations
from those that affect phenotype in other ways, e.g. interactions
with the host immune system. Detailed analysis of these datasets
demonstrates that the
Ka/
Ks results are highly reproducible:
independent datasets from different sets of patients show strong
quantitative agreement (
18).
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FUTURE ADDITIONS
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We are currently working to add new data and features to the
database. We will add a number of new datasets to the database.
First, we will add data for additional HIV genes, such as the
env gene, which is important for HIV immune evasion (
24); although
these datasets have smaller numbers of sequences, our analysis
has shown that useful
Ka/
Ks mapping information can be obtained
from such counts. Second, we will analyze mutation data from
patients under specific drug-treatment to compare selection
pressures caused by different drugs. Third, we will add datasets
for other HIV subtypes (e.g. subtype C) to reveal, where selection
pressure patterns appear to be consistent with those seen in
subtype B (allowing diagnostic criteria from subtype B to be
applied to other subtypes) versus where there are important
differences. Fourth, we will add a new very large dataset for
the Hepatitis C core gene, consisting of approximately 60 000
samples, generously donated by Specialty Laboratories. Lastly,
we will add new analyses and graphical interfaces to the database,
including phylogenetic analysis and clickable pathway diagrams.
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ACKNOWLEDGEMENTS
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Funding to pay the Open Access publication charges for this
article was provided by NIH Grants U54 RR021813 entitled Center
for Computational Biology (CCB) and T32-HG002536.
Conflict of interest statement. None declared.
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ABSTRACT
INTRODUCTION
