mapping evolutionary pathways of hiv-1 drug resistance using … · 2008. 1. 24. · selection...
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Mapping evolutionary pathways ofMapping evolutionary pathways ofHIV-1 drug resistance usingHIV-1 drug resistance using
conditional selection pressureconditional selection pressure
Christopher Lee, UCLA
HIV-1 Protease and RT:anti-retroviral drug targets
• Protease: responsible for the post-translational processing of the viralpolyproteins to yield the structural proteinsand enzymes of the virus
• Reverse transcriptase (RT): responsiblefor DNA polymerization
protease RT
Selection Pressure Mapping: Build anAtlas of HIV Evolution
• Selection pressure measures whether an amino acidmutation is selected for (Ka/Ks>1) or against(Ka/Ks<1) by evolution, vs. synonymous mutations.
• Dataset: sequencing of 50,000 HIV clinical samplesby Specialty Labs. Inc. 30-fold higher density ofpolymorphism information than human sequences.
• Goal: construct a selection pressure map of how HIVis evolving, where the virus is “going”, to evade ourdrugs.
Calculating Ka / Ks per Residue
vvstts
vvatta
s
a
s
a
fnfn
fnfn
N
N
K
K
,,
,,
+
+=
observed #amino acid changes vs.synonymous mutations at this codon
expected #amino acid changes vs.synonymous mutations at this codon
( ) iNiN
Nis
aa qq
i
N
K
KqNNipLOD
a
!
=
!""#
$%%&
'!==(!= ) 1log)1,,|(log 1010
Confidence that Ka/Ks>1 is statistically significant: log-odds score
LOD 2 means 99% confidence, LOD 3 means 99.9% confidence.
Maximum Likelihood estimation of Ka/Ks using PAML takes intoaccount inferred phylogeny, ft/fv ratio, mutation rates, etc.
(b) Selection Pressure for HIV-1 Protease
0
5
10
15
20
25
30
35
40
45
50
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97
Codon Position
Ka/K
s
Positive selection mapping automatically discovers causes of drug resistance!
HIV Protease Positive SelectionChen, Perlina & Lee, J. Virol. (2004)
Positive Selection Mapping IdentifiesDrug Resistance Mutations
• Correctly identified 19 of 22 knowndrug resistance mutation positions.
• Compared with multi-year researchprocess (clinical, biochemical,genetics) that was previouslyrequired, this analysis is completelyautomatic; works directly on outputfrom sequencing machines.
Build a Reaction Rate Diagram of HIV’sGlobal Evolution
• A network diagram of the rates oftransition between all possiblegenotypes. Ka/Ks is proportional torate of increase of a mutation.
• Shows the speeds of all possiblepaths of evolution the viralpopulation will follow, under thepressure of current drug treatments.
Selection Pressure is like anEvolutionary Velocity
For wildtype, synonymous mutant, and amino acid mutant allelefrequencies fo=1, fs=0, fa=0 initially, equal amino acid and synonymousmutation frequency λ, and reproduction rates ro, ra, after one unit of time
!
fo " ro fo 1# 2$( ); fs " ro$fo; fa " ra$fo
Assuming λ<<1, Ka/Ks and the normalized ∆fa will be:
!
Ka
Ks
=fa
fs=ra
ro; "fa =
fa
fo + f s + fa# $
ra
ro= $
Ka
Ks
So initially the rate of change of the amino acid mutant allele frequencydfa/dt is proportional to the selection pressure Ka/Ks.
What does Ka/Ks really calculate?
WT Wildtype protein sequence
30 A single mutant at codon 30
Ka/Ks calculates selectionpressure for mutation X,assuming wildtype (WT) asthe starting genotype. So weshould write Ka/Ks as beingconditioned on WT as thestarting point:
WT
1082
90
45
3016.3
0.5
0.36.3
3.5
Ka/Ks graph gives rates offlow of HIV population todifferent mutations.
WTXsaXsaKKKK |)/()/( =
Ka/Ks Ignores Interactions
• Because different sites interact, theeffect of an amino acid mutation atone site can depend on the aminoacid at other sites.
• But Ka/Ks does not consider theseinteractions; in effect, it assumesthat all mutations take place in thewildtype sequence as a completelyfixed background.
84
88
7148
WT
1082
90
45
30
XYsa KK |)/(
Conditional Ka/Ks Reveals CompleteMutation Network
We can generalize Ka/Ks tomeasure the selection pressurefor a mutation Y conditioned onthe presence of a previousmutation X. We define this asthe conditional Ka/Ks:
Each edge represents one conditional Ka/Ks value.
12
3
0.4
0.5
0.6
4
22
0.30.2
7
0.6
Fast Mutation Paths of HIV Protease
Different Paths to the Same Genotypecan Differ in Speed
WT 11.69
34.50
0.52 5.37
90
10 10/90
90 mutations are known to directly cause drug resistance but lowerstability; 10 is site of compensatory mutations that improve stability.
Our analysis distinguishes them: faster path first introduces the drugresistance mutation, then the stabilizing mutation.
NB: speed of a multistep path is generally controlled by its slowest step.
Chen & Lee, Biol. Dir. (2006)
Conditional Ka/Ks“rate” shown oneach edge.
Pathway to doublemutant: simplestpossible “subgraph”of the conditionalKa/Ks network.
Reproducible Results in IndependentDatasets
WT 11.69
34.50
0.52 5.37
90
10 10/90
Specialty Stanford-Treated Stanford-Untreated
WT 10.81
13.32
0.88 4.66
90
10 10/90
WT 0.23
2.35
0.21 1.97
90
10 10/90
Highly reproducible in independent studies ofdifferent patients. They indicate real patterns ofdrug-associated selection pressure within theHIV population in the wild.
Conditional Ka/Ks: Specialty vs. Treated
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30
35
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0 5 10 15 20 25 30 35 40
Specialty
Tre
ate
d
Primary drug resistance
Accessory drug resistance
Function unknown
Quantitative Reproducibility
For codons withsufficient counts(NXa>400), the resultsmatch the Specialtyresults surprisinglywell.
Chen & Lee, Biol. Dir. (2006)
Experiment: Which Mutation HappenedFirst in Patients? (Shafer, Stanford)
• Take multiple samples at differenttime points during each patient’streatment.
• Identified pairs of mutations that co-occur, then re-examined previoussamples to see which mutationoccurred first.
Slow step is the first step of each path: 30N path is aboutsix-fold faster..
WT0.16
57
1.05 876
88D
30N 30N/88D
Different Paths to the Same Genotype Can Differin Speed
Pan et al. Nucleic Acids Res. 35: D371-D375 (2007)http://www.bioinformatics.ucla.edu/HIV
88D / 30N Comparison withLongitudinal Studies
v(PathX) : v(PathY) = 1.05: 0.16 = 6.6
PathX 30N->30N88D 13 patients PathY 88D->30N88D 2 patients
n(PathX): n(PathY) =13: 2
Longitudinal Data
WT0.16
57
1.05 876
88D
30N 30N/88D
90M mutations known to directly cause drug resistance butreduce protein stability; 73S mutations stabilize the mutantprotein.
Our analysis distinguishes them: faster path first introducesthe drug resistance mutation, then the stabilizing mutation.
WT171
>300
0.37 21
90M
73S 73S/90M
Different Paths to the Same Genotype Can Differin Speed
WT171
>300
0.37 21
90M
73S 73S/90M
90M / 73S Comparison withLongitudinal Studies
v(PathX) : v(PathY) = 0.37: 21
PathX 73S →73S90M 3 patients PathY 90M→73S90M 31 patients
n(PathX): n(PathY) =3: 31
Longitudinal Data
Shafer Longitudinal Results
• For 23 mutation pairs with apreferred path predicted by ourconditional Ka/Ks data (p=0.01),20 match the preferred kineticpathway observed in Shafer’slongitudinal patient studies.
• Statistically significant match,p=0.0002
Danger: Fast Paths to Multi-DrugResistance
• Multiple resistance: the combination ofthree mutations (at codons 82 (V82A/T/S),84 (I84V), and 90 (L90M)) is resistant tomost available protease inhibitors
• Rapid evolution of this triple mutant is aserious threat to individual treatment and tocontrol of the global AIDS epidemic.
• Our map shows where the fast paths to thiscombination are. Don’t want to go there!
WT
842.1249.30 825.16
90 7412.42 9.0416.2871 847.2615.26
90
82
Reveals Accelerated Paths toMulti-Drug Resistance
The path that includes mutation at codons 74 and 71 is 3 times faster than the direct path, and 7 times faster in its first step:
Use the order in which drugs are given (which in turncan select for one mutation over another) to pick aslower path!
Amino Acid Pairs Showing StrongNegative Selection Are Neighbors
1/10 11/100
0
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40
Atom
ic D
ista
nce
(A)
Selection
The Future of SNP Analysis?
• It should be emphasized that theonly input to this analysis was singlenucleotide polymorphism discoveryfrom chromatograms.
• No information about drug treatment,time, etc.
• Yet conditional Ka/Ks yields drug-resistance mutations; kinetics &temporal order; pathways.
A New Level of Strategic Intelligence
• A global picture of how HIV will respond inthe future to our drug treatments.
• Ka/Ks velocities tell us where HIVpopulation is going, detectable even whilemutations still rare.
• Moreover, since these selection pressuresare due to our actions (drugs), they aremanipulable.
• Even slowing DR evolution two-fold couldmake a big difference for control of theepidemic.
HIV Positive Selection Database
http://www.bioinformatics.ucla.edu/HIV
•Atlases of HIV drug resistance evolution from Specialty dataset.
•Analysis tools (snpindex).
AcknowledgementsLamei Chen: Ka/Ks analysisQi Wang: linkage analysisCalvin Pan: new analyses of cond. Ka/KsAlexander Alekseyenko: Nested List
Specialty Laboratories, Inc.Alla Perlina, Beatrisa Boyadzhyan
UCLA Collaborators:Christina Kitchen, Paul Krogstad
http://www.bioinformatics.ucla.edu/HIV
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