Mapping evolutionary pathways of HIV-1 drug resistance using conditional selection pressure. Christopher Lee, UCLA

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1 Mapping evolutionary pathways of HIV-1 drug resistance using conditional selection pressure Christopher Lee, UCLA

2 HIV-1 Protease and RT: anti-retroviral drug targets protease RT Protease: responsible for the posttranslational processing of the viral polyproteins to yield the structural proteins and enzymes of the virus Reverse transcriptase (RT): responsible for DNA polymerization

3 Selection Pressure Mapping: Build an Atlas of HIV Evolution Selection pressure measures whether an amino acid mutation is selected for (Ka/Ks>1) or against (Ka/Ks<1) by evolution, vs. synonymous mutations. Dataset: sequencing of 50,000 HIV clinical samples by Specialty Labs. Inc. 30-fold higher density of polymorphism information than human sequences. Goal: construct a selection pressure map of how HIV is evolving, where the virus is going, to evade our drugs.

4 Calculating Ka / Ks per Residue v v s t t s v v a t t a s a s a f n f n f n f n N N K K,,,, + + = observed #amino acid changes vs. synonymous mutations at this codon expected #amino acid changes vs. synonymous mutations at this codon ( ) i N i N N i s a a q q i N K K q N N i p LOD a! =! " # $ % & ' =! = (! = ) 1 log 1),, ( log 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 into account inferred phylogeny, f t /f v ratio, mutation rates, etc.

5 50 45 HIV Protease Positive Selection Chen, Perlina & Lee, J. Virol. (2004) Ka/Ks Codon Position Positive selection mapping automatically discovers causes of drug resistance!

6 Positive Selection Mapping Identifies Drug Resistance Mutations Correctly identified 19 of 22 known drug resistance mutation positions. Compared with multi-year research process (clinical, biochemical, genetics) that was previously required, this analysis is completely automatic; works directly on output from sequencing machines.

7 Build a Reaction Rate Diagram of HIV s Global Evolution A network diagram of the rates of transition between all possible genotypes. Ka/Ks is proportional to rate of increase of a mutation. Shows the speeds of all possible paths of evolution the viral population will follow, under the pressure of current drug treatments.

8 Selection Pressure is like an Evolutionary Velocity For wildtype, synonymous mutant, and amino acid mutant allele frequencies f o =1, f s =0, f a =0 initially, equal amino acid and synonymous mutation frequency λ, and reproduction rates r o, r a, after one unit of time f o " r o f o 1# 2$ ( ); f s " r o $f o ; f a " r a $f o Assuming λ<<1, Ka/Ks and the normalized f a will be: K a K s = f a f s = r a r o ; "f a = f a f o + f s + f a # $ r a r o = $ K a K s So initially the rate of change of the amino acid mutant allele frequency df a /dt is proportional to the selection pressure Ka/Ks.

9 What does Ka/Ks really calculate? WT Ka/Ks calculates selection pressure for mutation X, assuming wildtype (WT) as the starting genotype. So we should write Ka/Ks as being conditioned on WT as the starting point: ( = K a / K s ) X ( Ka / K s ) X WT WT Wildtype protein sequence 30 A single mutant at codon 30 Ka/Ks graph gives rates of flow of HIV population to different mutations.

10 Ka/Ks Ignores Interactions Because different sites interact, the effect of an amino acid mutation at one site can depend on the amino acid at other sites. But Ka/Ks does not consider these interactions; in effect, it assumes that all mutations take place in the wildtype sequence as a completely fixed background.

11 Conditional Ka/Ks Reveals Complete Mutation Network WT We can generalize Ka/Ks to measure the selection pressure for a mutation Y conditioned on the presence of a previous mutation X. We define this as the conditional Ka/Ks: ( K a / Ks) Y X Each edge represents one conditional Ka/Ks value.

12 Fast Mutation Paths of HIV Protease

13 Different Paths to the Same Genotype can Differ in Speed Pathway to double mutant: simplest possible subgraph of the conditional Ka/Ks network. WT /90 Chen & Lee, Biol. Dir. (2006) Conditional Ka/Ks rate shown on each edge. 90 mutations are known to directly cause drug resistance but lower stability; 10 is site of compensatory mutations that improve stability. Our analysis distinguishes them: faster path first introduces the drug resistance mutation, then the stabilizing mutation. NB: speed of a multistep path is generally controlled by its slowest step.

14 Reproducible Results in Independent Datasets Specialty Stanford-Treated Stanford-Untreated WT WT WT / / /90 Highly reproducible in independent studies of different patients. They indicate real patterns of drug-associated selection pressure within the HIV population in the wild.

15 Quantitative Reproducibility Conditional Ka/Ks: Specialty vs. Treated Chen & Lee, Biol. Dir. (2006) Primary drug resistance Accessory drug resistance 25 Function unknown Treated For codons with sufficient counts (N Xa >400), the results match the Specialty results surprisingly well. Specialty

16 Experiment: Which Mutation Happened First in Patients? (Shafer, Stanford) Take multiple samples at different time points during each patient s treatment. Identified pairs of mutations that cooccur, then re-examined previous samples to see which mutation occurred first.

17 Different Paths to the Same Genotype Can Differ in Speed WT D N 57 30N/88D Slow step is the first step of each path: 30N path is about six-fold faster.. Pan et al. Nucleic Acids Res. 35: D371-D375 (2007)

18 88D / 30N Comparison with Longitudinal Studies WT D N 57 30N/88D v(pathx) : v(pathy) = 1.05: 0.16 = 6.6 PathX 30N->30N88D 13 patients PathY 88D->30N88D 2 patients Longitudinal Data n(pathx): n(pathy) =13: 2

19 Different Paths to the Same Genotype Can Differ in Speed WT M S >300 73S/90M 90M mutations known to directly cause drug resistance but reduce protein stability; 73S mutations stabilize the mutant protein. Our analysis distinguishes them: faster path first introduces the drug resistance mutation, then the stabilizing mutation.

20 90M / 73S Comparison with Longitudinal Studies WT M S >300 73S/90M v(pathx) : v(pathy) = 0.37: 21 Longitudinal Data PathX 73S 73S90M 3 patients PathY 90M 73S90M 31 patients n(pathx): n(pathy) =3: 31

21 Shafer Longitudinal Results For 23 mutation pairs with a preferred path predicted by our conditional Ka/Ks data (p=0.01), 20 match the preferred kinetic pathway observed in Shafer s longitudinal patient studies. Statistically significant match, p=0.0002

22 Danger: Fast Paths to Multi-Drug Resistance Multiple resistance: the combination of three mutations (at codons 82 (V82A/T/S), 84 (I84V), and 90 (L90M)) is resistant to most available protease inhibitors Rapid evolution of this triple mutant is a serious threat to individual treatment and to control of the global AIDS epidemic. Our map shows where the fast paths to this combination are. Don t want to go there!

23 Reveals Accelerated Paths to Multi-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: WT Use the order in which drugs are given (which in turn can select for one mutation over another) to pick a slower path!

24 Amino Acid Pairs Showing Strong Negative Selection Are Neighbors Atomic Distance (A) /100 1/10 Selection 1

25 The Future of SNP Analysis? It should be emphasized that the only input to this analysis was single nucleotide polymorphism discovery from chromatograms. No information about drug treatment, time, etc. Yet conditional Ka/Ks yields drugresistance mutations; kinetics & temporal order; pathways.

26 A New Level of Strategic Intelligence A global picture of how HIV will respond in the future to our drug treatments. Ka/Ks velocities tell us where HIV population is going, detectable even while mutations still rare. Moreover, since these selection pressures are due to our actions (drugs), they are manipulable. Even slowing DR evolution two-fold could make a big difference for control of the epidemic.

27 HIV Positive Selection Database Atlases of HIV drug resistance evolution from Specialty dataset. Analysis tools (snpindex).

28 Acknowledgements Lamei Chen: Ka/Ks analysis Qi Wang: linkage analysis Calvin Pan: new analyses of cond. Ka/Ks Alexander Alekseyenko: Nested List Specialty Laboratories, Inc. Alla Perlina, Beatrisa Boyadzhyan UCLA Collaborators: Christina Kitchen, Paul Krogstad