PhD Advisory Committee Report1st meeting

Fabio Zanini

fabio.zanini@tuebingen.mpg.de

March 9th, 2012

Beginning of thesis: June 1st, 2011

Committee members

- Dr. Richard Neher

- Prof. Dr. Daniel Huson

- Dr. Birte Hocker

- Prof. Dr. Ralf Sommer

- Prof. Dr. Karsten Borgwardt

Key words: HIV, evolution, population genetics, drug resistance

Main question of the thesis

How does the Human Immunodeficiency Virus population evolve within itshost during chronic infection? What is the relative importance of the variousevolutionary processes, such as selection, mutation, recombination, popula-tion size and compartmentalization? Can one rationalize the experimentalobservations in a quantitative model?

Abstract

Human Immunodeficiency Virus (HIV) evolves very rapidly withininfected patients. Because of its clinical significance, a large amountof data has been collected on this process, but different datasets havebeen built for different purposes and are only loosely connected toeach other [1–4]. I aim at combining clinical, biological and geneticinformation into a baseline theoretical model of HIV evolution at thehost level [5, 6]. Once such a model is established, I plan to use it forstudying the evolution of drug resistance.

Specific Aims

1. what demographic and biological parameters govern HIV evolution?

2. what are the evolutionary consequences of rapid changes in the environ-ment, such as start or interruption of antiretroviral treatment (ART)and coreceptor tropism?

3. what mechanisms drive the appearance and spread of drug resistantstrains during therapeutic failure?

Progress Report

Literature background

Some basic properties of intrahost HIV evolution have been measured orestimated, including the genome length (L ∼ 104 b.p.), the mutation rate(µ ∼ 10−5 changes · gen−1 · b.p.−1 [7]) and the recombination rate (r ∼10−5 switches · gen−1 · b.p.−1 [8]). However, other crucial properties, such aspopulation size and structure, are still poorly understood.

HIV evolution during chronic infection

Despite a lack of symptoms, HIV evolves rapidly and continuously duringchronic infection. Divergence from the founder strain increases, in the enve-lope gene, by ∼ 1 % per year. The immune system of the host imposes astrong selective pressure and causes a selective sweep (s ∼ 0.01) every fewmonths. Nonetheless, the viral population is quite diverse (see Fig. 1a) [3].

HIV evolution is usually rationalized using neutral models with effectivepopulation sizes Ne ∼ 103 - 104 [9]. However, natural selection and linkagebetween loci (genetic draft) limit the genetic diversity and suggest much

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NJtree

(a)

bifurcation

(b)

Fig. 1: (a) HIV evolution during chronic infection. Genetic diversity withinthe viral env gene and divergence from the founder strain increase at∼ 1% year−1. Inset: Neighbor-Joining tree of the same sequences, col-ored based on sampling time (blue: early infection, red: late infection).(b) Trajectory of the first three principal components of the HIV env geneduring coreceptor switch. Colors like in (a). After a certain time point,two HIV populations with different genomes are seen, but no intermedi-ates; this is suggestive of intrahost “speciation”. Data from Ref. [3].

larger real population sizes (N ∼ 107) [5]. Linkage effects are especiallyimportant because of the small genome size and low recombination rate.

A broader approach beyond neutral models is therefore needed. Computer-based simulations represent a key component, since they are able to disentan-gle the effects of mutation, selection, and recombination. Specific instances ofthis idea have been published recently [6,10], but a thorough understandingis still lacking.

Evolution after rapid environmental change

The ecological environment and the selective landscape of HIV change dur-ing the infection. In addition to the steady adaptation of the host immunesystem, more sudden changes happen as well. Two relatively well-knowninstances of this phenomenon are coreceptor tropism and start/interruptionof ART. The former refers to the emergence of HIV strains that enter tar-get cell not via the usual C-C chemokine receptor type 5 (R5) – which is

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exclusive in early infection –, but via the C-X-C chemokine receptor type4 (X4). X4-HIV variants are able to infect previously unaccessible cells, defacto overcoming, at least transiently, the limitedness of resources that keepR5-HIV under control. Previous studies have underlined the relevance ofcoreceptor switch for clinical progression to acquired immunodeficiency syn-drome (AIDS) [3]. Moreover, it has been shown that, in most cases, X4strains do not eradicate R5 ones; a process similar to sympatric speciationseems to be taking place [11]. An example of this population splitting isshown in Fig. 1b. Intriguingly, the ecological and evolutionary time scalesare comparable; HIV adapts to the new target cells while the prey-predatorequilibrium is being established.

A complete demographic substitution happens in the case of ART startinstead. Recent analyses based on deep sequencing protocols suggest thatmutations associated with drug resistance are essentially absent in the HIVpopulation before ART start [4]. However, the relative importances of stand-ing genetic variation – i.e. to-become beneficial mutations that are alreadypresent by chance before ART start – and de novo mutations remains unclear(see Ref. [12] for an introduction); the less clear in HIV dynamics, since thepopulation size is thought to be very large and every viable single mutant ofthe most abundant haplotype is almost surely present.

Emergence of drug resistance related mutations

Suboptimal ARTs lead to the emergence of drug resistant strains [13]. Listsof mutations that fixate quickly after starting treatment and confer partialresistance (primary mutations) have been compiled by expert panels; othermutations are thought to be compensatory in functional terms, e.g. forviral replication efficiency (secondary mutations) [14]. Moreover, machinelearning algorithms have been applied to predict resistance phenotypes fromgenotypes. These include least squares regression, decision trees, and kernel-based approaches [15, 16].

The basic questions about the evolutionary dynamics of drug resistancehave proved to be more difficult to answer, and are subjects of ongoing re-search.

Current status of the project

My project is proceeding along these three lines of research. I started bymodeling the genotype-phenotype map of drug resistance. I tested severalmethods that included single-mutation effects and pairwise epistasis. I triedto include structural information by observing what parts of the HIV en-

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Fig. 2: Divergence and diversity of simulations at two different abundances ofsites under positive selection (af). (left) 1 % and (right) 10 % of sitescarry beneficial alleles. Diversity does not change much, but divergenceincreases with the number of positively selected sites.

zymes mutate first and what later. However, these efforts have been delayedby the heterogeneous and scattered nature of the available data. My advisorand I concluded that a baseline model of HIV evolution was needed first; thiswould then constitute a solid platform to study drug resistance. I thereforestarted to develop a model of HIV evolution during chronic infection. I amcurrently integrating various data sources and performing computer simula-tions. I recently noticed that HIV frequently undergoes genetic splitting dueto coreceptor usage, and started thinking about this aspect as well.

HIV evolution during chronic infection

Several datasets on HIV evolution during chronic infection have been recentlypublished. Each has its own strengths, for instance long time courses, deepsequencing or long reads [3, 11, 17]. This enables me to focus on differentpredictions of the model, such as the frequencies of rare alleles, the linkagebetween sites and the genetic diversity.

The computational simulations include the basic evolutionary processessuch as mutation, recombination and selection. I am focusing on the simula-tion results depending on recombination rate, population size, and numberof sites under positive selection (see Fig. 2).

I can show that neutral models are unable to explain even simple proper-ties of the evolving HIV population, such as the frequency of rare alleles. Thesimulations confirm this result, but some characteristic patterns are still notfully understood (see Fig. 3). For instance, how strong and frequent mustselective sweeps be to produce the excess of fixed alleles seen in Fig. 3a?

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(a) (b)

Fig. 3: (a) Allele frequency of new alleles during ART. Data from Ref. [4]. x−1

is the prediction of the neutral model. Note the excess of very abundantalleles, ν & 0.5, due to selection and linkage.(b) The same quantity as in (a) but generated in a computer simulationwith reasonable parameters for HIV.

Evolution after rapid environmental change

I have observed the presence of speciation-like phenomena only recently. Iam currently using simple analyses to figure which patients underwent suchbifurcation events (see Fig. 1b). Single-population based simulations andtheory are not good models for these patients, because only one ecologicalniche is taken into account.

On the side of data analysis, I plan to analyze these peculiar histories toprobe the population structure. On the side of models, I intend to simulatethis phenomenon by describing two weakly linked populations that exchangegenetic material only rarely.

Emergence of drug resistance related mutations

I am performing an extensive analysis of the Stanford HIV database in orderto get an intuition about the role of epistasis in cases of treatment failure [1].I am testing statistics- and structure-based fit techniques to infer the drug re-sistance genotype-phenotype map. Among other things, I have been focusingon the three dimensional crystal structure of HIV enzymes to correlate theposition of a certain mutation with its first time of appearance during ART.There is evidence that groups of structurally linked residues define “regions”

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2 0 2 4 6

Real pheno

2

0

2

4

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Fitpheno

PR + IDV: regions fit, L = 6

REGIONS (train)

REGIONS (test)

PCA

Fig. 4: Goodness of fit for region-based fits of drug resistance on train and testdata (yellow and blue points, R2 = 0.81), compared to a fit using the firstprincipal components (green, R2 = 0.74). The fit based on a few primarymutations performs much worse and is not shown.

that can be treated as building blocks for modeling the genotype-phenotypemap. For instance, a fit based purely on the presence/absence in each re-gion of at least one mutation yields better results than picking well-knownprimary mutations, or even than principal component decomposition of thedataset (with the same number of parameters, L ∼ 6, see Fig. 4).

I have also studied the conservation of physical properties, such as thelocal protein charge, during evolution of drug resistance. There seems tobe an indication that local charge conservation is an important structuralconstraint, but the analysis is not conclusive yet (data not shown).

The main issue with this part of the project is the degeneracy of thestudied datasets. A lot of mutations are reported to be associated with drugresistance, but the sequence space is not evenly sampled in the HIV database.I am trying to lift this degeneracy by developing a dynamical model of HIVevolution in the chronic phase first.

Participation in meetings and conferences

To date, I have not presented any research work yet.

20-25 Aug 2011 13th Congress of the European Society for EvolutionaryBiology (Tubingen).

22-25 Feb 2012 Spring Meeting on Molecular Ecology and Evolution andworkshop on Viral Evolution: Linking Genetics to Epidemics (Cologne).

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References

[1] S.Y. Rhee, M.J. Gonzales, R. Kantor, B.J. Betts, J. Ravela, and R.W.Shafer. Human immunodeficiency virus reverse transcriptase and pro-tease sequence database. Nucleic acids research, 31(1):298–303, 2003.

[2] Kuiken C, Foley B, Leitner T, Apetrei C, Hahn B, Mizrachi I, MullinsJ, Rambaut A, Wolinsky S, and Korber B. HIV Sequence Compendium2010. Eds. Published by Theoretical Biology and Biophysics Group, LosAlamos National Laboratory, NM, LA-UR 10-03684., 2010.

[3] R. Shankarappa, J.B. Margolick, S.J. Gange, A.G. Rodrigo, David Up-church, Homayoon Farzadegan, Phalguni Gupta, C.R. Rinaldo, G.H.Learn, X. He, and Others. Consistent viral evolutionary changes as-sociated with the progression of human immunodeficiency virus type 1infection. Journal of Virology, 73(12):10489, 1999.

[4] Charlotte Hedskog, Mattias Mild, Johanna Jernberg, Ellen Sherwood,Goran Bratt, Thomas Leitner, Joakim Lundeberg, Bjorn Andersson,and Jan Albert. Dynamics of HIV-1 quasispecies during antiviral treat-ment dissected using ultra-deep pyrosequencing. PloS one, 5(7):e11345,January 2010.

[5] Richard A Neher and Boris Shraiman. Genetic Draft and Quasi-Neutrality in Large Facultatively Sexual Populations. Genetics,188(4):975–996, 2011.

[6] Rebecca Batorsky, Mary F Kearney, Sarah E Palmer, Frank Maldarelli,Igor M Rouzine, and John M Coffin. Estimate of effective recombina-tion rate and average selection coefficient for HIV in chronic infection.Proceedings of the National Academy of Sciences of the United States ofAmerica, 108(14):5661–6, April 2011.

[7] L M Mansky and H M Temin. Lower In Vivo Mutation Rate of HumanImmunodeficiency Virus Type 1 than That Predicted from the Fidelityof Purified Reverse Transcriptase. Journal of virology, 69(8):5087–5094,1995.

[8] R.A. Neher and Thomas Leitner. Recombination rate and selec-tion strength in HIV intra-patient evolution. PLoS Comput Biol,6(1):e1000660, January 2010.

[9] G Achaz, S Palmer, M Kearney, F Maldarelli, J W Mellors, J M Cof-fin, and J Wakeley. A robust measure of HIV-1 population turnover

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within chronically infected individuals. Molecular biology and evolution,21(10):1902–12, October 2004.

[10] Ha Youn Lee, Alan S Perelson, Su-Chan Park, and Thomas Leitner. Dy-namic correlation between intrahost HIV-1 quasispecies evolution anddisease progression. PLoS computational biology, 4(12):e1000240, De-cember 2008.

[11] Evelien M Bunnik, Luke C Swenson, Diana Edo-Matas, Wei Huang,Winnie Dong, Arne Frantzell, Christos J Petropoulos, Eoin Coakley,Hanneke Schuitemaker, P Richard Harrigan, and Angelique B van ’tWout. Detection of Inferred CCR5- and CXCR4-Using HIV-1 Vari-ants and Evolutionary Intermediates Using Ultra-Deep Pyrosequencing.PLoS pathogens, 7(6):e1002106, June 2011.

[12] Rowan D H Barrett and Dolph Schluter. Adaptation from standinggenetic variation. Trends in ecology & evolution, 23(1):38–44, January2008.

[13] P Kellam, C a Boucher, J M Tijnagel, and B a Larder. Zidovudinetreatment results in the selection of human immunodeficiency virus type1 variants whose genotypes confer increasing levels of drug resistance.The Journal of general virology, 75 ( Pt 2):341–51, February 1994.

[14] Javier Martinez-Picado and Miguel Angel Martınez. HIV-1 reverse tran-scriptase inhibitor resistance mutations and fitness: a view from theclinic and ex vivo. Virus research, 134(1-2):104–23, June 2008.

[15] Soo-Yon Rhee, Jonathan Taylor, Gauhar Wadhera, Asa Ben-Hur, Dou-glas L Brutlag, and Robert W Shafer. Genotypic predictors of human im-munodeficiency virus type 1 drug resistance. Proceedings of the NationalAcademy of Sciences of the United States of America, 103(46):17355–60,November 2006.

[16] Trevor Hinkley, Joao Martins, Colombe Chappey, Mojgan Haddad, EricStawiski, Jeannette M Whitcomb, Christos J Petropoulos, and SebastianBonhoeffer. A systems analysis of mutational effects in HIV-1 proteaseand reverse transcriptase. Nature genetics, 43(5):487–490, March 2011.

[17] E.M. Bunnik, Linaida Pisas, A.C. Van Nuenen, and HannekeSchuitemaker. Autologous neutralizing humoral immunity and evolutionof the viral envelope in the course of subtype B human immunodeficiencyvirus type 1 infection. Journal of virology, 82(16):7932, 2008.

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