Evoluzione genetica di HIV ed evoluzione clinica della malattia AIDS: due aspetti correlati?

Carlo Federico Perno

Why does a Virus evolve?

A virus needs to evolve to:

• Infect different cell types• Rapidly become resistant to otherwise highly effective antiviral drugs• Evade the immune system

Virus transmission• Obstacles for virus transmission:

– Natural• Host genetics• Host Immune system• Viral replication rate• Etc

– Artificial• Vaccines• Passive immunity• Antiviral drugs

• A continuous evolution allows viruses to achieve new characteristics able to overcome these obstacles, and be successful in their replication effort

Why does a Virus evolve?

A virus needs to evolve to:

• Infect different cell types• Rapidly become resistant to otherwise highly effective antiviral drugs• Evade the immune system

…SURVIVE !!!

Consequences

- The most fit virus, with the highest chances to survive, does not kill the host or, at minimum, kills the host in a long run- To be selected and expanded, it kills the host at a rate lower than other viruses of the same species

- Ex: HIV subtype A vs Subtype D

Does HIV-1 genetic diversity have an effect on clinical progression?

“HIV-1 Subtype D is associated with faster disease progression than Subtype A in spite of similar plasma HIV-1 loads”

Subtype C vs. subtype A, P at log-rank = 0.2Subtype D vs. subtype A, P at log-rank = 0.05

Baetan JM , JID 2007

Analysision 145 HIV-1 infected Kenyan women followed from the time of HIV-1 acquisition.

The steps in virus evolution are:

• generation of diversity through mutation, recombination, and genome segment reassortment in multipartite genomes

• competition among the generated variants

• selection of those mutants showing the largest phenotypic advantage in a given environment

How does a virus evolve?Evolution = genetic variation

“All the organic beings which have ever lived on this earth have

descended from some one primordial form”

Charles Darwin

From this idea, each characteristic of a species could be the result of a peculiar evolutionary history:

•Peacock’s ancestors•The number and the sequences of his genes•The catalytic ability of his enzymes•His needs•The structure of his cells •His environmental fitness

... This is his evolutionary history

Evolution is the unifying theory of biology

“Nothing in biology makes sense except in the light of the evolution”Theodosius Dobzhansky

• In biology a mutation is a randomly derived change to the nucleotide sequence of the genetic material of an organism.

• Non lethal mutations accumulate within the gene pool and increase the amount of genetic variation. The abundance of some genetic changes within the gene pool can be reduced by natural selection, while other "more favorable" mutations may accumulate and result in adaptive evolutionary changes.

Does occurrence of mutations mean their necessary selection and

appearance (fixation) in circulating virus strains?

NO

Substitution of a Nucleotide

Point Mutations

Early stop of protein

Same amino acid, same protein

Different amino acid, protein mutated

Mutation Non Sense

Silent Mutation Synonymous

MutationNON synonymous

A C G T 4 nucleotides X X X 1 codon = 1a.a.

43 = 64 codons -> 20 a.a.

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Objective

● To examine tropism after ART failure using a clinically validated genotypic tropism assay in a large sample of treatment-experienced patients

● HIV-1 tropism was assessed at baseline and virologic failure over 48 weeks in patients receiving an optimized background regimen (OBT) with a maraviroc placebo (PBO) in the MOTIVATE 1 and 2 studies1

1Gulick et al. N Engl J Med. 2008;359:1429-1441Svicher et al. 2009

What is the relation between the HIV tropism and its evolution

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V3 sequencing was successful for 87 (80.5%) out of 108 samples

83 out of 87 have also the trofile result available. For the remaining 4, Trofile failed to assess viral tropism.

All of them resulted R5 with Trofile at screening

Rate of successful V3 sequencing

Svicher et al. 2009

17~90% of patients with R5 tropism at screening had an R5 result at treatment failure.

Screening Tropism Tropism at treatment failure

False positive rate 5% R5 X4

R5 78 (89.7)* 3 (3.4)X4 1 (1.2) 5 (5.7)

Viral tropism has been predicted by Geno2Pheno algorithm at a false positive rate of 5% using 87 V3 sequences*P< 0.001

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Analysis of 65 patients where R5-usage is maintained both at screening and at failure

Svicher et al. 2009

Despite tropism stability (R5 both at screening and at failure), the majority (23/35) of V3 positions shows higher entropy at failure than at screening suggesting viral evolution despite tropism stability

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V3 positionsThe analysis was performed in the sub set of 65 patients with R5-tropism at baseline and failure (using geno2pheno at FPR of10%).The Shannon entropy was calculated for each V3 position following the formula:

H(i) = - Σ P(si) log P(si) (where s=A,S,L,… for the 20 amino acids Ala, Ser, Leu, . . .). The difference between entropy at screening and at failure at each V3 position is reported in the graph.

Chan

ge in

ent

ropy

from

scr

eeni

ng to

failu

re

Svicher et al. 2009

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Increased genetic diversity between screening and failuresignificantly correlates with higher duration of treatment

Spearman Correlation between Genetic Diversity

and Therapy Duration

Rho P Value

0.2001 0.003

Rho is the Spearman's rank correlation coefficient. Rho, ranging from -1.00 to 1.00, is a measure of the strength and direction of the association between two variables. A positive coefficient indicates that the variables X and Y increase in a correlated manner.

The genetic distance (mean number of substitutions per site) of V3 sequences from screening to failure for each patient and treatment duration were used to calculate Rho. The analysis was performed in the sub-set of 65 patients with R5-tropism at baseline and failure (using geno2pheno algorithm at FPR of 10%).

Svicher et al. 2009

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- An accumulation of synonymous substitutions was observed from screening to failure in all 87 patients (100%)

- An accumulation of non-synonymous (amino acidic) substitutions was observed in 26/87 patients (28.9%)

- Tropism switches were observed in only 4 patients (3 from R5 to X4, 1 from X4 to R5) [4/87, 4.6%]

The analysis was performed on all 87 patients on study (using geno2pheno algorithm at FPR of 5%).Average observation from baseline to virological failure was 150 days

Despite the high natural genetic variability of V3, the frequency of tropism switches remains limited

Switches from R5 to X4 usage is mainly driven by a shift of viral species

The analysis was performed on the 3 samples resulting R5 at screening and X4 at failure using geno2pheno algorithm at both 5% and 10%. Genetic distance is the mean number of substitutions per site.

The high genetic distance values and the high number of amino acid substitutions from screening to failure support

the shift from an R5-using strain to an X4-using strain

R5 at screeningX4 at failure

R5 at screeningX4 at failure

R5 at screeningX4 at failure

Svicher et al. 2009

C. If a mutation with lower fitness remains fixed, we obtain a minority species (called quasispecies), that may become predominant if the environment changes

Ex 1. Antiviral pressure that selects for a viral strain with lower sensitivity to drugs2. Immunological pressure by a vaccine that selects for an escape mutant not neutralized by the immune system

In the case of viruses, this switch in predominance may take days (not millennia!!)- Selection of strains resistant to antiviral drugs

CONSEQUENCES

Baseline Tropism:Designated R5

R5D/M

X4Non-functional clone Lewis M, et al. 16th IHIVDRW, 2007. Abstract 56.

StopMaraviroc

Re-Emergenceof R5!!

X4 HIVNot Detected at <4%

Maravirocin Suboptimal

Regimen

Tropismat Failure:

D/M

Re-emergence of the most fit R5-virus

GRT September ‘02PR: L63P V77I I93LRT: G333E

GRT March ’05 (ARV: 3Tc d4T LPV/r)PR: L10I K20R L33F M36M/I/V M46I I54V L63P A71T G73G/A V82A N88D L90M I93LRT: M41L E44E/D D67D/N V118I M184V L210W T215Y G333E

GRT May ‘06PR: L63P V77I I93LRT: G333E

GRT January ’08 (ARV: AZT 3TC ABC DRV/r)PR: L10I K20R V32I L33F M36I K43T M46I I47V I54V L63P A71T G73A/T I84V N88D L90M I93LRT: M41L E44D D67N V118I M184V L210W T215Y G333E

GRT during therapy interruption GRT under antiretroviral treatment

Clinical Case: Id 186 - Patient infected with HIV-1 B subtype

Age: 46

Sex:

M

Risk Factor: Not known

CDC stage: C3

GRT March ’02 (ARV: 3TC d4T ABC LPV/r)PR: L10I M36V M46L I54V L63P A71T V82A N88D L90M I93LRT: M41L E44E/D D67N L74L/V V118V/I M184V G190G/E/Q/R L210W T215Y K219K/N G333E

• Virus under drug pressure selects, among thousands of quasispecies present in the body, the virus strain with the greatest fitness in that environment– Wild type strain (the most fit) without drugs– Highly mutated (resistant) strain in the presence of

drugs

No chances of winning the battle until viral replication is sharply decreased/nullified

CONSEQUENCES

• The replication is a necessary prerequisite for occurrence and appearance of mutations

• Without replication, no mutation

By decreasing the replication rate of a virus, we dramatically decrease its ability to

escape immune system and antiviral drugs

……If a mutation produces a variant with low fitness, and/or this mutation is not fixed, this new variant disappearsEx. Loss of viral species

BUT……

Time

Alle

le F

requ

ency

0

1

lost mutation

fixed mutation

polymorphism maintained

Adapted from The Phylogenetic Handbook 2009, M Salemi and AM Vandamme

Each different symbol represent a different allele. A mutation event in the sixth generation gives rise to a new allele. The figure illustrates fixation and loss of alleles during a bottleneck event, and the concept of coalescence time (tracking back the time to the most recent common ancestor of the gray individuals). N: population size.

Bottleneck event Mutatio

n event

Coalescence time

Population dynamics of alleles

Effective sample size: the genetic bottleneck

PRACTICAL CONSEQUENCE

• By reducing the sample size of a species (bacteria, viruses, etc) we dramatically reduce the chance that the species mutates and thus escapes pressure by chemotherapy and/or immune system:

- Success of antivirals and antibiotics despite a small remaining number of microorganisms

- Success of vaccines against viruses with low mutational rate

- Insuccess of vaccines against highly mutating viruses- Insuccess of vaccines targeted against genes with high

rate of mutations

- The case of smallpox virus- The case of influenza virus

Evolutionary abilities of Variola• Variola has a single linear double stranded DNA genome of 186

kilobase pairs.• Some studies showed the presence of a low mutation rate.• A similar situation is present in other component of orthopoxviruses

genus.

Variola virusIsolated compared

Year isolation SNPs* among genomes

ETH72_16 vs ETH72_17 1972 0

AFG70_vlt4 vs SYR72_119 1970, 1972 1

SYR72_119 vs PAK69_lah 1972, 1969 1

SYR72_119 vs IRN72_tbrz 1972 1

Adapted form Li et al., 2007 *Single Nucleotide Polymorphisms

Variola is lacking of great evolutionary potential

Number of SNPs found in different couples of viral isolates

Smallpox Vaccine

• Its history is strictly connected to the birth of modern vaccinology.

• Variolation• 1796: Edward

Jenner.• 1977: last case of

smallpox.

Such good result was due to…

• the biological characteristics of the organism,• vaccine technology,• surveillance and laboratory identification,• effective delivery of vaccination programmes and

international commitment to eradication.

• Smallpox virus has no host reservoir outside humans!!.

The case of influenza virus

The evolutionary power ofantigenic shift

Name of pandemic

Subtype involved

Pandemic severity index

Asiatic (Russian)

H2N2 NA

Spanish H1N1 5

Asian H2N2 2

Hong Kong H3N2 2

“Swine” H1N1 NA

The last known flu pandemics

Genesis of “Swine flu” H1N1 virus

Classical swine flu virusH1N1

Human H3N2 flu virus

Avian flu virus(unknown subtype)

H3N2 swine virus

Swine H1N1 flu virus

H1N1 “Swine flu” virus

The reservoir hosts act as variability source for the new evolutionary steps of flu virus

The evolutionary novelty of “Swine flu” virus

… gene sequences collected from the USA for swine flu (subtype H1N1) in the year 2009 are evolutionarily widely different form the past few years sequences…the 2009 sequences are evolutionarily more similar to the most ancient sequence reported in the NCBI database collected in 1918. (Sinha et al., 2009)

CONSEQUENCESSmallpox: Low rate of polymerase errors + lack

of animal reservoir (even in the presence of high replication rate) =

Eradication possible (and obtained indeed!!)

Flu: High rate of polymerase errors + presence of multiple animal reservoirs (+ high rate of

recombination) = Eradication impossible New vaccine required every year

Resistance to anti-HIV drugs is the most elegant, and practically relevant, example of

the consequences of viral evolution

What about the effect of resistance on clinical outcomes?

HIV-1: Drug resistance development

It’s important to detect resistant quasispecies before the treatment starting or as soon as possible during treatment

Toxicity No AdherenceBioavailability

Patients’ MetabolismReservoir

Cozzi-Lepri et al., AIDS 2007RTI resistance at t0 RTI resistance from t0 to t1

Percentage of patients’ viruses who had RTI resistance mutations at t0 and of those who acquired such mutations from t0 to t1 by specific mutation/drug class

In patients kept on the same virologically failing

cART regimen for a median of 6 months,

there was considerable accumulation of drug resistance mutations

Logistic regression Multivariate P

CD4 time-dependent(per 50 cells increase)

0.79 (0.66-0.93) 0.006

Plasma HIV-RNA time-dependent(per 1 log10 increase)

1.14 (0.76-1.71) 0.539

Previous AIDS 2.29 (0.86-6.12) 0.098

LPV after GRT 0.57 (0.19-1.67) 0.302

3 drug class multi-resistance (DCMR) 12.29 (3.00-50.28) <0.001

Poor survival in drug-class multi-resistance

Days from GRT

16001400120010008006004002000

Cum

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n su

rviv

ing

1,0

,9

,8

,7

,6

,5

,4

3 DCMR

2 DCMR

1 DCMR

0 DCMR

P at log-rank <0.001

Zaccarelli, AIDS 2005

Main Findings

Cozzi-Lepri A, et al. AIDS. 2008;22:2187-2198.

Definition of Resistance Adjusted RH (95% CI) P Value

≥1 NRTI mutation 1.52 (1.14-2.03) .004≥1 NNRTI mutation 1.95 (1.28-2.95) .002≥1 PI mutation (major and minor) 1.50 (1.14-1.97) .004Drug-class resistance mutations 1.79 (1.28-2.50) .0007Cumulative drug-class resistance (major and minor PI mutations counted)Virologic failure with no resistance 1.32 (0.57-3.06) .52Single-class resistance 1.03 (0.65-1.63) .90Double-class resistance 1.55 (1.15-2.08) .004Triple-class resistance 1.80 (1.20-2.70) .005

• In multivariable analyses, patients with drug resistance mutations to ≥ 2 classes during first 2 years of HAART at significantly higher risk of AIDS progression or death

Virus continues to evolve if kept under pressure of failing antiviral therapy.

This may increase cross-resistance, and then decrease chances of efficacy of subsequent drugs and regimens.

In the frame of a correct therapeutic sequencing, first failing therapies should be changed as soon as possible after definition of virological failure.

Conclusions• Viruses represent the best model of evolution on the

earth• They mutate in days faster than what humans have

ever changed in millennia• Their evolution capacity is function of several factors

• The host represents the most important extrinsic factor

• Through a proper use of interdisciplinary tools (mathematics, physics, biochemistry, molecular biology, biology, pharmacy, medicine) we can reasonably predict their evolution, and define ways and consequences of the interaction with humans

Conclusion (II)• The understanding of viral evolution has major

consequences in medicine, of key practical relevance: • Identification of targets for viral vaccines• Definition of potential outcomes of massive

vaccinations• Eradication, infection containment, functional

cure of infected people• Setting therapeutic strategies against viral infections

• Definition of the chances of success of antivirals (resistance testing, antivirograms)

• Select therapies with the greatest chances of success (Ex. multiple drugs against viruses with high mutation rate)

INMI “L. Spallanzani A. Antinori P. NarcisoC. Gori R. d’Arrigo F. ForbiciM.P. TrottaA. AmmassariR. BellagambaM. ZaccarelliG. LiuzziV. TozziP. SetteN. PetrosilloF. AntonucciE. BoumisE. NicastriU. ViscoP. De LongisG. D’OffiziG. Ippolitoand the Resistance Study Group

ACKNOWLEDGEMENTS

University of Rome “Tor Vergata”C.F. PernoF. Ceccherini SilbersteinV. SvicherM. SantoroA. BertoliD. ArmeniaS. DimonteL. FabeniR. SalpiniC. AlteriV. CentoF. StaziS. DimonteL. SarmatiM. Andreoni

Modena and Ferrara Infectious Diseases

C. MussiniV. Borghi

W. GennariL. SighinolfiF. Ghinelli

G. Rizzardini V. MicheliA. Capetti

L. Sacco University Hospital

The I.CO.N.A. Study GroupA. d’Arminio MonforteM. Moroni

ACKNOWLEDGEMENTS

University of CatanzaroS. AlcaroA. Artese Catholic University of

Rome, Sacro CuoreA. De LucaR. Cauda

Infectious Diseases Unit FlorenceS. Lo CaputoF. Mazzotta

San Gallicano HospitalG. PalamaraM. Giuliani

Infectious Diseases, BergamoF. MaggioloAP. Callegaro

University of TurinG. Di PerriS. Bonora

ArcaM. Zazzi

University of PadovaG. Palu’S. Parisi

University of Rome Tor VergataDept. of MathematicsLivio TrioloMario Santoro University Cergy-Pontoise

LPTMThierry Gobron University of S. Raffaele

A. LazzarinM. Clementi