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Evoluzione genetica di HIV ed evoluzione clinica della malattia AIDS: due aspetti correlati? Carlo Federico Perno.

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Presentation on theme: "Evoluzione genetica di HIV ed evoluzione clinica della malattia AIDS: due aspetti correlati? Carlo Federico Perno."— Presentation transcript:

1 Evoluzione genetica di HIV ed evoluzione clinica della malattia AIDS: due aspetti correlati? Carlo Federico Perno

2 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

3 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

4 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 !!!

5 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

6 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 = 0.2 Subtype 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.

7 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

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9 “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

10 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.

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13 Does occurrence of mutations mean their necessary selection and appearance (fixation) in circulating virus strains? NO

14 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 Mutation NON synonymous A C G T 4 nucleotides X X X 1 codon = 1a.a. 4 3 = 64 codons -> 20 a.a.

15 15 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 studies 1 1 Gulick et al. N Engl J Med. 2008;359: Svicher et al What is the relation between the HIV tropism and its evolution

16 16 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 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% R5X4 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

18 18 Analysis of 65 patients where R5-usage is maintained both at screening and at failure Svicher et al. 2009

19 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 19 V3 positions The 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. Change in entropy from screening to failure Svicher et al. 2009

20 20 Increased genetic diversity between screening and failure significantly correlates with higher duration of treatment Spearman Correlation between Genetic Diversity and Therapy Duration RhoP Value Rho is the Spearman's rank correlation coefficient. Rho, ranging from 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

21 21 - 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

22 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 screening X4 at failure R5 at screening X4 at failure R5 at screening X4 at failure Svicher et al. 2009

23 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 drugs 2. 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

24 Baseline Tropism: Designated R5 R5 D/M X4 Non-functional clone Lewis M, et al. 16 th IHIVDRW, Abstract 56. Stop Maraviroc Re-Emergence of R5!! X4 HIV Not Detected at <4% Maraviroc in Suboptimal Regimen Tropism at Failure: D/M Re-emergence of the most fit R5-virus

25 GRT September ‘02 PR: L63P V77I I93L RT: 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 I93L RT: M41L E44E/D D67D/N V118I M184V L210W T215Y G333E GRT May ‘06 PR: L63P V77I I93L RT: 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 I93L RT: M41L E44D D67N V118I M184V L210W T215Y G333E GRT during therapy interruptionGRT under antiretroviral treatment Clinical Case: Id 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 I93L RT: M41L E44E/D D67N L74L/V V118V/I M184V G190G/E/Q/R L210W T215Y K219K/N G333E

26 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

27 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

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

29 Time Allele Frequency 0 1 lost mutation fixed mutation polymorphism maintained Population Dynamics

30 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 Mutation event Coalescence time Population dynamics of alleles Effective sample size: the genetic bottleneck

31 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

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

33 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 virus Isolated compared Year isolationSNPs* among genomes ETH72_16 vs ETH72_ AFG70_vlt4 vs SYR72_ , SYR72_119 vs PAK69_lah1972, SYR72_119 vs IRN72_tbrz19721 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

34 Smallpox Vaccine Its history is strictly connected to the birth of modern vaccinology. Variolation 1796: Edward Jenner. 1977: last case of smallpox.

35 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!!.

36 The case of influenza virus

37 The evolutionary power of antigenic shift Name of pandemic Subtype involved Pandemic severity index Asiatic (Russian) H2N2NA SpanishH1N15 AsianH2N22 Hong KongH3N22 “Swine”H1N1NA The last known flu pandemics

38 Genesis of “Swine flu” H1N1 virus Classical swine flu virus H1N1 Human H3N2 flu virus Avian flu virus (unknown subtype) H3N2 swine virus Swine H1N1 flu virus H1N1 “Swine flu” virus

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

40 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 (Sinha et al., 2009)

41 CONSEQUENCES Smallpox: 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

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

43 What about the effect of resistance on clinical outcomes?

44 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 Adherence Bioavailability Patients’ Metabolism Reservoir

45 Cozzi-Lepri et al., AIDS 2007 RTI resistance at t 0 RTI resistance from t 0 to t 1 Percentage of patients’ viruses who had RTI resistance mutations at t 0 and of those who acquired such mutations from t 0 to t 1 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

46 Logistic regression MultivariateP CD4 time-dependent (per 50 cells increase) 0.79 ( )0.006 Plasma HIV-RNA time-dependent (per 1 log 10 increase) 1.14 ( )0.539 Previous AIDS2.29 ( )0.098 LPV after GRT0.57 ( ) drug class multi- resistance (DCMR) ( )<0.001 Poor survival in drug-class multi-resistance 3 DCMR 2 DCMR 1 DCMR 0 DCMR P at log-rank <0.001 Zaccarelli, AIDS 2005

47 Main Findings Cozzi-Lepri A, et al. AIDS. 2008;22: Definition of Resistance Adjusted RH (95% CI) P Value ≥1 NRTI mutation1.52 ( ).004 ≥1 NNRTI mutation1.95 ( ).002 ≥1 PI mutation (major and minor)1.50 ( ).004 Drug-class resistance mutations1.79 ( ).0007 Cumulative drug-class resistance (major and minor PI mutations counted) Virologic failure with no resistance1.32 ( ).52 Single-class resistance1.03 ( ).90 Double-class resistance1.55 ( ).004 Triple-class resistance1.80 ( ).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

48 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.

49 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

50 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)

51 INMI “L. Spallanzani A. Antinori P. Narciso C. Gori R. d’Arrigo F. Forbici M.P. Trotta A. Ammassari R. Bellagamba M. Zaccarelli G. Liuzzi V. Tozzi P. Sette N. Petrosillo F. Antonucci E. Boumis E. Nicastri U. Visco P. De Longis G. D’Offizi G. Ippolito and the Resistance Study Group ACKNOWLEDGEMENTS University of Rome “Tor Vergata” C.F. Perno F. Ceccherini Silberstein V. Svicher M. Santoro A.Bertoli D. Armenia S. Dimonte L. Fabeni R. Salpini C. Alteri V. Cento F. Stazi S. Dimonte L. Sarmati M. Andreoni

52 Modena and Ferrara Infectious Diseases C. Mussini V. Borghi W. Gennari L. Sighinolfi F. Ghinelli G. Rizzardini V. Micheli A. Capetti L. Sacco University Hospital The I.CO.N.A. Study Group A. d’Arminio Monforte M. Moroni ACKNOWLEDGEMENTS University of Catanzaro S. Alcaro A. Artese Catholic University of Rome, Sacro Cuore A. De Luca R. Cauda Infectious Diseases Unit Florence S. Lo Caputo F. Mazzotta San Gallicano Hospital G. Palamara M. Giuliani Infectious Diseases, Bergamo F. Maggiolo AP. Callegaro University of Turin G. Di Perri S. Bonora Arca M. Zazzi University of Padova G. Palu’ S. Parisi University of Rome Tor Vergata Dept. of Mathematics Livio Triolo Mario Santoro University Cergy-Pontoise LPTM Thierry Gobron University of S. Raffaele A. Lazzarin M. Clementi


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