The next frontier

1. The next frontier Aspectos genòmicos y factores de protecctiòn Amalio Telenti, University of Lausanne

2. #1 Exploiting differences among pathogens #2 Exploiting extreme phenotypes #3 Exploiting technological breakthroughs

3. #1 - Exploiting differences amongpathogens

4. A 32 year-old person is found at the time of a blood donation to be HIV+/HCV+. • His HIV viremia is 4.9 log copies/ml, CD4 T cells are 168 cell/ul. • His HCV viremia is undetectable, liver tests are normal.

5. Genome analyses: How do we do it? • DNA from a large number of individuals • Large scale genotyping of common human variation (500’000 – 1 moi polymorphisms). • Association analysis with correction for the large number of tests (significative p-values should be <10-7 to 10-8

6. Genome-wide genotyping Homozygous 1 Heterozygous Homozygous 2 500.000 to 1.000.000 SNPs/individual

7. HCV Chr. 19 Chromosomal location of locus of susceptibility to HIV-1 and to Hepatitis C HIV Chr. 6 N=478 HIV+ N=1350 HepC+ Fellay et al Science 2007 Rauch el al. Submitted

8. Survival/Progression rs2395029 (HLA-B*5701) rs9264942 (HLA-C -35) rs9261174 (ZNRD1) rs333 (CCR5Δ32) Fellay et al, Years

9. Genetic score and progression

10. HCV Chr. 19 Chromosomal location of locus of susceptibility to HIV-1 and to Hepatitis C HIV Chr. 6 N=478 HIV+ N=1350 HepC+ Fellay et al Science 2007 Rauch el al. Submitted

11. HCV - Genetic determinants of spontaneous clearance and treatment success

12. HCV - Viral and genetic determinants of treatment success

13. GWAS Results • We have reached experimental power conditions to identify most common human (Caucasian) variation influencing susceptibility to HIV-1 • We can know explain 22% of population variance by genetics, population effects, gender and age. • Clear and profoundly different signals for various pathogens (n=2).

14. #2 - Exploiting extreme phenotypes

15. HIV- HIV+ A rapid progressor

16. Rapid progression – a genetic extreme

17. Integrating host and viral parameters Casado et al.

18. « Sooty-like » patterns of evolution

20. A genomic, tanscriptomic and immunogenetic study of rapid progression Red: associated with an AIDS event/death <350 CD4 T cells CD4 evolution of Rapid Progressors (n=73) during 3 years after seroconversion

21. CD4 T cell analysis

22. Interferon-stimulated genes CD4 T cell analysis

23. Interferon stimulated genes Rapid progressors versus Sooty-like profiles More expressed in Rapid progressors More expressed in Sooty-like CD4 T cell analysis

24. PM AGM Gene expression changes in African green monkeys (natural host model) and Asian pigtailed macaques (pathogenic model) between day 10 and day 45 post infection. Lederer et al. PLoS Pathogens 2009

25. Interferon-stimulated genes T cell receptor signalling CD4 T cell analysis

26. T cell receptor signalling

27. Kaufmann DE, Walker BD. PD-1 and CTLA-4 inhibitory cosignaling pathways in HIV infection and the potential for therapeutic intervention. J Immunol. 2009

28. Transcriptome Results • The analysis of extreme phenotypes (beyond elite controllers) remains of major interest. • New profiles, such as the rare “sooty-like” can be very informative and directly link to some of the non-pathogenic primate models. • However….what to do with the long lists of candidate genes??

29. #3 - Exploiting technological breakthroughs

30. A severe hemophilia patient received multiple blood transfusions through the early 1980’ies. • Today this patient remains HIV negative, while HCV positive

31. Characterization of high-risk HIV-1 seronegative hemophiliacs. Salkowitz et al. Among hemophiliacs from the MACS who remained HIV-1 seronegative despite a high (94%) risk for acquisition of HIV-1 infection, 7/43 (16%) were homozygous for the protective CCR5 Delta32 polymorphism. Among the remainder, neither CCR5 density nor beta-chemokine production, nor in vitro susceptibility to infection with the HIV-1 isolate JR-FL could distinguish HRSN hemophiliacs from healthy controls. Clin Immunol. 2001 Feb;98(2):200-11.

32. Genetic frequency in a population <<<<<<1% 1% >5% Primary immuno-deficiencies Common trait disease ????????? Severe ????? Mild Disease manifestation / risk

33. Genetic frequency in a population <<<<<<1% 1% >5% Primary immuno-deficiencies Common trait disease Severe ????? Mild Disease manifestation / risk

34. Genetic frequency in a population <<<<<<1% 1% >5% RARE AND PRIVATE MUTATIONS Primary immuno-deficiencies Common trait disease Severe ????? Mild Disease manifestation / risk

35. Whole Genome Sequencing James WATSON “…some 11,000 of Watson’s SNPs (15% novel) are predicted to change the amino-acid sequence — and so, perhaps, the function — of a protein.”

36. Where could be the genes of interest?

37. HIV-1 group M Homo sapiens 100 SIVcpz2 Pan paniscus SIVcpz5 46 Pan troglodytes verus HIV-1 group N 100 79 Pan troglodytes schweinfurthi SIVcpz1 95 Pan troglodytes vellerosus SIVcpz4 100 100 HIV-1 group O SIVcpzANT Gorilla gorilla SIVcpzTAN1 100 SIVmnd3 100 Pongo pygmaeus 100 SIVmnd1 96 hylobates lar SIVdrll 100 hylobates syndactylus SIVrcm2 hylobates leucogenys SIVrcm1 100 SIVsm3 Mandrillus sphinx HIV-2 B 84 100 Mandrillus leucophaeus HIV-2 A 100 Cercocebus torquatus SIVstm 85 Cercocebus atys SIVsm4 100 100 SIVsm1 100 SIVsm2 SIVmne 100 100 Macaca nemestrina 97 SIVmac1 Macaca mulatta 100 SIVmac3 100 Macaca arctoides SIVmac2 97 SIVagmSab Cercopithecus sabaeus SIVagmTan Cercopithecus tantalus 100 SIVagmGri 100 SIVagmVer1 Cercopithecus aethiops 92 SIVagmVer2 100 SIVdeb Cercopithecus neglectus 100 SIVden Cercopithecus denti SIVsyk1 100 Cercopithecus albogularis 100 SIVsyk2 SIVmon Cercopithecus mona 86 SIVmus Cercopithecus cephus 100 SIVgsn1 92 Cercopithecus niticans SIVgsn2 100 SIVmnd4 SIVhoest Cercopithecus lhoesti 100 SIVsun 100 Cercopithecus solatus SIVcol Guereza colobus Callithrix jacchus SIV Human jump SIV Simian jump? Lemurs

38. Ortiz et al Mol Biol Evol 2009

39. siRNA/shRNA screens for genes needed for HIV replication in human cells Brass et al. Science 2008. Konig et al. Cell 2008. Zhou et al. Cell Host Microbe 2008 Jeung et al. J Biol Chem 2009. • >1000 gene candidates • Only 3 genes common to at least three studies. • 38 genes common to 2 or more studies. • No restriction factors identified

40. Predicted interaction networks of genes identified as HIV dependency factors in silencing screens and differentially expressed during HIV-1 infection. NF-kappa-B Proteasome Mediator complex Protein kinases Nuclear pore

42. Putting it to work Analysis Law Ethics Equipment Materials

45. Haas

46. Genotyping Imaging Proteomics Transcriptomics

47. Mathematics, statistics and computer sciences "Scientists have learned to expect everything from mathematicians short of actual help" John HAMMERSLEY, Bull Inst Math Appl 10, 235, 1974

48. The role of the physician • Identification of study phenotyes • Avoiding low-power, limited scope studies. • Bringing the best predictors to clinical use.

49. VIRAL LOAD GENETICS - Effect estimates in Genome-wide studies: Recruiting seroprevalent versus seroconverter individuals <Stronger in seroprevalent Stronger in seroconverters>

50. Final Conclusions • The genetic basis of human susceptibility to HIV-1 susceptibility to infection includes common variants (probably known by now), and a undefined number of rare variants. • Technological breakthroughs are not adequately supported by clinical cohorts.