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Amalio TELENTI Institute of Medical Microbiology CHUV – UNIL chuv.ch/imul

HIV and Host Genetics. Amalio TELENTI Institute of Medical Microbiology CHUV – UNIL www.chuv.ch/imul. Genetic risk Predicting appropriate drug levels Treatment choice What comes next A view on human populations. Genetic frequency in a population. <<<<<<1%. 1%. >5%.

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Amalio TELENTI Institute of Medical Microbiology CHUV – UNIL chuv.ch/imul

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  1. HIV and Host Genetics Amalio TELENTI Institute of Medical Microbiology CHUV – UNIL www.chuv.ch/imul

  2. Genetic risk • Predicting appropriate drug levels • Treatment choice • What comes next • A view on human populations

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

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

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

  6. Genome-wide results (2554 individuals) Genetic score and HIV-1 progression HCP5/HLA-B*5701 HLA-C –35 ZNRD1/HLA-A10 CCR5D32 Fellay et al.

  7. Genetic markers Clinical definition Elite controllers LTNP-viremic controllers LTNP-non viremic controllers Progressors Rapid progressors

  8. CD4 evolution of Rapid Progressors (n=73) during 3 years after seroconversion <350 CD4 T cells Red: associated with an AIDS event/death Martinez-Picado & Telenti

  9. Rapid progression – a genetic extreme Casado et al.

  10. Integrating host and viral parameters Casado et al.

  11. Results - I • We can now explain up to 22% of population differences in viral load on the basis of common variants, demographics and population factors. • At the individual level, these type of data may translate into prediction of disease progression.

  12. Genetic risk • Predicting appropriate drug levels • Treatment choice • What comes next • A view on human populations

  13. Extensive metabolizer Slow metabolizer Rapid metabolizer Distribution of Efavirenz AUC values Rotger et al. Clin Pharm Ther 2007

  14. EFV metabolic pathways C F 3 Cl 7-OH-EFV-O-sulf S O - N O C F 4 3 H Cl …SULT? EFV-N-gluc O N H C F …UGT? C F EFV 3 CYP2A6>2B6 3 Cl Cl 7-OH-EFV 7-OH-EFV N O H O N O CYP2B6> 3A5>3A4 H H O O C O …UGT? H O O H H O C F 3 C F Cl 3 C F Cl 3 CYP2B6>2D6> 2C19>2A6>2C9 O H Cl O N H O O C O O N O H O O H O N O H H 8-OH-EFV H H O 7-OH-EFV-O-gluc O H H 8,14-(OH)2-EFV …SULT? …UGT? …UGT? C F 3 Cl C F 3 C F 3 Cl O H Cl O N O S O - H 3 O H O O C N O H O O C N 7-OH-EFV-O-sulf O O O H O H H O H O H O H O O H O H 8,14-(OH)2-EFV-O-gluc 8-OH-EFV-O-gluc

  15. EFV metabolic pathways C F 3 Cl 7-OH-EFV-O-sulf S O - N O C F 4 3 H Cl …SULT? EFV-N-gluc O N H C F …UGT? C F EFV 3 CYP2A6>2B6 3 Cl Cl 7-OH-EFV 7-OH-EFV N O H O N O CYP2B6> 3A5>3A4 H H O O C O …UGT? H O O H H O C F 3 C F Cl 3 C F Cl 3 CYP2B6>2D6> 2C19>2A6>2C9 O H Cl O N H O O C O O N O H O O H O N O H H 8-OH-EFV H H O 7-OH-EFV-O-gluc O H H 8,14-(OH)2-EFV …SULT? …UGT? …UGT? C F 3 Cl C F 3 C F 3 Cl O H Cl O N O S O - H 3 O H O O C N O H O O C N 7-OH-EFV-O-sulf O O O H O H H O H O H O H O O H O H 8,14-(OH)2-EFV-O-gluc 8-OH-EFV-O-gluc

  16. EFV metabolic pathways C F 3 Cl 7-OH-EFV-O-sulf S O - N O C F 4 3 H Cl …SULT? EFV-N-gluc O N H C F …UGT? C F EFV 3 CYP2A6>2B6 3 Cl Cl 7-OH-EFV 7-OH-EFV N O H O N O CYP2B6> 3A5>3A4 H H O O C O …UGT? H O O H H O C F 3 C F Cl 3 C F Cl 3 CYP2B6>2D6> 2C19>2A6>2C9 O H Cl O N H O O C O O N O H O O H O N O H H 8-OH-EFV H H O 7-OH-EFV-O-gluc O H H 8,14-(OH)2-EFV …SULT? …UGT? …UGT? C F 3 Cl C F 3 C F 3 Cl O H Cl O N O S O - H 3 O H O O C N O H O O C N 7-OH-EFV-O-sulf O O O H O H H O H O H O H O O H O H 8,14-(OH)2-EFV-O-gluc 8-OH-EFV-O-gluc

  17. CYP2A6CYP2A6&CYP3A4and Efavirenz Pharmacokinetics CYP2B6: Normal funct. 4 4 3 3 3 3 2 2 2 2 - - 1 1 1 1 - - Het GOF Het LOF Hom LOF Total LOF: CYP2A6 CYP3A4 Di Iulio, PGG 2009, Arab CPT 2009 CYP2B6 CYP2A6/3A

  18. TDM vs Genotype-driven efavirenz dose adjustement 600200 « LOF » genotype  600200 « DOF » genotype 600400 « DOF » genotype  600400 « LOF » genotype Therapeuticrange CDB076 – Calmy et al -Therapeutic drug monitoring (TDM) enables efavirenz dose reduction in virologically-controlled patients

  19. Cases Controls Extreme (>P85%) (n=92) Low Outliers (n=121) Lopinavir/r Pharmacokinetic-Genetics Lubomirov et al.

  20. CYP3A OATP1B1 LPV MRP2 Lopinavir/r Pharmacokinetic-Genetics Lubomirov et al.

  21. Dyslipidemia as common genetic trait Manolio Nat Genet 2009 Rotger et al. 2009

  22. Results - II • For a number of drugs, we have a good understanding of the genetic determinants of plasma drug levels. • It helps evaluate the correlation of drug levels-genes-toxicity. • This information could lead to dose adjustement.

  23. Genetic risk • Predicting appropriate drug levels • Treatment choice • What comes next • A view on human populations

  24. We have a problem B. Ledergerber, SHCS

  25. Tools for initiating antiretroviral therapy in HIV-1 infected individuals CD4 cell count Viremia Clinical symptoms Viral genetics (primary drug resistance) Can host genetic information add something?

  26. CLINICAL EFFECT CNS toxicity Gilbert syndrome Cardiovascular diseases Renal proximal tubulopathy Hypersensitivity INTERMEDIATE PHENOTYPE DRUGS WITH GENETIC MARKERS High plasma levels Increased bilirubin Increased lipid levelsPhosphaturia, glucosuria, etc… Efavirenz Atazanavir Lopinavir Tenofovir Abacavir

  27. Hypothesis Individuals carrying risk genetic markers will discontinue the initial treatment more frequently/earlier than individuals without

  28. Analysis of 577 individuals starting first line ART 2004-2007

  29. Tenofovir Efavirenz Lopinavir/r Atazanavir/r

  30. Genetic risk • Predicting appropriate drug levels • Treatment choice • What comes next • A view on human populations

  31. Integration of large scale genome/cellular data • Bushman et al. PLoS Pathogen 2009 • Telenti A. F1000 Biology Reports2009

  32. Integrating expression and genetic variation Viral control (Phenotype) Co-factors Genetic variation Expression variation

  33. How do we do it? • Choice of tissue/cell type • Clinical/lab conditions (perturbations) • Genome-wide transcription analysis • Gene and pathway analysis • Search for genetic variants influencing gene expression.

  34. Transcriptome analysis in CD4 T cells from 127 HIV-infected individuals Low High

  35. Rotger et al.

  36. Healthy controls Elite controllers Roger et al.

  37. OAS1 Susceptibility to West Nile Virus Susceptibility to HIV? Expression variants influencing HIV-1 disease? 300K gene-centric SNPs 48000 transcripts 260 genes differentially expressed during HIV-1 infection 190 genes under cis-acting SNP modulating expression 1

  38. The next frontier http://www.ipadrblog.com/BlindMenandElephant.jpg

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

  40. 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.”

  41. Genetic risk • Predicting appropriate drug levels • Treatment choice • What comes next • A view on human populations

  42. The genomics of human ancestry Tishkoff et al. Science 2009

  43. Human ancestry in genetic studies • ATTENTION! • Genetic/genomic studies need to take population origin into account (« pop. stratification »). • Frequencies of alleles may vary substantially (eg. HLA, Cytochrome P450). • Less information available on non-Caucasians. • HOWEVER! • More differences found across individuals than across populations. • Causal variants are equally functional across populations. • Markers (non-causal) may not work across populations.

  44. Conclusions • We can know explain 22% of population variance in viral load by genetics, population effects, gender and age. • We can explain pharmacokinetics for an increasing number of drugs in ART • Useful predictive strategies might be brought to clinical use. • Integrating data and understanding the role of rare and private mutations is the next step

  45. Duke University J. Fellay K. Dang E. Heinzen D. Goldstein University of Lausanne M. Rotger J. di Iulio S. Colombo R. Lubomirov L. Decosterd C. Csajka T. Buclin P. Tarr M. Cavassini University of Geneva A. Calmy P. Descombes CNM – Carlos III Madrid C. Lopez-Galindez • SHCS • Rauch • H. Günthard • B. Ledergerber • H. Furrer • B. Hirschel

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