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

Amalio TELENTIInstitute of Medical Microbiology

CHUV – UNILwww.chuv.ch/imul

HIV and Host HIV and Host GeneticsGenetics

•Genetic riskGenetic risk

•Predicting appropriate drug levelsPredicting appropriate drug levels

•Treatment choiceTreatment choice

•What comes nextWhat comes next

•A view on human populationsA view on human populations

>5%1%<<<<<<1%

Severe ????? Mild

Primary immuno-deficiencies

?????????Common trait disease

Genetic frequency in a populationGenetic frequency in a population

Disease manifestation / riskDisease manifestation / risk

Understanding genetic riskUnderstanding genetic riskHow do we do it?How 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 ).

Homozygous 1

Heterozygous

Homozygous 2

Genome-wide genotypingGenome-wide genotyping

500.000 to 1.000.000 SNPs/individual

HCP5/HLA-B*5701HLA-C –35ZNRD1/HLA-A10CCR532

Fellay et al.

Genome-wide results (2554 individuals) Genome-wide results (2554 individuals) Genetic score and HIV-1 progressionGenetic score and HIV-1 progression

Elite controllersElite controllersLTNP-viremic controllersLTNP-viremic controllersLTNP-non viremic controllersLTNP-non viremic controllersProgressorsProgressorsRapid progressorsRapid progressors

Clinical definition Clinical definition Genetic markers Genetic markers

CD4 evolution of Rapid Progressors (n=73) CD4 evolution of Rapid Progressors (n=73) during 3 years after seroconversion during 3 years after seroconversion

Red: associated with an AIDS event/death

<350 CD4 T cells

0 365 730 10950

100

200

300

400

500

600

700

800

900

1000

Days post SC

CD

4 T

cel

l co

un

t

Martinez-Picado & Telenti

Rapid progression – a genetic extreme Rapid progression – a genetic extreme

ECLTNP

CNP P RP

0.0

0.5

1.0

CCR2 V64ICCR5 32

HLA-C

ZNRD1HLA-A+

HLA-B+

CCR5_HHE/HHE

HLA-B-

CCR5_H+/H+

CCR5_P1/P1

Alle

lic f

req

uen

cy o

r p

rop

ort

ion

Casado et al.

Integrating host and viral parametersIntegrating host and viral parameters

Casado et al.

Results - IResults - 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.






Cou

nt

0

5

10

15

20

25

30

35

log10 EFV AUC (µg*h/ml)

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Cum

ulat

ive

Fre

quen

cy

1

10

30

50

70

90

99

99.9

99.99

Cou

nt

0

5

10

15

20

25

30

35

log10 EFV AUC (µg*h/ml)

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Cum

ulat

ive

Fre

quen

cy

1

10

30

50

70

90

99

99.9

99.99

Distribution of Efavirenz AUC valuesRotger et al. Clin Pharm Ther 2007

Extensive metabolizer

Slow metabolizerRapid metabolizer

8-OH-EFV

N

CF 3

O

H

Cl

OH

CYP2A6>2B6

CYP2B6> 3A5>3A4

EFV 7-OH-EFV

8,14-(OH)2-EFV

CYP2B6>2D6>2C19>2A6>2C9

8-OH-EFV-O-gluc

7-OH-EFV-O-sulf

7-OH-EFV-O-gluc

EFV-N-gluc

…UGT?

8,14-(OH)2-EFV-O-gluc

…UGT? …UGT?

…UGT?

…SULT?

7-OH-EFV-O-sulf

…SULT?

7-OH-EFVN

CF3

O

Cl

OH OHOH

OHOOC

N

CF3

O

H

Cl OH

OH

O

OHOH

OH

HOOC

OH

N

CF3

O

H

Cl

ON

CF3

O

H

Cl

OO

OHOH

OH

HOOC

N

CF 3

O

H

Cl

OSO 3-

OH OHOH

OHOOC

N

CF3

O

H

Cl

O

N

CF3

O

H

Cl

SO 4-

N

CF3

O

H

Cl

N

CF3

O

H

Cl

OH

EFV metabolic pathways

8-OH-EFV

N

CF

O

H

Cl

OH

CYP2A6>2B6

CYP2B6> 3A5>3A4

EFV 7-OH-EFV

8,14-(OH)2-EFV

CYP2B6>2D6>2C19>2A6>2C9

8-OH-EFV-O-gluc

7-OH-EFV-O-sulf

7-OH-EFV-O-gluc

EFV-N-gluc

…UGT?


…UGT? …UGT?

…UGT?

…SULT?

7-OH-EFV-O-sulf

…SULT?

7-OH-EFVN

CF3

O

Cl

OH OHOH

OHOOC

N

CF3

O

H

Cl OH

OH

O

OHOH

OH

HOOC

OH

N

CF3

O

H

Cl

ON

CF3

O

H

Cl

OO

OHOH

OH

HOOC

N

CF 3

O

H

Cl

OSO 3-

OH OHOH

OHOOC

N

CF3

O

H

Cl

O

N

CF3

O

H

Cl

SO 4-

N

CF3

O

H

Cl

N

CF3

O

H

Cl

OH

3


8-OH-EFV

N

CF

O

H

Cl

OH

CYP2A6>2B6

CYP2B6> 3A5>3A4

EFV 7-OH-EFV

8,14-(OH)2-EFV

CYP2B6>2D6>2C19>2A6>2C9

8-OH-EFV-O-gluc

7-OH-EFV-O-sulf

7-OH-EFV-O-gluc

EFV-N-gluc

…UGT?


…UGT? …UGT?

…UGT?

…SULT?

7-OH-EFV-O-sulf

…SULT?

7-OH-EFVN

CF3

O

Cl

OH OHOH

OHOOC

N

CF3

O

H

Cl OH

OH

O

OHOH

OH

HOOC

OH

N

CF3

O

H

Cl

ON

CF3

O

H

Cl

OO

OHOH

OH

HOOC

N

CF 3

O

H

Cl

OSO 3-

OH OHOH

OHOOC

N

CF3

O

H

Cl

O

N

CF3

O

H

Cl

SO 4-

N

CF3

O

H

Cl

N

CF

O

H

Cl

OH

3

3


CYP2A6 CYP2A6 & CYP3A4 and Efavirenz Pharmacokinetics

4.00

4.25

4.50

4.75

5.00

5.25

5.50

5.75

6.00

Lo

g1

0 E

FV

pla

sm

a A

UC

(u

g*h

/ml)

Hom LOF Het LOFCYP2B6: Het GOF Normal funct.

4321-321-CYP2A6CYP3A4

Total LOF: 4321-321-

Di Iulio, PGG 2009, Arab CPT 2009

CYP2B6CYP2B6

CYP2A6/3ACYP2A6/3A

0

2000

4000

6000

8000

10000

12000

14000Pre-adjustment

Screening Baseline Week 6 Week 10 Week 24

EF

V c

on

ce

ntr

ati

on

[n

g/m

l]

CDB076 – Calmy et al -Therapeutic drug monitoring (TDM) enables efavirenz dose reduction in virologically-controlled patients

Therapeuticrange

600200 « LOF » genotype 600200 « DOF » genotype600400 « DOF » genotype 600400 « LOF » genotype

TDM vs Genotype-driven efavirenz dose adjustementTDM vs Genotype-driven efavirenz dose adjustement

Low Low OutliersOutliers (n=121) (n=121)

Extreme (>P85%) Extreme (>P85%) (n=92)(n=92)

CasesCases ControlsControls

Lopinavir/r Pharmacokinetic-GeneticsLopinavir/r Pharmacokinetic-Genetics

Lubomirov et al.

Lopinavir/r Pharmacokinetic-GeneticsLopinavir/r Pharmacokinetic-Genetics

CYP3AOATP1B1

MRP2

LPV

Lubomirov et al.

Manolio Nat Genet 2009

Dyslipidemia as common genetic traitDyslipidemia as common genetic trait

Rotger et al. 2009

Results - IIResults - 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.






We have a problem

B. Ledergerber, SHCS

Tools for initiating antiretroviral Tools for initiating antiretroviral therapy in HIV-1 infected individualstherapy in HIV-1 infected individuals

• CD4 cell count • Viremia• Clinical symptoms• Viral genetics (primary drug

resistance)

Can host genetic information add Can host genetic information add something?something?

Can host genetic information add Can host genetic information add something?something?

DRUGS WITH GENETIC MARKERS

Efavirenz

Atazanavir

Lopinavir

Tenofovir

Abacavir

INTERMEDIATE PHENOTYPE

High plasma levels

Increased bilirubin

Increased lipid levels

Phosphaturia, glucosuria, etc…

CLINICAL EFFECT

CNS toxicity

Gilbert syndrome

Cardiovascular diseases

Renal proximal tubulopathyHypersensitivity

HypothesisHypothesisIndividuals carrying risk genetic markers will discontinue the initial treatment more frequently/earlier than individuals without

0 60 120 180 240 300 3600

1020

304050

60708090

100without GR

with GR

Estimated rates of global (n = 577)discontinuation at 1 yearaccording to genetic risk

506

71

No. at Riskwithout GR

with GR

458

59

432

51

411

44

380

38

347 305

35

50.2%

31.2%

aHR = 1.76 (CI95%: 1.18 - 2.63)

Pa= 5.65 x 10-3

29

Time to discontinuation (days)

Glo

bal d

isco

ntinuat

ion (%

)

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

b)a) a) b)

0 60 120 180 240 300 3600

20

40

60

80

100without GRwith GR

Estimated rates of EFV (n = 272)discontinuation at 1 yearaccording to genetic risk

259

13


with GR

239

11

225

9

216

8

203

6

191

6 3

71.2%

28.1%

aHR = 3.43 (CI95% 1.48 - 7.95)

P= 0.004

177


EFV discontinuation (%)

0 60 120 180 240 300 3600

20

40

60

80

100without GRwith GR

Estimated rates of EFV (n = 272)discontinuation at 1 yearaccording to genetic risk

259

13


with GR

239

11

225

9

216

8

203

6

191

6 3

71.2%

28.1%

aHR = 3.43 (CI95% 1.48 - 7.95)

P= 0.004

177


EFV discontinuation (%)

a) b)

0 60 120 180 240 300 3600

20

40

60

80

100without GR

with GR

Estimated rates of LPV (n = 184)discontinuation at 1 year

according to genetic score 2gRn

168

16


with GR

159

15

148

12

136

11

124

10

109

9 6

46.5%

33.1%

aHR = 1.28 (CI95%: 0.53 - 3.12)

P = 0.587

87


LPV discontinuation (%)

a) b)

0 60 120 180 240 300 3600

20

40

60

80

100without GR

with GR

Estimated rates of LPV (n = 184)discontinuation at 1 year

according to genetic score 2gRn

168

16


with GR

159

15

148

12

136

11

124

10

109

9 6

46.5%

33.1%

aHR = 1.28 (CI95%: 0.53 - 3.12)

P = 0.587

87


LPV discontinuation (%)

a) b)

0 60 120 180 240 300 3600

20

40

60

80

100without GR

with GR

Estimated rates of ATV (n = 121)discontinuation at 1 year

according to UGT1A1*28, *37 (rs8175347)

103

18


with GR

97

14

94

12

93

10

87

8

81

7 6

62.5%

18.9%

68

aHR = 6.10 (CI95%:2.78 - 13.32)

P = 6.00 x 10-6


ATV discontinuation (%)

a) b)

0 60 120 180 240 300 3600

20

40

60

80

100without GR

with GR

Estimated rates of ATV (n = 121)discontinuation at 1 year

according to UGT1A1*28, *37 (rs8175347)

103

18


with GR

97

14

94

12

93

10

87

8

81

7 6

62.5%

18.9%

68

aHR = 6.10 (CI95%:2.78 - 13.32)

P = 6.00 x 10-6


ATV discontinuation (%)

Tenofovir Efavirenz

Lopinavir/r Atazanavir/r






•Telenti A. F1000 Biology Reports 2009

Integration of large scale genome/cellular dataIntegration of large scale genome/cellular data

•Bushman et al. PLoS Pathogen 2009

Integrating expression and Integrating expression and genetic variationgenetic variation

Viral control (Phenotype)Viral control (Phenotype)Viral control (Phenotype)Viral control (Phenotype)

Genetic variationGenetic variationGenetic variationGenetic variation

Expression variationExpression variationExpression variationExpression variation

Co-factorsCo-factorsCo-factorsCo-factors

How do we do it?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.

Transcriptome analysis in

CD4 T cells from 127 HIV-infected

individuals

Low

High

Rotger et al.

Hea

lthy

cont

rols

Elite

co

ntro

llers

Roger et al.

Expression variants influencing HIV-1 disease?

48000 transcripts

260 genes differentially

expressed during HIV-1 infection

190 genes under cis-acting SNP

modulating expression

300K gene-centric SNPs

1

OAS1

Susceptibility to West Nile Virus Susceptibility to HIV?

The next frontierThe next frontier

http://www.ipadrblog.com/BlindMenandElephant.jpg

>5%1%<<<<<<1%

Severe ????? Mild

Primary immuno-deficiencies

RARE AND RARE AND PRIVATE PRIVATE

MUTATIONSMUTATIONS

Common trait disease

Genetic frequency in a populationGenetic frequency in a population

Disease manifestation / riskDisease manifestation / risk

“…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.”

Whole Genome SequencingWhole Genome Sequencing

James WATSON






The genomics of human ancestryThe genomics of human ancestry

Tishkoff et al. Science 2009

Human ancestry in genetic studiesHuman ancestry in genetic studies

ATTENTION!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!HOWEVER!• More differences found across individuals than across populations.•Causal variants are equally functional across populations.• Markers (non-causal) may not work across populations.

ConclusionsConclusions• 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

University of LausanneM. Rotger J. di IulioS. ColomboR. LubomirovL. DecosterdC. CsajkaT. BuclinP. TarrM. Cavassini

University of GenevaA. Calmy P. Descombes

Duke UniversityJ. FellayK. DangE. Heinzen D. Goldstein

SHCSA. RauchH. GünthardB. LedergerberH. FurrerB. Hirschel

CNM – Carlos III MadridC. Lopez-Galindez

Documents

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