PPARA: A Novel Genetic Determinant of CYP3A4 In Vitro and In Vivo

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PPARA: A Novel Genetic Determinant of CYP3A4 In Vitro and In Vivo Kathrin Klein1, Maria Thomas1, Stefan Winter1, Andreas K. Nussler2, Mikko Niemi3, Matthias Schwab1,4 and Ulrich M. Zanger1 Interindividual variability in cytochrome P450 3A4 (CYP3A4) is believed to be largely heritable; however, predictive genetic factors have remained scarce. Using a candidate-gene approach in a human liver bank, we identified singlenucleotide polymorphisms (SNPs) in the Ah-receptor nuclear translocator (ARNT), glucocorticoid receptor (GR), progesterone receptor membrane component 2 (PGRMC2), and peroxisome proliferator–activated receptor-α (PPARA) that are associated with CYP3A4 phenotype. Validation in atorvastatin-treated volunteers confirmed a decrease in atorvastatin-2-hydroxylation in carriers of PPARA SNP rs4253728. Homozygous carriers expressed significantly less PPAR-α protein in the liver. Moreover, shRNA-mediated PPARA gene knockdown in primary human hepatocytes decreased expression levels of the PPAR-α target ACOX1 and of CYP3A4 by more than 50%. In conclusion, this study identified novel genetic determinants of CYP3A4 that, together with nongenetic factors, explained 52, 55, and 33% of hepatic CYP3A4 mRNA, protein, and atorvastatin-2-hydroxylase activity, respectively. These findings have implications for variability in response to drug substrates of CYP3A4. Inter- and intraindividual variability in the expression and activity of drug metabolizing enzymes, transporters, and their regulators have been recognized as major determinants of drug response.1,2 The identification of factors influencing variability (genetic polymorphisms as well as nongenetic factors) currently constitutes a major effort toward personalized medicine.3,4 In humans, cytochrome P450 3A4 (CYP3A4) is particularly important because it has broad substrate specificity for ­substances in most therapeutic categories5,6; it is also prominently expressed in the liver and gut, making it the enzyme responsible for large first-pass metabolism of many drugs.7,8 The expression and function of CYP3A4 can vary several-fold within the same individual at different time points depending on environmental influences such as inflammatory disease, 9 drug interactions due to enzyme inhibition, and nuclear receptor PXR- and CAR-mediated gene induction.5,6,8 Interindividual variability, attributable to a combination of genetic and nongenetic factors, is observed to an even greater extent.10–12 The influence of genetics on CYP3A4 activity has been estimated in previous studies. For example, the rate of antipyrine 4-hydroxylation, which is catalyzed mainly by CYP3A4,13 appears to be largely inherited (88%), as shown in twin studies.14 Furthermore, using

a repeated drug administration approach, Kalow and colleagues found evidence for similarly high degrees of heritability of the capacity for CYP3A4 drug oxidation with respect to several ­substrates.15 However, the genetic factors underlying these observations remained obscure. Currently, there are more than 380 known polymorphisms, including at least 46 coding variants of CYP3A4 (dbSNP, http://www.ncbi.nlm.nih.gov/projects/SNP, and CYP allele nomenclature homepage, http://www.­cypalleles. ki.se/cyp3a4.htm). Although a recently discovered intronic single-nucleotide polymorphism (SNP, CYP3A4*22) shows promise because it appears to be associated with decreased CYP3A4 expression and function,16,17 most of the CYP3A4 polymorphisms have very low frequencies, and the phenotypic effects are weak and often controversial.12,18,19 Taken together, the currently known genetic variants at the CYP3A locus cannot account for the proposed high heritability of CYP3A4 metabolic function.4,15,18 Furthermore, recent genome-wide association studies in human liver failed to identify novel genetic markers for CYP3A4.20,21 An obvious but less investigated possibility is that trans-acting genes encoding regulatory transcription factors or proteins that modulate mono-oxygenase activity in other ways contribute to

1Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; 2Department of Traumatology, University Hospital Tübingen, Tübingen,

Germany; 3Department of Clinical Pharmacology, University of Helsinki and HUSLAB, Helsinki University Central Hospital, Helsinki, Finland; 4Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany. Correspondence: Ulrich M. Zanger ([email protected]) Received 15 September 2011; accepted 29 November 2011; advance online publication 18 April 2012. doi:10.1038/clpt.2011.336 Clinical pharmacology & Therapeutics

1

articles CYP3A4 variation.18,22,23 Transcription of CYP3A4 is regulated through several constitutive and inducible pathways. The important ones include liver-enriched transcription factors such as C/EBPα and HNF4α and ligand-dependent nuclear receptors such as the glucocorticoid receptor and the xenosensors PXR and CAR, which activate transcription of the CYP3A4 gene in response to many drugs and other xenobiotics.8,24 In addition, in pathological situations, inflammatory signaling pathways interfere with constitutive expression, leading to significant downregulation.9,25 In this study, we applied a systematic candidate-gene approach to investigate the influence of genetic polymorphisms on CYP3A4 phenotype in a large and well-documented sample of human livers. We selected functional and tagging polymorphisms in several pathways of transcriptional expression, hemoprotein stability, and mono-oxygenase function. In total, we analyzed 334 SNPs in 40 genes to investigate their associations with the quantitative hepatic CYP3A4 phenotypes, namely, mRNA, protein, and enzyme activity. Statistical and biological consistency analysis revealed the involvement of several genes that had not previously been implicated in the regulation of CYP3A4. Studies in volunteers as well as gene knockdown experiments in primary human hepatocytes confirmed a strong impact of peroxisome proliferator–activated receptor-α (PPAR-α) on CYP3A4 expression and function. Our data imply that trans-effects contribute significantly to genetic determination of CYP3A4-dependent drug metabolism phenotype. Results Variability of CYP3A4 expression in human liver

The suitability of atorvastatin 2-hydroxylation as a highly selective marker of CYP3A4 activity has recently been shown in vitro by ­determining specific activities of recombinant CYPs and by correlating the results with those of two other established activity-based markers: verapamil N-demethylation and testosterone 6β-hydroxylation.26 In vivo in humans, the potent CYP3A4 inhibitor itraconazole impairs atorvastatin 2-hydroxylation, whereas the CYP3A4 inducer rifampin (rifampicin) enhances it.27,28 The CYP3A4 phenotypes in the livers in our study sample were highly variable and not normally distributed (Supplementary Table S4 online). All the phenotypes were significantly correlated with one other (activity-protein, rS = 0.78; mRNA–protein, rS = 0.59; activity–mRNA, rS = 0.57; P < 0.0001). It was confirmed that the donor’s sex influences CYP3A4 phenotype at all levels, as previously described for a subset of the samples.10 Furthermore, donors with clinically high levels of the inflammation marker C-reactive protein or of total serum bilirubin had significantly lower CYP3A4 expression (data not shown). All other parameters (age, smoking status, alcohol consumption, presurgical medication, diagnosis, cholestasis, and liver function parameters) were not significantly correlated with any CYP3A4 phenotype (data not shown). SNP selection and genotyping

We selected 40 candidate genes based on their role in regulating CYP3A4 phenotype. We included validated transcriptional 2

regulators described as having promoter binding sites within the CYP3A4 5′-upstream region, including their dimerization partners and coactivators/corepressors, but also less well-investigated regulators such as the Ah-receptor and PPAR-α, mediators of inflammation and proteins involved in heme synthesis and degradation or in the delivery of electrons for the mono-oxygenase reaction (Figure 1). In addition, we analyzed 19 SNPs in the CYP3A locus, including 8 SNPs from a previous study.19 The final set of 334 genotyped SNPs included 16 synonymous and 60 nonsynonymous SNPs in coding regions, 187 SNPs in introns, and 71 SNPs in 5′-/3′-regions. Thirty-eight SNPs were not detected in our study population (11.4%). Genotype frequencies of the 296 SNPs that were present corresponded well with published data in dbSNP (rS = 0.94, P < 0.0001; Supplementary Table S2 online). Association between genotype and CYP phenotype

A total of 252 SNPs with Hardy-Weinberg equilibrium P value >0.0001 and minor allele frequency >5% were further analyzed for possible association with CYP3A4 phenotype. Prior to adjustment for multiple testing, univariate analysis using three different genetic models (dominant, recessive, and log-additive) revealed 51 SNPs (20.2%) in 26 gene loci having association (P value < 0.05) with at least one CYP3A4 phenotype. This association remained with respect to 32 of these SNPs (P value < 0.05) even after correction for nongenetic factors (Supplementary Figure S1 online, Supplementary Table S5 online). A total of 14 SNPs in 8 genes (AhR, ARNT, CYP3A5, IL6ST, NFkB1, NFkB2, PPARA, and SRC1) were associated with the CYP3A4 activity and protein phenotypes, and 5 SNPs in 4 genes (ARNT, GR, PGRMC2, and PPARA) consistently affected all three phenotypes in the same direction (Figure 2, Supplementary Table S5 online). Because of its low frequency, the often-cited CYP3A4*1B promoter ­variant was separately analyzed, but no associations were found (data not shown). Seven of the observed trans-associations remained significant after adjustment for multiple testing. As shown in Table 1, these were the associations between ARNT (rs2134688) and CYP3A4 protein, between GR (rs258747) and PGRMC2 (rs3733260) on the one hand and atorvastatin hydroxylation on the other, and between two linked intronic SNPs in PPARA (rs4253728, rs4823613) on the one hand and both protein and activity phenotypes on the other. The SNPs in ARNT and PPARA showed consistent effects on all phenotypes (including mRNA) before adjustment for multiple testing (Supplementary Table S5 online). Of note, all seven significant associations were revealed within the recessive model and corresponded to up to 7.7-fold lower CYP3A4 expression in carriers of the homozygous variant allele. Multivariate modeling revealed that genetic factors, together with nongenetic factors, explained 52, 55, and 33% of the variability in hepatic microsomal CYP3A4 mRNA, protein, and atorvastatin 2-hydroxylation, respectively (Table 2). The two linked PPARA SNPs—rs4253728 and rs4823613—were the most influential factors with respect to activity; each of them singly explained 8–9% of the variability. The most influential factor with respect to protein was ARNT rs2134688, and with respect www.nature.com/cpt

articles Drug transporters

Ligand-dependent nuclear receptors

Constitutive transcription factors

mRNA

PXR

COUP-TFII

CAR

SLCO1B1 SLCO1B3 SLCO2B1

VDR FXR PPAR-α

RXR-α

RAR-α LXR-α LXR-β

MDR1 LRH1 SHP

AhR AhRR ARNT

Inflammatory signaling

CLEM4

lL-1β IL-1RN

NF-κB

HNF-4α GR USF-1 HNF-1α HNF-3γ c/EBP-α PGC1-α SRC-1

XREM prP

IL-6ST IL-6R

IL-6

LAP-LAP LIP-LIP c/EBP-β

TATA

ATG Heme biosynthesis/metabolism

ALAS1 HMOX1 HMOX2

Electron donors POR CyB5

CYP3A4 Fe3+ Fe2+

Substrate − H + O2 + NADPH + H+

2e− PGRMC1 PGRMC2

Substrate − OH + NADP+ + H2O

Figure 1  Overview of investigated genes with potential influence on CYP3A4 expression or activity. The scheme indicates known and assumed transcriptional regulators of CYP3A4 (upper part) and factors affecting hemoprotein stability and cytochrome P450 mono-oxygenase function (lower part) that were included in the pathway-guided genetic analysis. AhR, aryl hydrocarbon receptor; AhRR, aryl hydrocarbon receptor regulator; ALAS1, δ-aminolevulinatesynthase; ARNT, aryl hydrocarbon receptor nuclear translocator; CAR, constitutive androstane receptor; c/EBP, CCAAT/enhancer binding protein-α; Coup-TFII , COUP transcription factor II ; CyB5, cytochrome B5; CYP3A4, cytochrome P450 3A4; FXR, farnesoid X-receptor; GR, glucocorticoid receptor; HMOX, hemeoxygenase; HNF-1α, hepatocyte nuclear factor 1 homeobox A; HNF3γ, forkhead box A3; HNF-4α, hepatocyte nuclear factor 4α; IL-1β, interleukin-1β; IL-1RN, interleukin-1 receptor antagonist; IL-6, interleukin-6; IL-6R, interleukin-6 receptor; IL-6ST, interleukin-6 signal transducer; LRH-1, liver receptor homolog 1; LXRα/β, liver X-receptor; MDR, multidrugresistance protein; NF-κB, nuclear factor–κB subunits; PGRMC, progesterone receptor membrane component; POR, P450 oxidoreductase; PPAR-α, peroxisome proliferator-activated receptor-α; RAR-α, retinoic acid receptor; RXR-α, retinoid X-receptor; PXR, pregnane X-receptor; SHP, short heterodimer partner; SLCO, solute carrier organic anion transporters; USF-1, upstream stimulatory factor 1; VDR, vitamin D receptor.

to RNA it was C-reactive protein, accounting for 12.1 and 17.5% of the variation, respectively. Impact of candidate SNPs on CYP3A4 activity in human ­volunteers

To validate the identified candidate SNPs in vivo, we genotyped 56 volunteers who had received single-dose atorvastatin during a previously published pharmacogenetics study.29,30 Analysis of atorvastatin and its major CYP3A4-dependent metabolite, 2-OH-atorvastatin, in relation to four selected SNPs showed a reduced 2-OH-atorvastatin/atorvastatin area under the plasma concentration–time curve (AUC0–∞) ratio of 81% (P = 0.044) for PPARA G/A heterozygotes and 50% for the single A/A homozygote in the cohort (Table 3), whereas the ARNT, GR, and PGRMC2 genotypes had no significant influence. We also found that the intronic SNP rs35599367 (CYP3A4*22) was significantly associated with decrease in AUC0–∞ ratio. A twoway analysis of variance indicated that the PPARA rs4253728 (P = 0.002) and CYP3A4 rs35599367 (P < 0.001) variants were independently associated with the AUC0–∞ ratio. A regression model showed that the AUC0–∞ ratio was 21% lower per copy of the PPARA rs4253728A allele and 35% lower per copy of the CYP3A4 rs35599367T allele. Clinical pharmacology & Therapeutics

PPARA gene knockdown in primary hepatocytes

Because PPARA SNPs showed strong and consistent associations with hepatic CYP3A4 phenotypes and also demonstrated in vivo effects, we wanted to carry out a more direct investigation into the functional impact of PPARA on CYP3A4. We constructed specific lentiviral shRNA-vectors to silence the expression of PPAR-α in primary human hepatocytes. As shown in Figure 3, when donor cells from three individuals were infected with shRNA-viruses targeting the 5′- and 3′-regions in PPARA, there was a >50% reduction in the expression of PPAR-α itself, and also of the known target gene ACOX1, as compared with cells treated with nonsilencing shRNA. Remarkably, knockdown of PPAR-α consistently resulted in up to ~70% reduction in mRNA levels of CYP3A4. The measurement of corresponding enzyme activities after PPARA gene silencing resulted in an average reduction as compared with nontargeting control of CYP3A4 activity (atorva­ statin 2-hydroxylation reduced by 35% (95% confidence interval (CI) 21%–46%; P = 0.002, paired t test on log-transformed data), whereas CYP2C9 activity remained unchanged. Polymorphic PPAR-α protein expression in liver

Because the signifi­cantly associated PPARA variant rs4253728 denotes an intronic SNP that is apparently not linked to any 3

articles Protein

Activity 500

*

400

300

ARNT_rs2134688

300

200

200 100

100

0

0 A/A (n = 114)

GR_rs258747

G/A (n = 33)

G/G (n = 3)

A/A (n = 114)

500

500

400

400

300

300

200

200

100

100

0

G/A (n = 33)

G/G (n = 3)

**

0 G/G (n = 35)

G/A (n = 78)

A/A (n = 35)

G/G (n = 35)

G/A (n = 78)

400

300 250

A/A (n = 35)

**

300

200 PGRMC2_rs3733260 150

200

100 100

50 0

0 G/G (n = 92)

G/T (n = 53)

T/T (n = 4)

* 300

PPARα_rs4253728

G/G (n = 92)

G/T (n = 53)

T/T (n = 4)

**

500 400 300

200

200 100 100 0

0 G/G (n = 87)

G/A (n = 53)

A/A (n = 8)

G/G (n = 87)

G/A (n = 53)

A/A (n = 8)

Figure 2  Representative box-and-whisker plots of selected single-nucleotide polymorphisms (SNPs) having significant correlations with cytochrome P450 3A4 protein and activity. For each gene the most significant SNP is shown. Boxes are defined by the 25% and 75% quantile. The median is displayed as a vertical line inside the box. Whiskers are defined by the lowest/highest data point still within 75%/25% quantile ± 1.5 times the interquantile range (75–25% quantile). For purposes of visualization, data points above the upper whiskers and below the lower whiskers are not presented. *P < 0.05; **P < 0.005 (adjusted for nongenetic factors and corrected for multiple testing; see Table 1 for further details).

known amino acid variant, we next investigated PPAR-α expression levels in 46 human liver tissues preselected by rs4253728 genotype. As shown in Figure 4, rs4253728 AA homozygotes had approximately 1.6-fold (95% confidence interval 1.2–2.0) lower PPAR-α protein levels than did heterozygotes and GG homozygotes (P = 0.0033; Wilcoxon–Mann–Whitney test). It should be 4

noted that the relationships between hepatic PPAR-α protein levels and PPARA genotype were very similar to those between CYP3A4 expression/activity phenotypes and PPARA genotype, in that only homozygous carriers showed reduced protein/activity levels. The reason for this is unknown but may be related to the fact that PPARA gene expression depends, in part, on autoregulation.31 www.nature.com/cpt

articles Table 1 Significant genotype–phenotype relationships between candidate-gene SNPs and CYP3A4 phenotypes in multivariate analysis SNP (gene, location) rs2134688 (ARNT, intron)

Protein FC (95% CI)a

BH-adj. Pb

0.13 (0.1,0.4)

0.0091

2-OH-ATV FC (95% CI)a

BH-adj. Pb

0.56 (0.4,0.8)

rs3733260 (PGRMC2, intron)

0.16 (0.1,0.4)

0.0029 0.0038

rs4253728 (PPARA, intron)

0.34 (0.2,0.6)

0.0223

0.26 (0.1,0.5)

0.0013

rs4823613 (PPARA, intron)

0.34 (0.2,0.6)

0.0223

0.26 (0.1,0.5)

0.0013

CYP3A4, cytochrome P450 3A4; SNP, single-nucleotide polymorphism. aFold change (FC) and 95% confidence interval (CI) of CYP3A4 protein/2-OH-ATV levels

between carriers of homozygote variant allele and carriers of homozygote major allele plus heterozygotes (recessive genetic model). Fold changes and 95% CIs were corrected for the following nongenetic factors (corrected phenotypes in parentheses): C-reactive protein (protein, 2-OH-ATV); donor’s sex (protein); total bilirubin levels (protein); smoking behavior (2-OH-ATV); serum gamma glutamyl transferase (2-OH-ATV). bCorresponding Benjamini-Hochberg adjusted association P value.

Table 2  Adjusted R2 (%) for CYP3A4 phenotypes Protein

2-OH-ATV

Univariate modeling   ARNT_rs2134688

6.3

12.1

5.6

  GR_rs258747

−0.3

1.1

6.4

  PGRMC2_rs3733260

−0.7

-0.6

8.2

  PPAR-α_rs4253728

1.3

4.6

8.3

  PPAR-α_rs4823613

1.3

4.6

8.3

  CYP3A5_rs4646450

Genotype (n)

2-OH-Atorvastatin/ atorvastatin AUC0–∞ ratio

ARNT_rs2134688A/G

rs258747 (GR, 3’ flank)

mRNA

Table 3  Pharmacokinetic variables of a single 20-mg oral dose of atorvastatin in 56 healthy white subjects in relation to various genotypes

0.1

3.6

3.3

  CRP

17.5

8.2

3.8

  SEX

10.5

9.2

3.6

  BILI

6.9

7.1

3.3

Multivariate modelinga

  AA (50)

1.09 (32%)

  AG (5)

0.95 (37%)

  GG (1)

1.21

  Ratio AG/AA

0.88 (0.68, 1.13)

  Ratio GG/AA

1.11 (0.65, 1.90)

GR_rs258747 A/G   AA (18)

1.12 (31%)

  AG (17)

1.09 (32%)

  GG (21)

1.03 (35%)

  Ratio AG/AA

0.97 (0.81, 1.17)

  Ratio GG/AA

0.92 (0.77, 1.09)

PGRMC2_rs3733260 G/T   GG (25)

1.06 (30%)

  GT (19)

1.11 (38%)

  TT (3)

1.04 (23%)

  Ratio GT/GG

1.04 (0.89, 1.24)

  Ratio TT/GG

0.98 (0.70, 1.36)

PPAR-α_rs4253728 G/A   GG (44)

1.13 (24%)

  GA (11)

0.92 (49%)*

  AA (1)   Ratio GA/GG   Ratio AA/GG

0.57 0.81 (0.69, 0.96) 0.50

CYP3A4_rs35599367 C/T   CC (44)

1.15 (24%)

  CT (10)

0.75 (37%)†

  Ratio CT/CC

0.65 (0.56, 0.76)

Data are geometric means (geometric coefficients of variation or 90% confidence intervals).

  Nongenetic factors

37.86

20.79

10.90

  Genetic factors

24.64

46.00

21.11

AUC0–∞, area under the plasma concentration–time curve from 0 h to infinity.

  Genetic and nongenetic factors

51.86

54.82

33.34

*P = 0.044 vs. GG genotype, †P < 0.001 vs. CC genotype using Fisher’s least significant difference method after analysis of variance.

Only factors with adjusted R2 ≥ 5% for at least one CYP3A4 phenotype are displayed. BILI, total bilirubin levels; CRP, C-reactive protein; CYP3A4, cytochrome P450 3A4; SEX, donor’s sex. aStepwise model selection; detailed information on factors used within models is in Supplementary Table S6 online.

Discussion

In this study, we identified polymorphisms in several trans­acting factors as novel genetic predictors of CYP3A4 and found evidence that the nuclear receptor PPAR-α is involved in the regulation of CYP3A4 expression in human liver. These conclusions are based on (i) statistical association between candidate-gene polymorphisms and hepatic CYP3A4 expression and activity phenotypes in a study sample of 150 human livers, (ii) a Clinical pharmacology & Therapeutics

validation cohort of 56 genotyped volunteers who were pharmacokinetically characterized for atorvastatin 2-hydroxylation, and (iii) PPARA gene knockdown experiments in primary human hepatocytes. In addition, we confirmed predictive value for two previously known SNPs within the CYP3A locus with respect to expression and/or activity of CYP3A4. Our search for genetic determinants of CYP3A4 within the CYP3A gene locus revealed only one association of limited significance: intronic variant rs4646450 in the CYP3A5 gene was associated with decreases in both protein and activity. This variant, which has previously been shown to be associated with reduced tacrolimus dosage requirements in Japanese patients32 and with serum dihydroepiandrosterone sulfate concentrations,33 5

articles 1.25

mRNA fold change

1.00

0.75

# #

#

#

0.50 # 0.25

0.00

PPARα

ACOX1

PXR

CYP2C9

CYP3A4

Figure 3  PPARA knockdown in primary human hepatocytes. Two shRNA-encoding lentiviral vectors were targeted to PPAR-α mRNA (light bars, shPROX; dark bars, shFAR). mRNA levels were measured in primary hepatocyte cultures obtained from three donors 5 days after infection and compared with mRNA levels measured in cells treated with nontargeting shRNA control vector set at 1.0. Graph shows the mean value of results from three independent experiments, with error bars indicating standard deviations. ACOX1, acyl CoA-oxidase1; PXR, pregnane X-receptor. #, statistically significant (P < 0.05, paired t test).

a

W V H W V H W V H W V HW V H W V H W V H

PPAR-α

β-actin

PPAR-α/β-actin relative protein content

b

2.5 2.0 1.5 1.0 0.5 0.0

GG (n = 16)

GA (n = 16)

AA (n = 14)

Figure 4  PPAR-α protein expression in total liver homogenates selected according to PPARA (rs4253728) genotype. (a) Western blot analysis of total liver tissue homogenate (50 µg) with antibody against PPAR-α and β-actin; (b) PPAR-α protein expression normalized to β-actin expression and grouped by rs4253728 genotype. GG, reference genotype; GA, heterozygotes; AA, homozygous variant. The number of samples in each group is shown in parentheses.

explained ~3–5% of the variabilities in CYP3A4 protein and activity. Remarkably, the well-documented but rare promoter variant CYP3A4*1B, which has been the subject of controversy in the literature,12,34 could not be validated in our study. The intronic SNP rs35599367 (CYP3A4*22), which has recently been shown to affect CYP3A4 expression and activity,16,17 was significantly associated with decreased protein levels by univariate and multivariate analysis (Supplementary Table S5 6

online), but this association was abolished by multiple-testing correction. Although the heterozygous carriers of this variant had 21% reduced activity in the multivariate model, this difference was not statistically significant given the large extent of scattering of the data (95% confidence interval –27% to 51%). Nevertheless, we could confirm a rather strong and statistically significant decrease of 35% in 2-OH-atorvastatin/atorvastatin AUC0–∞ ratio per variant allele in the in vivo cohort. Therefore, the results obtained with the liver samples differed from those obtained from the in vivo cohort with respect to statistical significance. This could have been due to the greater homogeneity of subjects and the almost twofold higher frequency of the variant in the Finnish in vivo cohort. Our pathway-guided approach included more than 300 polymorphisms in 40 genes selected for their known or potential role in regulating CYP3A4 transcription, heme biosynthesis and degradation, and P450 mono-oxygenase function. This approach represents the most systematic and comprehensive analysis of CYP3A4 genetic predictors carried out to date.22,23,35 An additional important input in our candidate-gene study as compared with previous studies is the inclusion of hepatic CYP3A4 phenotypes at three levels—expression of mRNA, expression of protein, and enzymatic activity—which allowed assessment of associated variants for biological consistency. The most significant markers comprised variants in ARNT, GR, PGRMC2, and PPARA that were associated with up to an 87% reduction in protein and/or enzyme activity (Table 1 and Supplementary Table S5 online). The linked PPARA SNPs, rs4253728 and rs4823613, revealed the most consistent results, each of them explaining ~5% and ~9% of the variations in hepatic CYP3A4 protein and activity levels, respectively, and showing a 21% lower 2-OHatorvastatin/atorvastatin AUC0–∞ ratio per copy of the PPARA rs4253728A allele in the in vivo cohort. PPARA (NR1C1) is one of the best-investigated nuclear receptors and has a key role in regulating lipid homeostasis as well as immunomodulatory and anti-inflammatory functions.36,37 It is highly expressed in the liver and regulates gene transcription primarily in fatty acid–catabolizing tissues as a heterodimer with retinoid X-receptor via binding to PPAR-α response elements. Regulation of P450 cytochromes by PPAR-α was described primarily for rodent CYP4A genes, whereas human CYP4A genes do not seem to be regulated in primary hepatocytes.38 Further interactions between PPAR-α and drug detoxification genes are described for certain CYP and phase II enzymes.39 Two recent studies showed induction of CYP3A4 mRNA in human hepatocytes by the agonist WY14 643,40,41 whereas no CYP3A genes were upregulated in mouse hepatocytes.38,40 In our study, we provide new evidence for a role of PPAR-α in the regulation of CYP3A4. First, two linked PPARA variants (rs4253728 and rs4823613) were consistently associated with CYP3A4 at all phenotype levels, and homozygous carriers showed approximately 50% expression and activity (Figure 2). Second, PPARA genotyping predicted CYP3A4 enzyme activity in a separate cohort of volunteers who received a single oral dose of atorvastatin (Table 3). Third, quantification of PPAR-α protein in liver demonstrated a twofold difference in the levels www.nature.com/cpt

articles of this protein between homozygous carriers and noncarriers, for the first time establishing that the expression of this nuclear receptor in humans is polymorphism-dependent. Finally, the silencing of the PPARA gene in primary human hepatocytes resulted in strong downregulation of both CYP3A4 mRNA and activity. One or more of at least three possible mechanisms may explain these findings. First, it is possible that there is indirect regulation by PXR. This important regulator of CYP3A4 was shown to be transcriptionally activated by PPAR-α42 and also translationally induced via repression of inhibitory microRNA 148a.43 Although the likelihood of this mechanism is supported by the observed downregulation of PXR after lentiviral PPAR-α knockdown, it is questionable whether it is sufficient to explain the strong effects seen on CYP3A4 expression (Figure 3). Second, we propose involvement of the known anti-inflammatory properties of PPAR-α; these would be expected to have a positive influence on CYP3A4 expression, which is known to be downregulated in inflammation.9,25 To our knowledge, no direct support for this putative mechanism has thus far been provided. The third possible explanation is that PPAR-α may directly activate transcription of the CYP3A4 gene. To our knowledge, no functional PPAR-α response element has yet been described in the CYP3A4 promoter. Promoter analysis using various software tools indicated numerous possible nonconsensus PPAR-α response elements, the functionalities of which are, however, unknown. When these findings are considered together, it is possible that both direct and indirect mechanisms may contribute to the modulation of CYP3A4 by PPAR-α. Although in vivo validation of the other identified genetic predictors of CYP3A4 failed, presumably because of the limited number of phenotyped subjects, our data indicate potentially interesting aspects about the role of these predictors in regulating CYP3A4. The aryl hydrocarbon receptor (AhR) nuclear translocator (ARNT, also termed Hif1β) was included for exploratory purposes. To our knowledge, no AhR binding sites are present within the investigated regions of the CYP3A4 promoter, and no evidence of interactions between AhR pathways and CYP3A4 has been reported, with the exception of an omeprazole-specific metabolism-mediated crosstalk.44 The observed association between ARNT SNP rs2134688 and CYP3A phenotype was biologically consistent for mRNA, protein, and activity. Interestingly, the same variant, rs2134688, has previously been associated with lower CYP1A2 expression.45 The basis for this observation remains to be elucidated. Glucocorticoids are potent inducers of CYP3A4 expression through both PXR-dependent and PXR-independent mechanisms, and GR sites in the CYP3A4 promoter have also been described.44,46,47 To our knowledge, the GR variant rs258747, located near the 3′UTR, has not previously been reported to be associated with any gene expression patterns. Finally, the progesterone receptor membrane component (PGRMC) 1 and 2 genes code for cytochrome b5-related hemoproteins that have been proposed to modulate P450-dependent mono-oxygenase activity.48 To our knowledge, ours is the first report of a potential impact of PGRMC2 on P450 activity. The Clinical pharmacology & Therapeutics

lack of association of the variant rs3733260 with mRNA and protein levels is consistent with the underlying biological hypothesis of a possible role in electron transfer. In conclusion, the nuclear receptor PPAR-α, which is known mainly for its roles in regulating and orchestrating lipid homeo­ stasis and inflammation, was here shown to be a novel genetic predictor and regulator of CYP3A4 expression. This finding points at a novel link between endogenous processes and drug metabolism. Although the exact mechanism of the proposed new regulatory function of PPAR-α remains to be elucidated, the PPARA genetic polymorphisms described are promising pharmacogenetic predictors of CYP3A4-dependent pharmacokinetics and drug response phenotypes with respect to many clinically used drug substrates of this enzyme. Moreover, the fact that PPAR-α was shown to be polymorphically expressed in human liver suggests a wide spectrum of potential pharmacogenetic applications, for example, in lipid pathophysiology, as cardiovascular risk factors, and in inflammatory diseases. Methods

Liver donors. The collection of liver tissues (79 female, 71 male) and

corresponding blood samples has been described in detail.45 The study protocol was approved by the ethics committees of the medical faculties of the Charité, Humboldt University, and the University of Tübingen. The study was conducted in accordance with the Declaration of Helsinki, and informed consent was obtained from each patient. Only nontumorous tissue was collected, as confirmed by histological examination, and stored at –80°C. Available patient documentation included age, sex, smoking habits, alcohol consumption, presurgery medication, diagnosis leading to liver resection, and serological liver function parameters. Atorvastatin pharmacokinetics in vivo. The pharmacokinetics of ator-

vastatin and its 2-hydroxymetabolite were obtained from previously published studies.29,30 The ratio of 2-OH-atorvastatin/atorvastatin the AUC0–∞ was chosen for analysis because this parameter was shown to be unaffected by possible confounders such as SLCO1B1 genotype and gender (data not shown). In brief, after an overnight fast, 56 healthy volunteers ingested a single 20-mg dose of atorvastatin (Lipitor; Pfizer/ Gödecke, Karlsruhe, Germany). Other drugs and grapefruit products were not allowed prior to atorvastatin administration. Concentration levels of atorvastatin and its metabolites in plasma were measured using a procedure described previously.29 The study was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa, Finland and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from the participants. Quantitative PCR. Quantification of PPAR-α and ACOX1 was per-

formed using newly developed TaqMan assays (Supplementary Table S1 online). Previously published assays or predeveloped assays (Applied Biosystems, Darmstadt, Germany) were used for all other genes, and raw data were normalized to RPLP0 as described earlier.26,45 For further details, see the Supplementary Information online.

Protein quantification. CYP3A4 protein was quantified in liver microsomes essentially as described,10 using lymphoblast-expressed CYP3A4/P450 reductase (BD Biosciences, Heidelberg, Germany) as a standard that was coanalyzed in each experiment for absolute quantification. PPAR-α protein was determined in 50 µg of total liver homogenates with polyclonal rabbit anti-human PPAR-α antibody (H98) (Santa Cruz Biotechnology, Heidelberg, Germany). For normalization, blots were stained with monoclonal β-actin antibody (Sigma-Aldrich, Munich, Germany). 7

articles Enzyme activity measurements. CYP3A4 and CYP2C9 enzyme activi-

ties were determined using mass spectrometry for quantification of atorvastatin 2-hydroxylation and tolbutamide 4-hydroxylation in hepatocyte culture supernatants, as previously described.26

SNP selection and genotyping methods. Candidate genes and SNPs

were selected from public databases (http://www.ncbi.nlm.nih.gov/ snp (Build132), http://www.ncbi.nlm.nih.gov/omim, http://www. pharmgkb.org, http://www.phosphosite.org, and http://variome.net) considering published data on functionality, frequency, and linkage. SNP selection was complemented by “tag”-SNPs, selected using the HaploView tagger tool (http://www.broadinstitute.org/haploview/haploview; r2 >0.8 and haplotype frequencies >10%) on the HapMap-CEU population. The final selection of 334 SNPs in 40 genes is presented in Supplementary Table S2 online. A detailed description of genotyping procedures is available in the Supplementary Information. Primary human hepatocytes and shRNA-mediated knockdown. The

use of human hepatocytes for research purposes was approved by the local ethics committees of the Ludwig-Maximilians-University of Munich and the Charité, Humboldt University Berlin, and written informed consent was obtained from all patients. Human hepatocytes were cultured in 12-well plates with 0.4 × 106 cells/well in William’s E medium (Invitrogen Life Technologies, Darmstadt, Germany) with daily change of medium. For detailed information on hepatocyte isolation, production of lentiviral vectors and viruses, and infection procedures, see the Supplementary Information. In brief, two shRNAs (shPROX and shFAR) targeting PPAR-α were designed, using PPAR-α reference sequence NM_001001928 and the BLOCK-iT RNAi designer from Invitrogen. For control, a validated nontargeting siRNA sequence from Dharmacon (Lafayette, CO) was used to design shRNA shDF9 (Supplementary Table S3 online). Statistical methods. The software package R-2.13.0 (http://www.rproject.org) with additional packages hwde-0.62 and SNPassoc-1.6-0 was used for statistical analyses. Hardy-Weinberg equilibrium P values were calculated in accordance with a previously described procedure.49 SNPs with Hardy-Weinberg equilibrium P value 0.8 and haplotype frequencies >10%) on HapMap-CEU population. Selected genes and polymorphisms are listed in Supplementary Table 2. Genomic DNA isolated from EDTA blood samples corresponding to the 150 liver samples was genotyped as described (8). As quality control approximately 10% of the samples within each assay were subjected to repeated genotyping by the same assay or to sequencing. Only assays with less than 0.1% discordant results were accepted. A total of 334 SNPs were genotyped, of which 154 were previously described (8). Of the remainder, 82 SNPs were genotyped using new assays for MALDI-TOF MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry); 80 SNPs were selected from HumHap300v1.1 chip data (9); 5 SNPs were genotyped using predesigned TaqMan genotyping assays (Applied Biosystems, Darmstadt, Germany); genotypes for two SNPs in MDR1 were kindly provided by E. Schaeffeler (IKP, Stuttgart, Germany); eight SNPs in the CYP3A4 region (10) and three SNPs in POR (1) were reanalyzed in this study. SNP locations and genotyping methods are summarized in Supplementary Table 2. The samples from the volunteer study were genotyped with predesigned TaqMan genotyping assays specifically designed for the assigned SNPs purchased from Applied Biosystems (C_26672991_10, C_3234206_10, C_30936827_10, C_310582401_10).

3 Cell culture methods Human hepatocytes were isolated using a modified two-step EGTA/collagenase perfusion procedure as described previously (11). Cells were cultured in 12-well plates with 0.4x106 cells/well in William’s E medium (Invitrogen Life Technologies, Darmstadt, Germany) with daily change of medium. Production of lentiviral particles was based on an enhanced green fluorescent protein gene (EGFP)-modified pLenti6 (a friendly gift of Dr. M. Kriebel, NMI Reutlingen, Germany) and the BLOCK-iT Lentiviral RNAi Gateway Kit (Invitrogen life technologies, Darmstadt, Germany). Detailed procedures will be described elsewhere. In brief, two different shRNAs (shPROX, shFAR; sequences are shown in Supplementary Table 3) targeting PPARα were designed using PPARα reference sequence NM_001001928 and the BLOCK-iT RNAi designer from Invitrogen. As negative (non-targeting) control a validated non-targeting siRNA sequence was used to design shRNA shDF9 sequences (see Supplementary Table 2). Viral particles were produced according to the manufacturer’s recommendations. Human primary hepatocytes were cultured in 12-well plates with 0.4x106 cells/well in William’s E medium (Invitrogen) with daily change of medium. The infection was performed on day three after isolation applying a multiplicity of infection of 5 using 6 µg/ml Polybrene (Sigma) to enhance infection. Total RNA was isolated five days after infection using RNeasy MiniKit (Qiagen, Hilden, Germany). Statistical Methods Statistical analyses were performed using software R-2.13.0 with additional packages hwde 0.62 and SNPassoc-1.6-0. Hardy–Weinberg P-values were calculated according to (12). Minor allele frequency (MAF) and missing values frequency of polymorphisms are given in Supplementary Table 2. SNPs with MAF < 5% (n=71) or Hardy–Weinberg equilibrium Pvalue < 0.0001 (n=2) were excluded from further analyses. From seven pairs and one triplet of 100%-linked polymorphisms, the SNP with the fewest missing values, if present, was chosen. Finally, a total of 252 polymorphisms were included in the statistical analyses. Univariate associations between polymorphisms and CYP3A4 expression (mRNA, protein, activities) were investigated using the generalized linear model capabilities of the SNPassoc package. In order to satisfy the Gaussian distribution condition, phenotypic data were logtransformed and data distributions were checked using quantile–quantile plots. Polymorphisms with less than three heterozygous or homozygous carriers (n=35) were only analyzed in the dominant genetic model. For all other SNPs three different genetic models were considered: dominant, recessive, and log-additive. Package SNPassoc was also applied to study associations between polymorphisms and CYP3A4 expression corrected for non-

4 genetic factors. For each phenotype studied, the non-genetic correction factors were chosen based on linear models between phenotypic data and all ten non-genetic factors considered (age, sex, nicotine and alcohol intake, total bilirubin, GGT, CRP, cholestasis, diagnosis, and to known inducers of P450) as well as models derived from these complete models via stepwise model selection using Akaike’s information criterion. Non-genetic factors were chosen for correction if the P-value from complete and/or reduced models was var)

-11129 99382096 rs2740574 *1B A>G 99381463 rs6957392 C>T 99380021 rs28988574 T>C 99375702 rs56324128 *7 G>A 99366316 rs35599367 *22 C>T 99366081 rs4987161 *17 T>C 99365983 rs55785340 *2 T>C 99365719 rs2246709 A>G 99365083 rs4646437 C>T 99356188 rs28988602 A>C 99354808 rs28371763 A>T 99354114 rs12333983 T>A 93844240 rs7792939 T>C CYP3A5 chr7; NM_000777 93911609 rs4646446 G>A 93906909 rs776746 *3 G>A h 93902688 rs4646450 C>T 93881379 rs4646458 h A>C 93876543 rs10242455 A>G AhR (aryl hydrocarbon receptor) chr7; NM_001621 17211644 rs10247158 A>T 17219467 rs10250822 T>C 17221377 rs4719497 T>C 17232101 rs2282885 A>G 17244361 rs1476080 T>G 17250058 rs4236290 T>C 17254575 rs6960165 A>G 17254910 rs2158041 C>T 17257853 rs7811989 G>A

MAFe

HWE Pf

missing values (%)

0.025 0.016 0.023 no data 0.069 0.016 no data 0.349 0.133 0.023 0.014 0.117 0.125

0.017 0.02 0.007 0.034 0.007 0.053 0 0 0.241 0.092 0.02 0.024 0.092 0.145

1 0.051 0.02 0.03 0.001 1

0 2 0.7 1.3 1.3 0

0.823 0.322 0.001 0.071 0.117 0.502

2 5.3 2 2.7 2.7 3.3

Maldi Maldi (2) (2) AD AD AD AD (2) (2) (2) (2) (2) (2)

i iss i 3'u 3'

0.017 0.058 0.175 0.017 0.059

0.017 0.037 0.135 0.017 0.045

0.069 1 1 0.004 0.002

0.7 0 1.3 1.3 2.7

ILM AD ILM ILM ILM

5' 5' 5' i i i i i i

0.152 0.2 0.1 0.347 0.325 0.125 0.196 0.183 0.233

0.13 0.217 0.108 0.413 0.343 0.122 0.205 0.203 0.233

1 0.81 1 0.736 0.21 0.697 1 1 1

0 0 1.3 0 0 2 0.7 1.3 0

(6) (6) (6) (6) (6) (6) (6) (6) (6)

effect

typec

TGT insert

p p i i nc i nc nc i i i 3'u 3' 3'

G56D F189S S222P

MAF publishedd

methodg

reference

(1)

(3)

(4) (5) (5)

(7) (7) (7) (5) (8) (7)

Genea

chromosomal SNP rs position (dbSNP (HuRef build133) build 133) 17265602 rs2066853

allele/ nt positionb (ref>var) G>A

effect

typec

R554K

nc

17265649 rs4986826 G>A V570I nc AhRR (aryl hydrocarbon receptor regulator); chr5; NM_020731 296288 rs3756712 T>G i 301455 rs4957018 A>G i 334259 rs3734145 C>T i 349509 rs4957028 T>C i 359747 rs908114 A>G i 367179 rs2721020 G>A i 373998 rs7731963 C>A i 410970 rs2292596 C>G P189A nc 422738 rs34453673 G>C D641H nc 426193 rs10078 A>C 3'u 426655 rs2241598 C>T 3' ALAS1 (δ aminolevulinate synthase 1) chr3; NM_000688 52295492 rs35338461 G>A R13Q nc 52300167 rs352168 G>A I173 sc 52304298 rs34373571 G>A390del fs 52305099 rs352165 T>C i 52307532 rs17052017 G>A S456N nc 52309307 rs352163 C>T i ARNT (Hif1β ; Aryl hydrocarbon receptor nuclear translocator) chr1; NM_001668 122261935 rs2292166 C>T 5' 122237649 rs7412746 C>T 5' 122219316 rs11204735 C>T i 122194565 rs2134688 A>G i 122186568 rs2228099 i G>C V189 sc i 122177436 rs1889740 G>A i 122173007 rs10305724 C>T i CAR (NR1I3; constitutive androstane receptor) chr1; NM_00177469 132569410 rs11584174 C>T 5' 132566799 rs2502805 C>T 5'

MAF publishedd

MAFe

HWE Pf

missing values (%)

methodg

0.075

0.1

1

0

(6)

0

0

0.7

(6)

0.342 0.275 0.25 0.424 0.383 0.225 0.375 0.412 0.31 0.2 0.183

0.361 0.322 0.258 0.463 0.329 0.232 0.411 0.373 0.403 0.218 0.211

0.723 0.453 0.052 1 0.853 1 0.389 0.296 0.612 0.81 1

1.3 0.7 0.7 1.3 2.7 0.7 6.7 0 0 0.7 0.7

(6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6)

no data 0.458 no data 0.467 0 0.475

0 0.456 0 0.453 0 0.446

0.325

0.7

0.252

0.7

0.319

0.7

Maldi Maldi Maldi ILM Maldi ILM

0.217 0.475

0.015 0.737

0.142 0.35 no data 0.125

0.282 0.419 0 0.13 0.307 0.307 0.057

0.716 0.255 0.255 0.383

0.7 1.3 0.7 0 0 0 0.7

(6) (6) (6) (6) (6) (6) (6)

0.136 0.317

0.149 0.215

1 0.143

1.3 0.7

ILM ILM

reference (9), (10), (11), (12) (11)

(13) (14)

(15) (15) (16) (15)

Genea

chromosomal position (HuRef build 133) 132562842 132561157 132560155

SNP rs (dbSNP build133) rs6686001 rs3003596 rs2502815

allele/ nt positionb (ref>var)

effect

C>A T>C C>T 398 T>G V133G 132559533 rs2307424 C>T P180 132557514 rs2307418 A>C 132557415 rs4073054 G>T C/EBPα (CCAAT/enhancer binding protei alpha) chr19; NM_004364 30295964 rs16967952 G>A 7bp del 30293290 rs1049967 C>A A252S j 30292365 rs707656 C>G j 30291963 rs12691 C>T COUP-TFII (NR2F2) chr15; NM_001145155 73001452 rs3743462 T>C 73006024 rs60286962 G>T S68I 73008781 rs1807198 T>C 73008836 rs2001191 A>G CyB5α (cytochrome b5 type A) chr18; NM_005123 68647992 rs1788571 T>C 68661274 rs1790821 A>G 68677702 rs1790861 A>G 68675168 rs3794946 C>T 68669650 rs4891538 G>A 68678265 rs4891540 G>A 68663092 rs1803366 G>A R52K FXR (farnesoid X receptorNR1H4) chr12; NM_005123 97913484 rs11110385 C>T 97945890 rs4764980 A>G k 97947884 rs56163822 *1B G>T A>G M1V k 97948084 rs7304328 T>C

HWE Pf

missing values (%)

0.143 0.363 0.19 0 0.311 0.153 0.476

0.739 0.289 0.063

0 0 0

0.252 0.529 0.247

1.3 4 2

0.161 no data no data 0.164 0.158

0.13 0 0 0.127 0.125

1

0

1 1

0 1.3

i nc i i

0.125 no data 0.258 0.25

0.141 0 0.243 0.277

0.739

0

0.826 1

1.3 0

3' i i i i i nc

0.25 0.225 0.212 0.208 0.174 0.117 no data

0.269 0.304 0.29 0.174 0.142 0.132 0

0.836 0.051 0.016 0.158 0.175 0.282

2 1.3 0 0.7 1.3 1.3

ILM ILM Maldi ILM Maldi ILM Maldi

5' i 5'u nc i

0.317 0.492 no data no data 0.025

0.376 0.455 0.023 0 0.023

0.054 0.74 1

0.7 2.7 0

1

0

ILM ILM Maldi Maldi Maldi

MAF publishedd

MAFe

i i i nc sc i i

0.13 0.49 0.242 no data 0.333 0.183 0.408

5' fs nc 3'u 3'u

typec

methodg (6) (6) (6) (6) (6) (6) (6) Maldi Maldi Maldi Maldi ILM ILM Maldi Maldi Maldi

reference

(17) (18) (17) (17) (19)

Genea

chromosomal position (HuRef build 133) 97987096 98006027

SNP rs (dbSNP build133) rs61755050 rs1030454

allele/ nt positionb (ref>var)

effect

typec

T>C M173T nc A>G i *2 C>T H219Y nc 98009293 rs35739 A>G i GR (NR3C1; glucocorticoid receptor) chr5; NM_000176 137930690 rs10482605 T>C 5' m 137927508 rs6189 G>A E22 sc 137927506 rs6190 m G>A R23L nc 137926480 rs1800445 A>G N365S nc 137905665 rs2963156 C>T i l 137889707 rs2963154 T>C i l 137847669 rs33389 C>T i present in dbSNP build127, rs6195 A>G N363S nc does not map to NR3C1 in build133 137839703 rs10482672 C>T i 137809517 rs258751 C>T D678 sc 137804050 rs258747 G>A 3' HMOX1 (heme oxygenase 1) chr22; NM_002133 18737187 rs2071746 A>T 5' 18743367 rs9282702 C>T P106L nc 18743930 rs2071749 G>A i 18747382 rs5755720 A>G i HMOX2 (heme oxygenase 2) chr16; NM_001127204?? (rs1788085) merged to G>A 5' 59586832 rs519905 HNF1α (TCF1; hepatocyte nuclear factor 1 homeobox A) chr12; NM_000545 -58 A>C p 118426037 rs1169288 A>G I27L nc

HWE Pf

missing values (%)

0.017 0.094 0 0.452

1 0.618

0 0.7

0.739

2.7

Maldi ILM Maldi ILM

no data 0.042 0.049 no data 0.212 0.175 0.183

0.167 0.013 0.013 0.037 0.242 0.106 0.103

0.373 1 1 1 0.272 0.663 0.656

0 0 0 0 0.7 2.7 0

Maldi Maldi Maldi Maldi ILM ILM Maldi

(20), (21) (22) (23)

0.017

0

Maldi

(25)

0.142 0 0.45

0.174 0 0.5

0.174

0.7

0.622

1.3

ILM Maldi ILM

(23) (20) (20)

0.458 0 0.492 0.3

0.423 0 0.487 0.333

0.868

0

0.87 0.365

0.7 0

no data

0

no data 0.283

0 0.393

MAF publishedd

MAFe

no data 0.167 no data 0.432

methodg

reference

(24)

Maldi Maldi ILM Maldi Maldi

0.864

0.7 0

(6) (6)

(26) (27), (28),

Genea

chromosomal SNP rs position (dbSNP (HuRef build133) build 133)

allele/ nt positionb (ref>var)

118426251 rs1800574 C>T 118440586 rs1169300 G>A 118440633 rs2071190 T>A 118441689 rs1169302 T>G 118444041 rs2464196 G>A 118446925 rs1169306 C>T 118446996 rs1169307 T>C 118447458 rs735396 A>G HNF3γ (FOXA3;forkhead box A3) chr19; NM_004497

effect

A98V

S487N

G91R

42798895 rs8103278 G>A 42803086 rs11669442 T>C HNF4 α (NR2A1; Hepatocyte nuclear factor 4 alpha) chr20; NM_000448 39711594 rs4810424 G>C

typec

MAF publishedd

MAFe

nc i i i nc i i i

0.029 0.292 0.22 0.45 0.292 0.375 0.392 0.383

0.023 0.315 0.19 0.433 0.317 0.377 0.426 0.382

nc i i

no data 0.322 0.325

0 0.359 0.333

p

0.164

HWE Pf 1 0.851 1 0.318 1 1 0.319 0.727

missing values (%)

methodg

0 0.7 0 0.7 0 0 0.7 1.3

(6) (6) (6) (6) (6) (6) (6) (6)

0.481 0.853

0.7 2

Maldi ILM ILM

0.157

0.364

0

(6)

39722266 rs2144908

G>A

p

0.183

0.16

0.539

0

(6)

39763154 rs2868094

T>C

p

0.317

0.28

0.316

0

(6)

39718895 rs6031544

C>T

p

0.283

0.289

0.842

0.7

(6)

39776455 rs3212183 39782233 rs6073432 39783448 rs13041396

T>C A>C C>G C>T C>T C>T G>A G>T

i i i nc nc nc i i

0.413 0.309 0.24 no data no data no data 0.484 0.39

0.45 0.307 0.207 0 0.02 0 0.423 0.367

0.0029 0.057 0.133

0 0 0

0.05

0

0.404 0.86

0 0

(6) (6) (6) (6) (6) (6) (6) (6)

p sc

0.367 0.223

0.293 0.22

0.238 0.811

0 0

(6) (6)

i

0.392

0.399

0.733

0.7

(6)

39783682 rs1800961

379 460

39788612 rs6103731 39798799 rs3818247 IL1β (interleukin 1 beta) chr2; NM_000576 106049014 rs1143627 C>T 106045017 rs1143634 C>T IL1RN (interleukin 1 receptor antagonist) chr2; NM_000577 106331260 rs2637988 G>A

R127W T139I R154X

F105

reference (29) (30) (31) (32) (31) (31) (33)(31) (34)

(35)(36) (37) (38) (39) (40) SNP@pro moter (38) (41) (36) (42) (41) (43) (44)

chromosomal SNP rs position allele/ nt effect Genea (dbSNP (HuRef positionb (ref>var) build133) build 133) 106333772 rs3213448 G>A 106341683 rs419598 T>C A57 106344608 rs315952 T>C S133 106346445 rs397211 T>C IL6 (interleukin 6) chr7; NM_000600 22649301 rs1800797 G>A 22649326 rs1800796 G>C 22649725 rs1800795 G>C 22650513 rs2069832 A>G 22651107 rs2069837 A>G 22651787 rs1554606 T>G 22652244 rs11544633 T>C L119P 22654118 rs2069860 A>T D162V 22654119 rs13306435 T>A D162E 22654236 rs2069849 C>T F201 IL6R (interleukin 6 receptor) chr1; NM_000565 125727419 rs11582424 A>C 125727574 rs11265608 G>A 125741193 rs4845617 G>A 125745344 rs1386821 A>C 125765127 rs11557725 T>G L81R 125767635 rs7549250 T>C 125774254 rs4553185 T>C 125777387 rs4453032 A>G 125779077 rs4845623 G>A 125782181 rs4537545 C>T 125789993 rs4129267 C>T 125790779 rs28730736 G>A V385I 125793744 rs11265618 C>T 125796075 rs4329505 T>C 125799849 rs4240872 T>C IL6ST (gp130; interleukin 6 signal transducer) chr5; NM_002184 52271025 rs715180 A>C

MAF publishedd

MAFe

HWE Pf

missing values (%)

methodg

i sc sc 3'

0.117 0.275 0.258 0.314

0.128 0.287 0.295 0.342

0.469 0.84 0.555 0.856

0.7 4.7 0.7 0.7

(6) (6) (6) (6)

5' 5' 5' i i i nc nc nc sc

0.481 0.043 0.466 0.475 0.08 0.458 0 0.017 0 0.017

0.43 0.057 0.443 0.44 0.074 0.466 0 0.013 0 0.027

0.247 0.067 0.741 0.619 0.567 0.513

0 0 0 0 0.7 1.3

1

0

1

0

Maldi Maldi Maldi Maldi ILM ILM Maldi Maldi Maldi Maldi

5' 5' 5'u i nc i i i i i i nc i i i

0.275 0.119 0.397 0.226 no data 0.492 0.492 0.353 0.35 0.339 0.342 0 0.158 0.158 0.317

0.267 0.114 0.383 0.189 0 0.437 0.44 0.35 0.362 0.352 0.339 0 0.232 0.217 0.268

0.835 1 0.17 0.601

1.3 0.7 0 1.3

1 0.869 0.721 0.723 0.473 0.583

0 0.7 0 0.7 0.7 0.7

0.359 0.152 0.403

0.7 0 0.7

ILM ILM Maldi ILM Maldi Maldi ILM Maldi ILM ILM ILM Maldi ILM Maldi ILM

5'

0.058

0.067

0.126

0.7

ILM

typec

reference

(45) (45) (46) (45) (45) (45) (45) (47) (45)

(48) (49) (50)

(51)

Genea

chromosomal position (HuRef build 133) 52268054 52265762 52262872 52245809 52245345 52241535

SNP rs (dbSNP build133)

allele/ nt positionb (ref>var)

rs6870870 C>A rs2112979 T>C rs6863337 T>G rs1063560 C>G rs6861772 A>G n rs11741953 T>C (rs3729960) 52237874 merged to G>C n rs2228044 52236393 rs10940495 A>G n 52225663 rs2228043 C>G 52224458 rs2228046 T>C 52221526 rs3730294 A>T 52217768 rs11574780 A>G LRH-1 (NR5A2; chr1; NM_003822 171204396 rs10919806 C>T 171208706 rs3828112 A>G 171233207 rs2816999 G>A 171235113 rs2253771 C>T 171244456 rs2821324 G>A 171256575 rs3762398 C>T 171284843 rs871446 G>A 171305754 rs2246923 C>T 171306196 rs1524163 C>T 171313091 rs2246072 C>T LXRα (NR1H3; liver X receptor alspha) chr11; NM_005693 46979738 rs11039155 G>A 46979629 rs12221497 G>A 46981000 rs2279238 C>T 46977893 rs3758673 o C>T o 46985267 rs7120118 T>C LXRβ (NR1H2; liver X receptor beta) chr19; NM_007121 47220756 rs1405655 T>C

MAF publishedd

MAFe

HWE Pf

missing values (%)

methodg

5' 5' i nc i i

0.408 0.267 0.133 0.038 0.133 0.117

0.372 0.272 0.099 0.007 0.25 0.095

1 1 0.004 A 5' 0.333 0.36 0.216 47223022 rs4802703 C>A i 0.292 0.327 0.268 MDR1 (ABCB1) chr7; NM_000927 81749647 rs1045642 3435C>T i 0.466 (T>C) 0.493 0.871 81771632 rs2032582 2677G>T,A i 0.398 (G>T) 0.45 0.621 NFkB1 (Nuclear factor NF-kappa-B p105 subunit) chr4; NM_003998 99158542 rs28362491 del>ATTG 5' 0.34 0.361 0.019 99170635 rs3774937 T>C p 0.367 0.326 0.853 99190361 rs230530 T>C i 0.364 0.446 0.403 99191728 rs4647992 C>T i 0.067 0.047 0.344 99195206 rs230526 C>T i 0.467 0.396 0.23 99232825 rs4648022 C>T i 0.067 0.077 1 99242832 rs1598859 T>C i 0.343 0.379 1 99254916 rs4648072 A>G M507V nc 0.075 0.013 0.049 99273667 rs1609798 C>T i 0.331 0.329 1 99278129 rs997476 C>A 3' 0.1 0.057 0.068 99278325 rs10489113 A>G 3' 0.225 0.182 1 NFkB2 (nuclear factor NF-kappa-B p100 subunit) chr10; NM_001077493 97790920 rs7897947 T>G i 0.155 0.223 0.639 97792905 rs11574849 G>A i 0.015 0.081 1 97795944 rs1056890 C>T 3' 0.358 0.352 0.858 PGC1α (PPARGC1α; Peroxisome proliferator-activated receptor gamma coactivator 1-alpha) chr4; NM_013261 23220542 rs4361373 C>T i 0.192 0.141 1 23220426 rs4550905 A>G i 0.325 0.27 0.676 23181348 rs6448226 A>G i 0.375 0.34 0.465 23168218 rs2970853 G>A i 0.308 0.211 0.218 23167027 rs2932976 G>A i 0.271 0.314 0.334 23160624 rs2970847 C>T T394 sc 0.208 0.153 1 23160362 rs8192678 G>A G482S nc 0.367 0.322 0.708 23150203 rs2932965 G>A i 0.25 0.171 0.771 23145903 rs12650562 G>A i 0.435 0.426 0.74

missing values (%)

methodg

0 1.3 0 0

Maldi ILM Maldi Maldi

0 0

E E

2 0.7 2 0.7 0.7 0.7 3.3 0.7 0.7 1.3 1.3

(6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6)

1.3 0.7 0.7

ILM Maldi ILM

0.7 1.3 2 2 3.3 0 2.7 0.7 0.7

(6) (6) (6) (6) (6) (6) (6) (6) (6)

reference (56) (57)

(58) (59)

(60) (61) (60)

(62) (63) (62) (64) (65)

chromosomal SNP rs position MAF allele/ nt effect typec Genea (dbSNP (HuRef publishedd positionb (ref>var) build133) build 133) PGRMC1 (HPR6; progesterone receptor membrane component 1) chr23; NM_006667 107867148 rs2499043 G>T i 0.489 PGRMC2 (progesterone receptor membrane component 2) chr4; NM_006320 124939540 rs3733260 G>T i 0.212 124926614 rs4975220 T>C i 0.289 POR (P450 oxidoreductase) chr7; NM_000941 70695799 rs10239977 C>T i 0.429 70701108 rs2286823 G>A i 0.295 70701818 rs1057868

*28

C>T

A503V

nc

PPARα (peroxisome proliferator-activated receptor alpha) chr22; NM_001001928 29496915 rs135551 C>T i 29528919 rs7364220 A>G i 29542289 rs4823613 A>G i 29554099 rs4253728 G>A i 29558202 rs1800204 G>A R127Q nc *2 R131E/Q nc 29558306 rs1800206 C>G L162V nc 29573515 rs4253776 A>G i PXR (NR1I2; pregnane X receptor) chr3; NM_003889 116875126 rs1523130 G>A 5' 116875654 rs3814055 p C>T 5' 116876658 rs1523127 A>C 5' p 116876926 rs2276706 G>A i 116880961 rs4234666 C>G i 116882512 rs1403527 T>G i 116885012 rs1403526 A>G i 116887006 rs55764158 C>A i 116890700 rs16830505 A>G i 116894057 rs2472677 C>T i 116894538 rs4440154 C>T i 116895765 rs2461823 T>C i 116896695 rs13059232 T>C i

MAFe

HWE Pf

missing values (%)

methodg

0.463

var) 69245

effect

C>T 116901239 rs7643645 A>G 116901918 rs12721613 *2 C>T P27S 116901945 rs12721607 *3 G>A G36R 116906214 rs72551372 *10 G>A 116906284 rs72551374 A>G D163G 116906600 rs3732357 G>A 116909475 rs6785049 A>G 116909895 rs2276707 C>T 116912171 rs3732359 G>A 116912639 rs1054191 G>A 116912996 rs3814057 T>C RARα (NR1B1; Retinoic acid receptor alpha) chr17; NM_001033603 rs13706 q 34250318 (located in G>A V441I CDC6) 34289439 rs2715553 T>C q 34294047 rs9303286 G>C 34297360 rs482284 G>A 34301227 rs4890109 G>T RXRα (NR2B1; Retinoid X receptor alpha) chr9; NM_002957 106690458 rs3818740 T>C 106765881 rs3132297 C>T 106768929 rs3118529 T>C 106775414 rs4240705 A>G 106784780 rs3118570 A>C 106785081 rs1536475 G>A 106789746 rs3132293 G>A 106792366 rs1805348 G>A A457 SHP (NR0B2; small heterodimer partner) chr1; NM_021969 169 C>T R57W 278 G>A G93D 25493499 rs6659176 G>A G171A

missing values (%)

methodg

reference

0.009 0.018 0.641 0.091 0.079 0.81

0 0 0.7 0 0.7 0.7 0.7 0 0 6 0 0.7

(6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6)

(72) (72) (75) (75) (75) (75) (76)

0.094

0.618

0.7

(6)

no data 0.06 0.283 0.058

0.483 0.093 0.267 0.027

0.87 0.617 1 0.0016

1.3 0 0 2

(6) (6) (6) (6)

0.225 0.222 0.227 0.4 0.18 0.17 0.22 0.025

0.294 0.157 0.29 0.369 0.153 0.163 0.209 0.023

0.423 1 0.557 0.597 1 1 0.451 1

4.7 0 0 0.7 0 0 2.7 0

(6) (6) (6) (6) (6) (6) (6) (6)

0.092

0 0 0.073

0

Maldi Maldi Maldi

MAF publishedd

MAFe

i i nc nc nc i i i i 3'u 3'u 3'u

no data 0.43 0 0.03 no data no data 0.225 0.302 0.058 0.075 0.233 0.188

0.077 0.363 0 0.027 0 0 0.336 0.413 0.22 0.277 0.14 0.218

1 0.598

5'

0.142

i i i i i i i i i i i sc

typec

nc nc nc

HWE Pf

1

1

(73) (73) (77) (73) (73)

(78) (79) (79) (79)

(80)

chromosomal SNP rs position MAF allele/ nt effect typec MAFe Genea (dbSNP (HuRef publishedd positionb (ref>var) build133) build 133) 25491729 rs7504 G>A 3'u 0.259 0.206 SLCO1B1 (OATP-2; solute carrier organic anion transporter family, member 1B1) chr12; NM_006446 21055663 rs852550 A>G 5' 0.067 0.064 21099743 rs2291073 T>G i 0.114 0.085 21102170 rs4149038 A>G i 0.225 0.221 21102493 rs17329885 T>C i 0.153 0.18 21103666 rs2306283 T>C N130D nc 0.392 0.427 21103689 rs11045818 G>A S137 sc 0.144 0.171 21103741 rs11045819 *4 C>A P155T nc 0.15 0.173 21103745 rs72559745 A>G E156G nc no data 0 21105471 rs4149056 21105521 rs4149057 21105547 rs2291075 21105558 rs4603354 21112124 rs2100996 21153572 rs7966613 21160007 rs10841767 SLCO1B3 (OATP-8) chr12; NM_019844 20757882 rs10841661 20784548 rs4149117 20787244 rs7306033 20788825 rs7311358 20809561 rs12299012 20817813 rs6487172 SLCO2B1 (OATP-B) chr11; NM_007256 71149676 rs2712800 71152874 rs4944989 71167471 rs4944994 71168951 rs2851075 71198665 rs7110250 71203356 rs3824904 71227376 rs1676889

HWE Pf

missing values (%)

methodg

0.623

1.3

Maldi

0.458 1 0.47 0.047 0.864 0.077 0.049

0.7 2 2 2 4.7 2.7 0

ILM Maldi Maldi ILM ILM ILM Maldi Maldi

T>C

V174A

nc

0.158

0.17

0.765

6

ILM

C>T C>T G>A T>C A>G A>G

L191 F199 G203E

sc sc nc i i i

0.373 0.392 no data 0.492 0.381 0.188

0.403 0.432 0 0.463 0.385 0.203

0.865 0.407

0.7 1.3

0.741 0.73 0.799

1.3 1.3 1.3

Maldi Maldi ILM ILM Maldi Maldi

i nc i nc nc i

0.433 0.133 0.181 0.119 0 0.35

0.413 0.141 0.162 0.143 0 0.331

0.613 1 1 0.507

0.7 0.7 1.3 0

0.352

1.3

ILM ILM ILM ILM Maldi Maldi

5' 5' i i i i 3'

0.483 0.058 0.283 0.467 0.05 0.058 0.35

0.44 0.027 0.23 0.473 0.02 0.023 0.301

0.031 0.016 0.819 0.049 0.007 0.116 0.845

0.7 1.3 1.3 0 1.3 0.7 1.3

ILM ILM Maldi ILM ILM ILM Maldi

C>T G>T A>G G>A T>C C>T T>G G>A T>C G>A A>G C>T G>A

S112A M233I V560A

reference

(81) (82) (83) (83) (84) (81); (85) (86) (87)

chromosomal SNP rs position allele/ nt effect Genea (dbSNP (HuRef positionb (ref>var) build133) build 133) SRC-1 (NCOA1; nuclear receptor coactivator 1) chr2; NM_003734 24548803 rs995647 A>G 24585541 rs11677500 G>A 24625464 rs2119115 A>C 24661057 rs12468225 A>C 24661133 rs4578807 A>G 24682670 rs4665716 A>G 24685750 rs11682130 T>G 24707564 rs6743362 A>C 24716635 rs12617941 G>A 24731519 rs6724282 A>G 24732417 rs9309308 T>C USF1 (upstream stimulatory factor 1) chr1; NM_009053 132376972 rs3813609 C>G R873 132375488 rs2774279 G>A (ARHGAP 30) 132372581 rs1556259 T>C 132369651 rs2774276 C>G

MAF publishedd

MAFe

HWE Pf

missing values (%)

methodg

i i i i i i i i i 3' 3'

0.1 0.433 0.267 0.136 0.133 0.075 0.433 0.058 0.075 0.075 0.225

0.111 0.469 0.299 0.137 0.145 0.055 0.473 0.034 0.047 0.062 0.297

0.082 1 0.439 0.481 0.512 0.007 0.514 0.205 0.274 0.014 0.242

0.7 3.3 2 0 1.3 2.7 1.3 0.7 0.7 2.7 1.3

(6) (6) (6) (6) (6) (6) (6) (6) (6) (6) (6)

(88)

5'

0.3

0.373

0.601

0

(6)

(89)

5'

0.38

0.26

0.286

0

(6)

(90)

i i

0.083 0.21

0.127 0.247

0.47 0.269

0 0

(6) (6)

(89)

typec

132368697 rs2073658

r

G>A

i

0.283

0.363

0.48

0

(6)

132367458 rs3737787

r

C>T

3'u

0.309

0.363

0.48

0

(6)

5' i i i

0.458 0.35 0.457 0.452

0.493 0.382 0.433 0.419

1 0.605 0.183 0.502

1.3 1.3 0.7 1.3

(6) (6) (6) (6)

nc

0.442

0.34

0.71

4

(6)

i i

0.203 0.398

0.229 0.413

0.638 0.731

2.7 4

(6) (6)

VDR (NR1I1; vitamin D receptor) chr12; NM_000376 45331689 rs4516035 T>C 45321233 rs3890733 C>T 45312571 rs4760648 C>T 45310393 rs2853564 T>C rs10735810 45304801 (has merged to "FokI" T>A rs2228570) 45301314 rs2239186 T>C 45295717 rs2189480 C>A

M1T

reference

OMIM, (91) (89) (92) (91) (93)

(94)

chromosomal SNP rs position MAF allele/ nt effect typec Genea (dbSNP (HuRef publishedd positionb (ref>var) build133) build 133) 45289658 rs2239179 A>G i 0.408 45289218 rs1540339 G>A i 0.398 45280527 rs11574090 C>G L230V nc no data 45273885 rs7962898 T>C i 0.425 45270883 rs1544410 "BsmI" G>A i 0.442 45269885 rs7975232 "ApaI" A>C i 0.422 45269805 rs731236 "TaqI" T>C I352 sc 0.44 a Gene name with aliases in brackets, chromosome number and mRNA accession number

MAFe 0.405 0.391 0 0.497 0.4 0.493 0.372

HWE Pf 0.497 0.165 0.87 0.609 1 1

missing values (%)

methodg

1.3 2 0.7 0 0 0 8.7

(6) (6) (6) (6) (6) (6) (6)

reference

(95) (95) (95)

b

gives allele nomenclature (where allocated), previous names, or mRNA position

c

SNP type: i, intronic; nc, non-synonymous coding; sc, synonymous coding; fs, frameshift; 5’ upstream of mRNA start; 5’u, 5’ untranslated region; 3’, downstream of mRNA stop; 3’u, 3’ untranslated region; p, promoter;

d

MAF, minor allele frequency given in a HapMapCEU or alternative population of Caucasian origin ( http://www.ncbi.nlm.nih.gov/projects/SNP/ ; Build 133).

e

MAF, minor allele frequency found in this study

f

HWE p, Hardy-Weinberg equilibrium p-value

g

Method gives the genotyping method for the assigned SNP or the reference for the study from which the data were taken; Maldi, MALDI-TOF MS (mass assisted light desorption time of flight mass spectrometry) multiplexed genotyping assay; ILM, Illumina 300k Bead Chip; AD, TaqMan allelic discrimination genotyping assay (Applied Biosystems); E, genotype data provided by E.Schaeffeler, IKP, Stuttgart

h,i,j,k,l,m,n,o,p,q,r

SNPs 100% linked within the one gene

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2.

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10.

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Koyano S, Kurose K, Ozawa S, Saeki M, Nakajima Y, Hasegawa R, et al. Eleven novel single nucleotide polymorphisms in the NR1I2 (PXR) gene, four of which induce nonsynonymous amino acid alterations. Drug Metab Pharmacokinet. 17, 561-5 (2002).

17.

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19.

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Supplementary Table 3: shRNA sequences used for construction of lentiviral expression vectors primer name

sequence from 5’ to 3’a

shPROX_top

CACCGCCCGTTATCTGAAGAGTTCCCGAAGGAACTCTTCAGATAACGGGC

shPROX_bottom

AAAAGCCCGTTATCTGAAGAGTTCCTTCGGGAACTCTTCAGATAACGGGC

shFAR_top

CACCGCGTATGGAAATGGGTTTATACGAATATAAACCCATTTCCATACGC

shFAR_bottom

AAAAGCGTATGGAAATGGGTTTATATTCGTATAAACCCATTTCCATACGC

shDF9_top

CACCGATGTATTGGCCTGTATTAGTTCGAAAACTAATACAGGCCAATACA

shDF9_bottom

AAAATGTATTGGCCTGTATTAGTTTTCGAACTAATACAGGCCAATACATC

a

underlined sequences mark the complementary region for duplex formation of the shRNA.

Supplementary Table 4: Variability of hepatic CYP3A4 expression in the study population (n=150) mRNAa

Proteinb

ATVc

relative units

pmol/mg

pmol/min/mg

Minimum

0.01

1.5

0.31

Median

1.00

110

188

Maximum

3.59

1426

1397

Ratio max/min

493

950

4506

Normal

no

no

no

56.4

110

75.8

distribution Coefficient of variation (cv, %) a

CYP3A4 mRNA levels normalized to RPLP0 expression

b

Protein levels quantified in liver microsomes by Western blotting

c

Atorvastatin (ATV)-2-hydroxylation determined with liver microsomes

Supplementary Table 5: Significant associations between candidate gene-SNPs and CYP3A4 phenotypes based on multivariate analysis. SNP

AhR_rs10250822 AhR_rs2158041 AhR_rs6960165 AhR_rs7811989 ALAS1_rs352163 ALAS1_rs352165 ALAS1_rs352168 ARNT_rs10305724 ARNT_rs2134688 CAR_r4073054 CYP3A_rs7792939 CYP3A4_rs35599367 CYP3A5_rs4646450 GR_rs10482605 GR_rs258747 GR_rs33389 IL1β_rs1143627 IL1β_rs1143634 IL6R_rs11582424 IL6R_rs4240872 IL6ST_rs10940495 IL6ST_rs2112979 IL6ST_rs6870870 NFКB1_rs230530 NFКB1_rs28362491 NFКB2_rs1056890 NR5A2_rs10919806 PGC1α_rs4550905 PGRMC2_rs3733260 POR_rs1057868(*28) POR_rs10239977 PPARα_rs135551 PPARα_rs1800206 PPARα_rs4253728 PPARα_rs4823613 RARα_rs2715553 SLCO1B1_rs10841767 SLCO1B1_rs11045818 SLCO1B1_rs11045819 SLCO1B1_rs17329885 SLCO1B1_rs7966613 SLCO1B3_rs10841661 SLCO1B3_rs6487172 SRC1_rs11677500

mRNAa dom recb lab b

Proteina dom recb lab c

0.130

0.054 0.031 0.029

0.050 0.039 0.049

0.007

0.051 0.082

0.125

0.125

0.050 0.125

0.013 0.019 0.045

4-OH-ATVa domb recb lab

0.048 0.048 0.050 0.029

0.043 0.045 0.045 0.106