ACCEPTED ARTICLE PREVIEW
Genetics, epigenetics and regulation of drug metabolizing cytochrome P450 enzymes
rip
t
Ulrich M. Zanger, Kathrin Klein, Maria Thomas, Jessica K. Rieger, Roman Tremmel, Benjamin A. Kandel, Marcus Klein, Tarek Magdy
an
us c
Cite this article as: Ulrich M. Zanger, Kathrin Klein, Maria Thomas, Jessica K. Rieger, Roman Tremmel, Benjamin A. Kandel, Marcus Klein, Tarek Magdy., Genetics, epigenetics and regulation of drug metabolizing cytochrome P450 enzymes, Clinical Pharmacology & Therapeutics accepted article preview online 06 November 2013; doi:10.1038/clpt.2013.220
A cc
ep te
d
m
This is a PDF file of an unedited peer-reviewed manuscript that has been accepted for publication. NPG is providing this early version of the manuscript as a service to our customers. The manuscript will undergo copyediting, typesetting and a proof review before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.
Received 06 September 2013; accepted 30 October 2013; Accepted article preview online 06 November 2013
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
Genetics, epigenetics and regulation of drug metabolizing cytochrome P450 enzymes
Invited manuscript for the DISCOVERY section of Clinical Pharmacology and Therapeutics
Ulrich M. Zanger, Kathrin Klein, Maria Thomas, Jessica K. Rieger, Roman Tremmel, Benjamin
us c
rip
t
A. Kandel, Marcus Klein, Tarek Magdy
Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, and
d
m
an
University of Tuebingen, Tuebingen, Germany
ep te
Corresponding author: Ulrich M. Zanger, PhD, Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Auerbachstrasse 112, D-70376 Stuttgart, Germany; e-mail:
A cc
[email protected]
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
Of 57 functional human cytochromes P450 (CYP) about a dozen liver-expressed, highly variable forms are responsible for the biotransformation of most drugs. Owing to numerous genetic and non-genetic sources of variation, each individual possesses his/her own, rather unique CYP-profile. Here we explore the potential of new technologies and developments in genetics, epigenetics and regulation of gene expression to increase our understanding of the mechanisms that lead to the enormous inter- and intra-individual
t
variability of these enzymes.
rip
The human genome comprises 57 presumably functional CYP genes and a similar number of
us c
nonfunctional pseudogenes. The relevant drug-metabolizing CYPs belong to the families CYP1 (A1/A2), CYP2 (A6/B6/C8/C9/C19/D6/E1/J2) and CYP3 (A4/A5). CYPs 3A4, 2C9, 1A2,
an
and 2E1 are the most abundant forms with roughly 10-fold higher protein levels in the liver compared to the others, while the relative contribution of each form to the metabolism of
m
drugs follows a somewhat different order, which depends not only on abundance but also
d
on substrate specificity (Fig. 1). Each form varies 100-fold or more within a given population
ep te
due to genetic and non-genetic host factors and numerous environmental factors, some of which are constant over lifetime (genotype, sex) while others are dynamic (age, drug exposure, disease). These influential factors are not the same for each CYP: for example,
A cc
CYP2D6 is mainly influenced by genetic polymorphisms, while CYP3A4 is influenced by sex and inducible by a large range of substances. Therefore, every individual, at any given time, possesses his/her own unique CYP profile with important implications for drug treatment. Currently available pharmacogenomic knowledge and technology allow the prediction of only a small fraction of this variation, mainly by determining genotypes for well-studied common variants. With personal genome sequencing becoming clinical reality, will the huge amounts of data be helpful?
A broader view on genetic variations in CYP genes is enabled by the 1000 Genomes Project (http://www.1000genomes.org/). The latest data release comprises next-generation
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
sequencing data of more than 1000 genomes from different populations. What is the significance of 1000 Genomes Project data for CYPs? Table 1 summarizes genomic variation data for drug metabolizing CYPs, estimated on the basis of the currently available data. Overall SNP density varies between 14 (CYP2C19) and 127 (CYP2D6) per 1000 bp, with most of the variations found in non-coding regions. It must be emphasized that these numbers are particularly error-prone for CYPs due to the presence of pseudogenes and the resulting difficulties with (automatic) gene annotation. Looking at non-synonymous variants, between ~6% (CYP2E1) and ~30% (CYP2D6) have been previously cataloged in the CYPalleles Nomenclature Database (http://www.cypalleles.ki.se/), which provides functional
t
annotation for some variants, while no associated medical or phenotype data are available
rip
for 1000 Genomes samples. Overall, functional annotation (in vivo or in vitro) is available for
us c
less than 10% of known missense variants, including those predicted to create a stop codon or frame-shift. It is unlikely that this situation will change soon, because assignment of clinical pharmacokinetic/dynamic phenotypes to rare variants is statistically difficult. On the
an
other hand, experimental analysis remains technically demanding, laborious, and time-
m
consuming.
d
As an example, a recent CYP2B6 resequencing study identified eight new missense variants
ep te
in a relatively small number of HIV-1 infected individuals from Rwanda, in addition to known more common alleles (1). The variants appeared to be rare and probably geographically
A cc
confined to Rwanda. Functional assessment, showed that half of the variants were nearly or completely non-functional.
Rare variants of drug metabolizing enzymes are often regarded as clinically unimportant, because the poor metabolizer phenotype is a recessive trait requiring a non- or lowfunctional allele on each chromosome for manifestation. In the case of CYP2B6, the lowactivity allele CYP2B6*6 has a high world-wide prevalence, especially in Africa, with up to 60% in some populations. While CYP2B6*6 heterozygosity alone is not relevant for drug treatment, the combination of CYP2B6*6 with another non-functional allele leads to dangerously elevated drug levels associated with adverse responses. For any carrier of a rare, non-functional variant this implies a 20-60% chance (depending on ethnicity) to be a genetic poor metabolizer - not a low risk at all. Similar considerations apply to other CYPs with common low-activity alleles (CYP2A6, CYP2C9, CYP2C19, and CYP2D6).
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
In silico prediction of functional effect can be a solution, but how good is functional prediction for CYPs? Of the 68 non-functional CYP variants currently listed on the CYPalleles website, only 29 (43%) were consistently predicted as probably damaging by SIFT and PolyPhen, the prediction tools implemented in the 1000 Genomes Project. In essence this means that for a given new SNP, for example detected by routine sequencing of a patient’s genome, functional relevance cannot be predicted reliably. Based on CYP2B6 variants described above, it was shown that using a combination of tools and molecular docking of substrates improve prediction accuracy, although some variants may not be correctly predicted by any publically available tool (1). Larger scale characterization of missense
t
variants by standardized in vitro methods should allow further improvement of in silico
rip
prediction tools in a cytochrome P450-specific manner. Because effects of single variants are
us c
not necessarily additive, assessment of combinatorial alleles represent further challenges. Most genomic areas outside of protein coding sequences still represent genetic dark matter
an
but recent studies suggest their importance for pharmacogenomics. Variations in gene promoter and upstream regulatory regions, introns, 3’-UTRs or intergenic regions may
m
influence transcription, splicing, mRNA stability, or posttranscriptional regulation, with
d
subsequent impact on expression. In fact, most CYP SNPs with proven clinical impact affect
ep te
gene expression (e.g., CYP2B6*6; CYP2C19*2; CYP2D6*4; CYP2D6*41; CYP3A4*22; CYP3A5*3). Genetic variants associated with gene expression, known as expression
A cc
quantitative trait loci (eQTL), can be identified on a genome-wide scale by eQTL mapping, typically performed using oligonucleotide microarray platforms for both, SNP detection and transcriptional profiling. Liver data from several such studies confirmed some known CYP polymorphisms on a genome-wide level, while novel eQTLs concerning CYPs were rare (2). This may be either due to limited statistical power, limited SNP coverage, or it may indicate that most of the common functional CYP-polymorphisms have already been discovered. Nevertheless, the mapping of quantitative traits by association is highly relevant as it can link genomic variants in any gene region or in other genes to expression in a relevant tissue. Future studies should include greater sample numbers, more homogeneous sample origin and processing, and using RNA-seq (transcriptome sequencing) instead of microarray-based profiling, which is superior regarding qualitative (splice variants, pseudogenes) and quantitative (sensitivity, linearity) analysis.
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
Most variations known to influence CYP function are cis-variants, i.e. they are located within a defined region around a CYP gene. Variants in other genes may also influence CYP expression or function, for example transcription factors, splicing machinery components or genes required for cofactor biosynthesis, degradation, and electron transfer. Such variants are termed trans-variants, but unfortunately these are much more difficult to replicate on a genome-wide scale compared to cis-eQTLs, mainly for statistical reasons (2). However, transeQTLs can result in novel insight into gene regulation. A systematic candidate gene approach identified trans-variants that impact on hepatic CYP3A4 phenotype, i.e. microsomal enzyme activity (atorvastatin hydroxylation), protein abundance and gene expression (referenced in
t
3). SNPs in the Ah-receptor nuclear translocator (ARNT), glucocorticoid receptor (GR),
rip
progesterone receptor membrane component 2 (PGRMC2), and peroxisome proliferator–
us c
activated receptor-α (PPARA) were consistently associated with phenotype. The intronic PPARA SNP rs4253728 was also associated with decreased PPARα protein levels and with corresponding changes in atorvastatin pharmacokinetics in an in vivo cohort (referenced in
an
3). Subsequent studies revealed previously unknown functional PPARα binding sites within
m
the CYP3A4 promoter and confirmed direct transcriptional regulation of CYP3A4 by PPARα.
d
Treatment of human hepatocytes with agonistic PPARα ligands and with shRNAs to
ep te
downregulate PPARα expression indicated that additional CYPs of families 1 to 3 are regulated by this nuclear receptor, and furthermore provided evidence for a new link between lipid homeostasis and drug biotransformation in the liver (3). These findings
A cc
illustrate that novel regulatory pathways for CYPs are yet to be discovered and that genetic/genomic studies can play a decisive role. Epigenetics is a rapidly growing field of gene regulation concerned with heritable and potentially reversible changes in genome function that do not alter DNA sequence, comprising in particular DNA methylation, histone modification, and non-coding RNAs. Epigenetic mechanisms serve to silence gene expression in a reversible manner and many drug metabolizing enzymes are affected by DNA methylation of their promoter regions, with great potential for use as biomarkers (4). MicroRNAs (miRNAs) are short non-coding RNAs that imperfectly hybridize to transcripts and silence gene expression post-transcriptionally. While in silico prediction of target gene regulation by miRNAs remains difficult, their surprising stability in biofluids makes them interesting candidates as non-invasive biomarkers. Several miRNAs have been shown to modulate expression of certain CYPs
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
directly or via nuclear receptors. However, the extent of regulation in relevant tissues remained quantitatively undefined. Quantitative screening of miRNAs in human livers revealed numerous significant correlations with hepatic phenotypes of CYPs and other genes involved in drug metabolism and transport (5). Some miRNAs are furthermore significantly associated with hepatic disease states such as cholestasis and inflammation, where drug metabolism is compromised. The most comprehensive approach to gene regulation is being performed by the Encyclopedia of DNA Elements (ENCODE) Consortium, which aims to identify all functional DNA elements in the human genome, including transcriptional regions, chromatin structure,
rip
t
and histone modification (http://www.genome.gov/10005107). A range of next-generation genomic methods including RNA-seq, ChIP-seq, DNase-seq, and bisulfate sequencing are
us c
used to generate large amounts of data for integrative analysis. Unfortunately, however, CYPs are not regularly expressed in most of the numerous cell lines, primary cells and tissues
an
investigated by ENCODE. Application of the data to answer questions about CYP regulation may thus not be straightforward. Nevertheless, as the project has already assigned functions
m
for 80% of the genome, it will certainly provide a new basis to study the regulation of CYPs
ep te
CONCLUSIONS
d
on a new level.
A cc
Even in the post-genome era, cytochromes P450 present unique difficulties for genetic and functional investigation. Rare coding variants with functional impact are abundant. They may explain individual phenotypes and collectively contribute to population variability. Experimental analysis is however still tedious and prediction tools need improvement. Integrative analysis of large-scale data from genome-wide analyses platforms are promising approaches to study variants in non-coding areas, epigenetic alterations, and regulatory mechanisms. However, liver-restricted expression of most drug-metabolizing CYPs and shortage of suitable in vitro model systems limit data mining and complicate experimental validation.
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
ACKNOWLEDGMENTS This work was supported by the German Federal Ministry of Education and Research (Virtual Liver Network grant 0315755), the European Union's 7FP Training Network program ‚FightingDrugFailure’ (GA-238132), and by the Robert-Bosch Foundation, Stuttgart, Germany. CONFLICT OF INTEREST UMZ is coinventor of several patent applications directed to the detection of specific CYP
A cc
ep te
d
m
an
us c
rip
t
polymorphisms for diagnostic purposes. The other authors declare no conflict of interest.
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
REFERENCES: 1. Radloff R et al. Novel CYP2B6 Enzyme Variants in a Rwandese Population: Functional Characterization and Assessment of In Silico Prediction Tools. Hum. Mutat. 34, 725–734 (2013). 2. Glubb DM, Dholakia N, & Innocenti F. Liver expression quantitative trait loci: a foundation for pharmacogenomic research. Front. Genet. 3, 153 (2012). 3. Thomas M et al. Direct transcriptional regulation of human hepatic cytochrome P450 3A4 (CYP3A4) by peroxisome proliferator-activated receptor alpha (PPARα). Mol. Pharmacol. 83,
rip
t
709–718 (2013). 4. Ivanov M, Kacevska M, & Ingelman-Sundberg M. Epigenomics and interindividual
us c
differences in drug response. Clin. Pharmacol. Ther. 92, 727-736 (2012).
an
5. Rieger JK, Klein K, Winter S, & Zanger UM. Expression Variability of ADME-Related MicroRNAs in Human Liver: Influence of Non-Genetic Factors and Association with Gene
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ep te
d
m
Expression. Drug Metab. Dispos.[2013 Epub ahead of print]
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
Legends to figures: Figure 1. Contribution (in %) of individual cytochrome P450 isoforms to major drug metabolism pathways and factors influencing variability. A total of 248 drug metabolism pathways with known CYP involvement were analyzed. Variability factors are indicated by bold type with possible directions of influence indicated (↑, increased activity; ↓, decreased activity; ↑↓, increased and decreased activity). Factors of controversial significance are shown in parentheses. Reproduced with permission from Zanger UM, & Schwab M. Pharmacol. Ther.
A cc
ep te
d
m
an
us c
rip
t
138, 103–141 (2013).
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
Table 1. Genetic variation of human drug metabolizing CYPs SNP/kb
CYPallele
Synonymous
Non-coding
68
Nonsynonymous 114
CYP1A1
411
15 (0)
27
278
CYP1A2
465
63
105
24 (7)
39
328
CYP2A6
532
77
115
43 (11)
76
353
CYP2B6
1293
48
110
51 (11)
61
1135
CYP2C8
851
26
81
12 (7)
32
754
CYP2C9
1472
29
103
60 (14)
66
1314
CYP2C18
1196
22
75
N.a.
32
1099
CYP2C19
2298
14
122
CYP2D6
558
127
134
CYP2E1
824
20
81
CYP2J2
798
24
73
CYP3A4
750
27
CYP3A5
710
22
42
2145
77 (24)
102
341
4 (1)
39
720
8 (4)
35
697
87
22 (7)
44
629
12 (2)
33
619
an
us c
33 (8)
m
rip
t
All SNPs
ep te
d
74
A cc
Data were compiled from 1000 Genomes variation tables displayed for the main transcript of each CYP (http://browser.1000genomes.org/). Numbers in categories do not sum up to total “All SNPs” because of ambiguous assignments. “CYPallele” denotes non-synonymous variants cataloged on the CYPalleles website (http://www.cypalleles.ki.se/) and numbers in brackets refer to variants with functional annotation. N.a., not applicable (CYP2C18 is not expressed as protein).
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
Figure 1
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
Genetics, epigenetics and regulation of drug metabolizing cytochrome P450 enzymes
rip
t
Ulrich M. Zanger, Kathrin Klein, Maria Thomas, Jessica K. Rieger, Roman Tremmel, Benjamin A. Kandel, Marcus Klein, Tarek Magdy
an
us c
Cite this article as: Ulrich M. Zanger, Kathrin Klein, Maria Thomas, Jessica K. Rieger, Roman Tremmel, Benjamin A. Kandel, Marcus Klein, Tarek Magdy., Genetics, epigenetics and regulation of drug metabolizing cytochrome P450 enzymes, Clinical Pharmacology & Therapeutics accepted article preview online 06 November 2013; doi:10.1038/clpt.2013.220
A cc
ep te
d
m
This is a PDF file of an unedited peer-reviewed manuscript that has been accepted for publication. NPG is providing this early version of the manuscript as a service to our customers. The manuscript will undergo copyediting, typesetting and a proof review before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.
Received 06 September 2013; accepted 30 October 2013; Accepted article preview online 06 November 2013
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
Genetics, epigenetics and regulation of drug metabolizing cytochrome P450 enzymes
Invited manuscript for the DISCOVERY section of Clinical Pharmacology and Therapeutics
Ulrich M. Zanger, Kathrin Klein, Maria Thomas, Jessica K. Rieger, Roman Tremmel, Benjamin
us c
rip
t
A. Kandel, Marcus Klein, Tarek Magdy
Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, and
d
m
an
University of Tuebingen, Tuebingen, Germany
ep te
Corresponding author: Ulrich M. Zanger, PhD, Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Auerbachstrasse 112, D-70376 Stuttgart, Germany; e-mail:
A cc
[email protected]
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
Of 57 functional human cytochromes P450 (CYP) about a dozen liver-expressed, highly variable forms are responsible for the biotransformation of most drugs. Owing to numerous genetic and non-genetic sources of variation, each individual possesses his/her own, rather unique CYP-profile. Here we explore the potential of new technologies and developments in genetics, epigenetics and regulation of gene expression to increase our understanding of the mechanisms that lead to the enormous inter- and intra-individual
t
variability of these enzymes.
rip
The human genome comprises 57 presumably functional CYP genes and a similar number of
us c
nonfunctional pseudogenes. The relevant drug-metabolizing CYPs belong to the families CYP1 (A1/A2), CYP2 (A6/B6/C8/C9/C19/D6/E1/J2) and CYP3 (A4/A5). CYPs 3A4, 2C9, 1A2,
an
and 2E1 are the most abundant forms with roughly 10-fold higher protein levels in the liver compared to the others, while the relative contribution of each form to the metabolism of
m
drugs follows a somewhat different order, which depends not only on abundance but also
d
on substrate specificity (Fig. 1). Each form varies 100-fold or more within a given population
ep te
due to genetic and non-genetic host factors and numerous environmental factors, some of which are constant over lifetime (genotype, sex) while others are dynamic (age, drug exposure, disease). These influential factors are not the same for each CYP: for example,
A cc
CYP2D6 is mainly influenced by genetic polymorphisms, while CYP3A4 is influenced by sex and inducible by a large range of substances. Therefore, every individual, at any given time, possesses his/her own unique CYP profile with important implications for drug treatment. Currently available pharmacogenomic knowledge and technology allow the prediction of only a small fraction of this variation, mainly by determining genotypes for well-studied common variants. With personal genome sequencing becoming clinical reality, will the huge amounts of data be helpful?
A broader view on genetic variations in CYP genes is enabled by the 1000 Genomes Project (http://www.1000genomes.org/). The latest data release comprises next-generation
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
sequencing data of more than 1000 genomes from different populations. What is the significance of 1000 Genomes Project data for CYPs? Table 1 summarizes genomic variation data for drug metabolizing CYPs, estimated on the basis of the currently available data. Overall SNP density varies between 14 (CYP2C19) and 127 (CYP2D6) per 1000 bp, with most of the variations found in non-coding regions. It must be emphasized that these numbers are particularly error-prone for CYPs due to the presence of pseudogenes and the resulting difficulties with (automatic) gene annotation. Looking at non-synonymous variants, between ~6% (CYP2E1) and ~30% (CYP2D6) have been previously cataloged in the CYPalleles Nomenclature Database (http://www.cypalleles.ki.se/), which provides functional
t
annotation for some variants, while no associated medical or phenotype data are available
rip
for 1000 Genomes samples. Overall, functional annotation (in vivo or in vitro) is available for
us c
less than 10% of known missense variants, including those predicted to create a stop codon or frame-shift. It is unlikely that this situation will change soon, because assignment of clinical pharmacokinetic/dynamic phenotypes to rare variants is statistically difficult. On the
an
other hand, experimental analysis remains technically demanding, laborious, and time-
m
consuming.
d
As an example, a recent CYP2B6 resequencing study identified eight new missense variants
ep te
in a relatively small number of HIV-1 infected individuals from Rwanda, in addition to known more common alleles (1). The variants appeared to be rare and probably geographically
A cc
confined to Rwanda. Functional assessment, showed that half of the variants were nearly or completely non-functional.
Rare variants of drug metabolizing enzymes are often regarded as clinically unimportant, because the poor metabolizer phenotype is a recessive trait requiring a non- or lowfunctional allele on each chromosome for manifestation. In the case of CYP2B6, the lowactivity allele CYP2B6*6 has a high world-wide prevalence, especially in Africa, with up to 60% in some populations. While CYP2B6*6 heterozygosity alone is not relevant for drug treatment, the combination of CYP2B6*6 with another non-functional allele leads to dangerously elevated drug levels associated with adverse responses. For any carrier of a rare, non-functional variant this implies a 20-60% chance (depending on ethnicity) to be a genetic poor metabolizer - not a low risk at all. Similar considerations apply to other CYPs with common low-activity alleles (CYP2A6, CYP2C9, CYP2C19, and CYP2D6).
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
In silico prediction of functional effect can be a solution, but how good is functional prediction for CYPs? Of the 68 non-functional CYP variants currently listed on the CYPalleles website, only 29 (43%) were consistently predicted as probably damaging by SIFT and PolyPhen, the prediction tools implemented in the 1000 Genomes Project. In essence this means that for a given new SNP, for example detected by routine sequencing of a patient’s genome, functional relevance cannot be predicted reliably. Based on CYP2B6 variants described above, it was shown that using a combination of tools and molecular docking of substrates improve prediction accuracy, although some variants may not be correctly predicted by any publically available tool (1). Larger scale characterization of missense
t
variants by standardized in vitro methods should allow further improvement of in silico
rip
prediction tools in a cytochrome P450-specific manner. Because effects of single variants are
us c
not necessarily additive, assessment of combinatorial alleles represent further challenges. Most genomic areas outside of protein coding sequences still represent genetic dark matter
an
but recent studies suggest their importance for pharmacogenomics. Variations in gene promoter and upstream regulatory regions, introns, 3’-UTRs or intergenic regions may
m
influence transcription, splicing, mRNA stability, or posttranscriptional regulation, with
d
subsequent impact on expression. In fact, most CYP SNPs with proven clinical impact affect
ep te
gene expression (e.g., CYP2B6*6; CYP2C19*2; CYP2D6*4; CYP2D6*41; CYP3A4*22; CYP3A5*3). Genetic variants associated with gene expression, known as expression
A cc
quantitative trait loci (eQTL), can be identified on a genome-wide scale by eQTL mapping, typically performed using oligonucleotide microarray platforms for both, SNP detection and transcriptional profiling. Liver data from several such studies confirmed some known CYP polymorphisms on a genome-wide level, while novel eQTLs concerning CYPs were rare (2). This may be either due to limited statistical power, limited SNP coverage, or it may indicate that most of the common functional CYP-polymorphisms have already been discovered. Nevertheless, the mapping of quantitative traits by association is highly relevant as it can link genomic variants in any gene region or in other genes to expression in a relevant tissue. Future studies should include greater sample numbers, more homogeneous sample origin and processing, and using RNA-seq (transcriptome sequencing) instead of microarray-based profiling, which is superior regarding qualitative (splice variants, pseudogenes) and quantitative (sensitivity, linearity) analysis.
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
Most variations known to influence CYP function are cis-variants, i.e. they are located within a defined region around a CYP gene. Variants in other genes may also influence CYP expression or function, for example transcription factors, splicing machinery components or genes required for cofactor biosynthesis, degradation, and electron transfer. Such variants are termed trans-variants, but unfortunately these are much more difficult to replicate on a genome-wide scale compared to cis-eQTLs, mainly for statistical reasons (2). However, transeQTLs can result in novel insight into gene regulation. A systematic candidate gene approach identified trans-variants that impact on hepatic CYP3A4 phenotype, i.e. microsomal enzyme activity (atorvastatin hydroxylation), protein abundance and gene expression (referenced in
t
3). SNPs in the Ah-receptor nuclear translocator (ARNT), glucocorticoid receptor (GR),
rip
progesterone receptor membrane component 2 (PGRMC2), and peroxisome proliferator–
us c
activated receptor-α (PPARA) were consistently associated with phenotype. The intronic PPARA SNP rs4253728 was also associated with decreased PPARα protein levels and with corresponding changes in atorvastatin pharmacokinetics in an in vivo cohort (referenced in
an
3). Subsequent studies revealed previously unknown functional PPARα binding sites within
m
the CYP3A4 promoter and confirmed direct transcriptional regulation of CYP3A4 by PPARα.
d
Treatment of human hepatocytes with agonistic PPARα ligands and with shRNAs to
ep te
downregulate PPARα expression indicated that additional CYPs of families 1 to 3 are regulated by this nuclear receptor, and furthermore provided evidence for a new link between lipid homeostasis and drug biotransformation in the liver (3). These findings
A cc
illustrate that novel regulatory pathways for CYPs are yet to be discovered and that genetic/genomic studies can play a decisive role. Epigenetics is a rapidly growing field of gene regulation concerned with heritable and potentially reversible changes in genome function that do not alter DNA sequence, comprising in particular DNA methylation, histone modification, and non-coding RNAs. Epigenetic mechanisms serve to silence gene expression in a reversible manner and many drug metabolizing enzymes are affected by DNA methylation of their promoter regions, with great potential for use as biomarkers (4). MicroRNAs (miRNAs) are short non-coding RNAs that imperfectly hybridize to transcripts and silence gene expression post-transcriptionally. While in silico prediction of target gene regulation by miRNAs remains difficult, their surprising stability in biofluids makes them interesting candidates as non-invasive biomarkers. Several miRNAs have been shown to modulate expression of certain CYPs
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
directly or via nuclear receptors. However, the extent of regulation in relevant tissues remained quantitatively undefined. Quantitative screening of miRNAs in human livers revealed numerous significant correlations with hepatic phenotypes of CYPs and other genes involved in drug metabolism and transport (5). Some miRNAs are furthermore significantly associated with hepatic disease states such as cholestasis and inflammation, where drug metabolism is compromised. The most comprehensive approach to gene regulation is being performed by the Encyclopedia of DNA Elements (ENCODE) Consortium, which aims to identify all functional DNA elements in the human genome, including transcriptional regions, chromatin structure,
rip
t
and histone modification (http://www.genome.gov/10005107). A range of next-generation genomic methods including RNA-seq, ChIP-seq, DNase-seq, and bisulfate sequencing are
us c
used to generate large amounts of data for integrative analysis. Unfortunately, however, CYPs are not regularly expressed in most of the numerous cell lines, primary cells and tissues
an
investigated by ENCODE. Application of the data to answer questions about CYP regulation may thus not be straightforward. Nevertheless, as the project has already assigned functions
m
for 80% of the genome, it will certainly provide a new basis to study the regulation of CYPs
ep te
CONCLUSIONS
d
on a new level.
A cc
Even in the post-genome era, cytochromes P450 present unique difficulties for genetic and functional investigation. Rare coding variants with functional impact are abundant. They may explain individual phenotypes and collectively contribute to population variability. Experimental analysis is however still tedious and prediction tools need improvement. Integrative analysis of large-scale data from genome-wide analyses platforms are promising approaches to study variants in non-coding areas, epigenetic alterations, and regulatory mechanisms. However, liver-restricted expression of most drug-metabolizing CYPs and shortage of suitable in vitro model systems limit data mining and complicate experimental validation.
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
ACKNOWLEDGMENTS This work was supported by the German Federal Ministry of Education and Research (Virtual Liver Network grant 0315755), the European Union's 7FP Training Network program ‚FightingDrugFailure’ (GA-238132), and by the Robert-Bosch Foundation, Stuttgart, Germany. CONFLICT OF INTEREST UMZ is coinventor of several patent applications directed to the detection of specific CYP
A cc
ep te
d
m
an
us c
rip
t
polymorphisms for diagnostic purposes. The other authors declare no conflict of interest.
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
ACCEPTED ARTICLE PREVIEW
REFERENCES: 1. Radloff R et al. Novel CYP2B6 Enzyme Variants in a Rwandese Population: Functional Characterization and Assessment of In Silico Prediction Tools. Hum. Mutat. 34, 725–734 (2013). 2. Glubb DM, Dholakia N, & Innocenti F. Liver expression quantitative trait loci: a foundation for pharmacogenomic research. Front. Genet. 3, 153 (2012). 3. Thomas M et al. Direct transcriptional regulation of human hepatic cytochrome P450 3A4 (CYP3A4) by peroxisome proliferator-activated receptor alpha (PPARα). Mol. Pharmacol. 83,
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709–718 (2013). 4. Ivanov M, Kacevska M, & Ingelman-Sundberg M. Epigenomics and interindividual
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differences in drug response. Clin. Pharmacol. Ther. 92, 727-736 (2012).
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5. Rieger JK, Klein K, Winter S, & Zanger UM. Expression Variability of ADME-Related MicroRNAs in Human Liver: Influence of Non-Genetic Factors and Association with Gene
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Expression. Drug Metab. Dispos.[2013 Epub ahead of print]
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
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Legends to figures: Figure 1. Contribution (in %) of individual cytochrome P450 isoforms to major drug metabolism pathways and factors influencing variability. A total of 248 drug metabolism pathways with known CYP involvement were analyzed. Variability factors are indicated by bold type with possible directions of influence indicated (↑, increased activity; ↓, decreased activity; ↑↓, increased and decreased activity). Factors of controversial significance are shown in parentheses. Reproduced with permission from Zanger UM, & Schwab M. Pharmacol. Ther.
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138, 103–141 (2013).
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
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Table 1. Genetic variation of human drug metabolizing CYPs SNP/kb
CYPallele
Synonymous
Non-coding
68
Nonsynonymous 114
CYP1A1
411
15 (0)
27
278
CYP1A2
465
63
105
24 (7)
39
328
CYP2A6
532
77
115
43 (11)
76
353
CYP2B6
1293
48
110
51 (11)
61
1135
CYP2C8
851
26
81
12 (7)
32
754
CYP2C9
1472
29
103
60 (14)
66
1314
CYP2C18
1196
22
75
N.a.
32
1099
CYP2C19
2298
14
122
CYP2D6
558
127
134
CYP2E1
824
20
81
CYP2J2
798
24
73
CYP3A4
750
27
CYP3A5
710
22
42
2145
77 (24)
102
341
4 (1)
39
720
8 (4)
35
697
87
22 (7)
44
629
12 (2)
33
619
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33 (8)
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All SNPs
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Data were compiled from 1000 Genomes variation tables displayed for the main transcript of each CYP (http://browser.1000genomes.org/). Numbers in categories do not sum up to total “All SNPs” because of ambiguous assignments. “CYPallele” denotes non-synonymous variants cataloged on the CYPalleles website (http://www.cypalleles.ki.se/) and numbers in brackets refer to variants with functional annotation. N.a., not applicable (CYP2C18 is not expressed as protein).
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
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Figure 1
© 2013 American Society for Clinical Pharmacology and Therapeutics. All rights reserved
