CYP3A4 and CYP3A5 activities are therefore not specifically discriminable Because of their involvement in the metabolism not only of naturally .. Numerous additional genetic POR variants have been identified (Hart et al. The determination of an individual's genotype for key enzymes that participate Of all enzymes of pharmacokinetic importance, the CYP 2D6 gene that of dextromethorphan (DM) and its principal metabolite o-demethylated. The enzyme CYP3A4 can metabolize both THC and CBD. Its *22 genetic CYP3A4 Activity Is Determined By Your Genetics. The activity of.
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This provided a basis for the identification of key functional modules within the networks that contribute to variability of traits of interest.
We have previously described the characterization of transcriptional coexpression networks based on brain, adipose, and liver tissues in human and mouse Gargalovic et al. Building on this approach, we constructed a coexpression network based on the human liver tissue data to identify gene modules.
We identified multiple modules demonstrating functional enrichment that correlated with both P expression and enzyme activity. The turquoise module harbored a majority of the P genes and showed the most significant positive correlation with P traits.
The connections between oxidative stress genes as well as acute phase inflammatory genes with Ps have been noted before Strolin-Benedetti et al. As the connectivity levels of the genes within these modules were found to be positively correlated with their module-to-trait relevance, we were also able to identify the hubs, or the key genes in each module. Among the top hub genes in the turquoise module, hydroxyacid oxidase 1 HAO1 and enoyl coenzyme A hydratase 3-hydroxyacyl coenzyme A dehydrogenase EHHADH are oxidoreductases involved in fatty acid oxidation Hardwick ; Tomaszewski et al.
For the other modules that were correlated with P traits, the top hub genes were mostly acute phase response genes and those involved in translation as mentioned above. Although these top hub genes may represent novel regulators of Ps, coexpression modules cannot differentiate modules that are causally linked to Ps from those downstream of Ps.
This can be partially resolved with the genetic information collected in the same cohort, as discussed below. Genome-wide association studies between DNA variations and gene expression or enzyme activity can provide insights into the genetic regulation of Ps, and genetic information is a useful anchor to infer causality Schadt et al.
In the present setting, the term causality is used from the standpoint of statistical inference, where statistical associations between changes in DNA, changes in expression or other molecular phenotypes , and changes in complex phenotypes like disease are examined for patterns of statistical dependency among these variables that support directionality among them, where the directionality then provides the source of causal information highlighting putative regulatory control as opposed to physical interaction for further experimental testing.
We identified cis -eSNPs that were associated with the expression of one-third of the Ps, suggesting that many Ps are regulated by cis -acting polymorphisms. We also identified aSNPs that were associated with the enzyme activity measurements for most of the Ps tested.
These lines of evidence suggest that Ps are under strong genetic control. However, only the cis -eSNPs of the CYP2D6 gene were coincident with aSNPs of the corresponding enzyme activity, indicating that for many of the other Ps, the regulatory path from gene transcription to protein translation to activity is more complicated. A comparison of the eSNPs and aSNPs with the putative functional polymorphisms previously reported in the literature http: The limited overlap could result from the fact that the majority of the literature reports were based on single-gene analysis, whereas a genome-wide approach was used in our study, which involved aggressive multiple-testing correction.
An additional explanation could be that many of the literature findings were derived from in vitro systems, whereas human tissues were used in our study http: Moreover, the SNP coverage of the genotyping panels used in this study was less than optimal, as these platforms were not designed specifically for P studies but more for genome-wide coverage.
Such design results in insufficient coverage of SNPs in the coding regions of Ps. All these indicate that our study covers biological space different from the previous studies, and hence, any discoveries made in this study are potentially novel. The fact that we are able to recapitulate the associations between three CYP2D6 aSNPs and the enzyme activity levels in an independent cohort supports their true discovery nature, and the identification of the strong relationship between these aSNPs and known functional variants further highlights their relevance.
These cis - and trans -calls are suggestive of cis - or trans -regulation but by no means directly demonstrate true cis - or trans -regulation. By mapping genes with cis -eSNPs and aSNPs to the coexpression modules, we were able to differentiate the turquoise and brown modules as upstream causal modules from the red and pink modules.
The top hub genes from the turquoise and brown modules were thus candidate regulatory genes for Ps. SLC10A1 is solute carrier family 10 member 1 and functions as a sodium-dependent bile acid transporter. AKR1D1 is aldo-keto reductase family 1 member D1 and plays a role in bile acid synthesis and steroid hormone metabolism Lee et al. These genes are relevant to the functions and regulatory pathways of Ps, and this study implicates these genes as P regulators for the first time. Validation studies involving perturbation of these genes are underway.
Even though this study is the first comprehensive survey of human Ps in a large cohort, there are some limitations to the interpretation of the data. For example, medication information was not available for a majority of the individuals in the cohort and therefore not taken into consideration in the current study.
Since many drugs can be inducers or inhibitors of Ps, noise could have been introduced to the analyses due to the inability to control for medication status. We also acknowledge that one of the limitations of BN approach is that feedback regulation cannot be accommodated.
In addition, although we have demonstrated the utility of BNs in predicting how genetic and environmental perturbation signals propagate in biological systems in controlled study populations Zhu et al. Furthermore, the directionality of the outcome of any perturbation of genes in a network such as whether the perturbation will raise or lower a phenotypic outcome depends on the genetic background and environmental conditions due to complex network feedbacks Davey Smith and Ebrahim ; Chen et al.
Last but not least, confounding effects from pleiotropy and LD can complicate causality inference, and the interpretation of causality results can be further complicated by factors such as morphogenic stability, developmental adaptation, and canalization Davey Smith and Ebrahim ; Schadt et al. Therefore, some of the conclusions should be interpreted with care, and more follow-up studies are needed to test the hypothesis put forward.
In summary, we conducted a genetic and genomic survey of the P system in a large HLC. We found that Ps were highly correlated among themselves, with known regulators, and with thousands of functionally relevant genes.
A coexpression module that contained a majority of P genes and several known P regulators was found to be most significantly correlated with P expression and activity measures. The same module was also highly enriched for genes with regulatory SNPs and harbored several novel candidate P regulators that were also identified using a BN approach. The Bayesian subnetwork identified was highly enriched for gene signatures elicited by known ligands of P regulators.
This study provides broad insights into the regulation of P enzymes that underlie the interindividual pharmacokinetic and pharmacodynamic variability of Ps as well as the disease susceptibility, drug response, drug—drug interactions, and toxicity associated with Ps in the general population. The interrelationship between Ps, the regulatory mechanisms e. As described previously Schadt et al. Of the samples collected, high-quality RNA was successfully isolated and profiled on Caucasian subjects.
These subjects included the samples with successful genotype data described in the previous study Schadt et al. The sample size for each individual analysis varied and was a function of the data intersected in the particular analysis. For example, samples were involved in gene—gene correlation analysis; samples were used for trait—trait correlation and gene—trait correlation analyses; samples were used for eSNP discovery. Age, gender, and ethnicity were confirmed or imputed in cases of missing data as described previously Schadt et al.
Each of the liver samples was processed into cytosol and microsomes at CellzDirect using a standard differential centrifugation method. Frozen liver tissues were thawed on ice in homogenization buffer 50 mM Tris at pH 7.
The pellet was resuspended in homogenization buffer, recentrifuged, and resuspended in 0. The concentration of microsomal protein was determined with a bicinchoninic acid kit BCA protein assay, Pierce Chemical based on instructions provided by the manufacturer. The concentration of microsomal protein per gram of liver tissue was determined.
Microsomes were diluted to an equivalent concentration before evaluation. Two substrates were used for the CYP3A4 activity measurement: The substrate, in 0. Reactions were initiated by addition of NADPH 1 mM final concentration and incubated for the indicated times with gentle agitation.
The rate of substrate turnover was linear under these reaction conditions during method validations. Control incubations included pooled human liver microsomes CellzDirect; pooled from 15 male and female individuals as positive control samples as well as samples without NADPH. Metabolite standard curves with eight concentrations were included with each analytical run.
In addition, bioanalytical metabolite quality control samples QC at three concentrations with four replicates spaced throughout the analytical run were measured. All analytical runs were evaluated based on predefined acceptance criteria as follows: Mass spectrometry data were acquired, integrated, regressed, and quantified with MassLynx software, version 3. Data were graphed and calculated using Microsoft Excel Reaction velocities v were calculated using the following equation: All enzyme activity data have been deposited to the Sage Commons Repository and are freely available at http: The microarray design, RNA sample preparation, amplification, hybridization, and expression analysis were previously described in detail Schadt et al.
Each RNA sample was profiled on a custom Agilent 44, feature microarray composed of 39, oligonucleotide probes targeting transcripts representing 34, known and predicted genes, including high-confidence, noncoding RNA sequences. Arrays were quantified on the basis of spot intensity relative to background, adjusted for experimental variation between arrays using average intensity over multiple channels, and fitted to an error model to determine significance type I error He et al.
Gene expression is reported as the mean-log ratio relative to the pool derived from liver samples selected for balance from the Vanderbilt and Pittsburgh samples as the RNA from the Merck samples had been amplified at an earlier date. Thorough quality control was conducted to assess parameters such as study site, RNA quality, batch effects, age, and gender.
The most significant covariate was study site, which was confounded with amplification driven mostly by but not solely the processing of the Merck samples separately and lack of representation in the reference pool. In addition to study site, gender and age were also significant covariates. These covariates were all controlled for in the analysis. However, due to the nature of the cohort and Institutional Review Board IRB restrictions, information on these factors was very sparse, and thus was not incorporated directly in this analysis.
The iterative algorithm joins the most similar traits at each stage based on the distances computed by the Lance—Williams dissimilarity update formula. These genes represent those whose expression levels vary across samples and, thus, are more biologically relevant. Partial Spearman correlation was calculated between the selected transcripts and the activity measurements of the nine Ps by adjusting both the transcripts and enzyme activity traits for covariates including age, gender, and study site.
The the top Different from the traditional unweighted gene coexpression network, where two genes nodes are either connected or disconnected, the weighted gene coexpression network analysis assigns a connection weight to each gene pair using soft-thresholding and thus is robust to parameter selection. To measure how well a network satisfies a scale-free topology, we use the fitting index proposed by Zhang and Horvath , that is, the model fitting index R 2 of the linear model that regresses log [ p k ] on log k , where k is connectivity and p k is the frequency distribution of connectivity.
The fitting index of a perfect scale-free network is 1. The distribution p k of the resulting network approximates a power law: To explore the modular structures of the coexpression network, the adjacency matrix is further transformed into a TOM Ravasz et al. As the topological overlap between two genes reflects not only their direct interaction but also their indirect interactions through all the other genes in the network, previous studies Ravasz et al. To identify modules of highly coregulated genes, we used average linkage hierarchical clustering to group genes based on the topological overlap of their connectivity, followed by a dynamic cut-tree algorithm to dynamically cut clustering dendrogram branches into gene modules Langfelder et al.
Eight modules are identified, and the module size, in number of genes, varies from 55— To distinguish between modules, each module was assigned a unique color identifier, with the remaining, poorly connected genes colored gray. Figure 3A shows the hierarchical clustering over the TOM and the identified modules.
In this type of map, the rows and the columns represent genes in a symmetric fashion, and the color intensity represents the interaction strength between genes. This connectivity map highlights that genes in the liver transcriptional network fall into distinct network modules, where genes within a given module are more interconnected with each other blocks along the diagonal of the matrix than with genes in other modules.
There are a couple of network connectivity measures, but one particularly important one is the within module connectivity k. DNA isolation, genotyping with Affymetrix K genotyping array and Illumina Y panel, and quality control procedures were described in detail previously Schadt et al. The Kruskal-Wallis test was used to determine association between age and gender-adjusted expression or enzyme activity traits and genotypes of the approximately k SNPs. We chose this nonparametric method because it is robust to underlying genetic model and trait distribution.
We employed FDR for multiple-test correction. Since the number of tests was large, we found the empirical null distribution was very stable, and five permutation runs were sufficient for convergence to estimate FDR. For aSNPs, the P -value cutoff was 3. The validation cohort comprised Caucasian subjects previously described in detail Gaedigk et al. A total of genes were selected for inclusion in the network reconstruction process based on criteria: The genes were input into a BN reconstruction software program based on a previously described algorithm Zhu et al.
Genetics information was used as priors as follows: Bayesian information criteria BIC was used in the reconstruction process. One thousand BNs were reconstructed using different random seeds to start the reconstruction process. Edges in this consensus network were removed if 1 the edge was involved in a loop and 2 the edge was the most weakly supported of all edges making up the loop.
In order to determine whether a subnetwork is enriched for Ps, the following procedure was used. For a given subnetwork, the procedure first computes the signature of each node that could be reached by the node following directed links throughout the entire subnetwork. Then, the signature of each node was intersected with the set of P genes. We thank Jonathan Derry for valuable discussions on the manuscript.
Article published online before print. Article and publication date are at http: Steven Leeder 2 , F. Peter Guengerich 3 , Stephen C. Strom 4 , Erin Schuetz 5 , Thomas H. Rushmore 6 , Roger G.
Ulrich 7 , J. Greg Slatter 8 , Eric E. Previous Section Next Section. The human liver cohort and overall data analysis scheme The liver tissues of Caucasian samples from a previously described human liver cohort HLC were used in the current study Schadt et al.
Relationship between Ps Pair-wise Spearman correlations were employed to determine the relationships between the various enzyme activity measurements. Relationship between Ps and known regulators In order to evaluate the role of known P regulators in modulating the expression and enzyme activity levels of different Ps in our cohort, we investigated the correlation of the expression of 48 key regulators reported in the literature with the P expression as well as the enzyme activity traits.
P enzyme activity levels are highly correlated with their corresponding coding genes as well as many additional genes In order to test whether the enzyme activity level is mainly regulated at the gene expression level and whether each of the enzymatic measures capture information from genes other than the corresponding P genes, we analyzed the correlation between a selected set of transcriptionally active transcripts for details, see Methods and the activity measurements of each P In this window In a new window.
Gene coexpression network analysis The fact that many genes were found to be correlated with each enzyme activity measurement suggests these genes are coregulated, and as such, the coexpression structure may provide insights into the regulation of Ps. Gene module relevance to P enzyme activities and gene expression To examine how each gene module is related to P enzyme activities or gene expression, we performed principal component analysis PCA for each module and then took as module relevance the correlation between the first principal component module eigengene and the P enzyme measures or expression levels.
Identification of SNPs associated with P gene expression eSNPs In order to survey the contribution of genetic variations to P gene expression, we analyzed the association of the transcripts of the 54 P genes represented on the human 44k array with , unique SNPs that were represented, successfully genotyped, and passed quality control for details, see Methods in the HLC using both the Affymetrix K and Illumina Y panels Schadt et al.
SNPs associated with P enzyme activities aSNPs The identification of polymorphisms that associate strongly with P expression also leads us to investigate polymorphisms that associate strongly with P enzyme activities.
Constructing a predictive BN from the HLC data As an alternative approach to identifying the pathways and regulators for Ps, we used a Bayesian gene regulatory network reconstructed based on the HLC gene expression and genotyping data Schadt et al. Human liver cohort As described previously Schadt et al. Human liver microsome preparation Each of the liver samples was processed into cytosol and microsomes at CellzDirect using a standard differential centrifugation method.
RNA profiling The microarray design, RNA sample preparation, amplification, hybridization, and expression analysis were previously described in detail Schadt et al.
Correlation and hierarchical clustering of P enzyme activity traits Pairwise Spearman correlation among the residuals of the P activity traits after adjusting for covariates age, gender, and study site was calculated using the cor.
Constructing the HLC coexpression network The the top Reconstruction of HLC Bayesian gene regulatory network A total of genes were selected for inclusion in the network reconstruction process based on criteria: Freely available online through the Genome Research Open Access option. Smoking habit and genetic factors associated with lung cancer in a population highly exposed to arsenic. CrossRef Medline Google Scholar.
Regulation of cytochrome P by posttranslational modification. Drug Metab Rev Sexual dimorphism of rat liver gene expression: Regulatory role of growth hormone revealed by deoxyribonucleic acid microarray analysis.
These samples allowed the precise determination of the parental origin of alleles in the heterozygous children. We firstly determined the parent's genotypes using genomic DNA samples. At the Mnl I site for C1 in Fig. At the Afa I site for C2 , the corresponding genotypes were homozygous for the G allele and heterozygous for the A allele, respectively. In the RT—PCR products, in contrast to C1, who showed a monoallelic maternal T expression, the sibling C2 showed a biallelic expression, but the paternal allele A was preferentially expressed.
It is interesting to know whether allelic variation is inherited. In order to address this issue, we further analyzed allelic expression patterns using paternal RT—PCR products, because paternal genotype was heterozygous for both polymorphic sites at genomic DNA-based genotypes. As shown in Figure 4 B, paternal alleles were inherited by the two siblings; the paternal inactive allele i. These results suggest that allelic variation is inherited, at least in B virus-transformed lymphoblasts.
Although differentially methylated CpG sites were found, most of the sites analyzed were largely methylated Fig. No clear association between the methylation status and total mRNA levels was observed.
Correlations between the allelic expression ratio and phenotype indexes are shown in Figure 6. Of these 18 samples, we could measure CYP3A4 activity using testosterone as an enzyme-specific substrate in These results indicate that the individuals with a low ratio, who exhibited a large difference in hnRNA expression level between the two alleles, have extremely low total hepatic CYP3A4 mRNA levels, and consequently poor metabolic capability.
We determined frequencies of heterozygous carriers for CT and CT polymorphisms, which were relatively common among the eight liver samples, using genomic DNA from three racial populations Table 1. We found that the frequency of heterozygous carriers of the CT allele in Caucasians was 0. The corresponding values for the CT allele were 0. When excluding individuals having two SNPs simultaneously, Variations in gene sequence and expression underlie much of human variability.
As has been expected, none of the SNPs in these regions was clearly associated with differences in CYP3A4 levels and metabolic capability. These findings raise the possibility that other mechanisms such as an epigenetic gene alteration affect CYP3A4 levels more frequently than SNPs 7 — In the present study, we used unspliced hnRNA as the template and used intronic SNPs as the marker to assess the allelic variation, because there were no exonic SNPs that can be used to test for allelic variation in mRNA levels.
Thus, we secondarily examined whether tissue levels of hnRNA, the unprocessed precursor of the mature, functional mRNA, can be used as a surrogate for gene transcription. To ensure precise determinations for hnRNA and mRNA levels separately, we have designed intron- and exon-specific oligonucleotide primers, respectively. As shown in Figure 2 , the two exhibited a significant correlation. Recently, in order to determine allelic variation, an allele-specific quantitative PCR method with allele-specific probes has been developed.
Such an analytical method provides the direct expression level of each allele separately by measuring the fluorescent intensity 22 , In the present study, we had also tried to develop a real-time quantitative PCR method; however, we were unsuccessful owing to a technical limitation, low expression levels of CYP3A4 hnRNA, especially in samples indicating monoallelic expression.
Thus, we determined allelic variations on the basis of difference of band intensities between the two alleles using a fluorescence image analyzer. With such an analytical method, in addition to DNA contamination, splicing variants are drawbacks for the accurate estimation of allelic variation. Differential allelic gene expression resulting from genomic imprinting has been a focus of cancer research.
Among known imprinted genes, the Wilms' tumor suppressor gene WT1 has been reported to exhibit a unique allele-specific expression profile 35 ; cultured human fibroblasts and lymphocytes showed a paternal or biallelic expression of WT1 in some cases, whereas a maternal or biallelic expression was observed in human placental villi and fetal brain tissue 36 , These results suggest that the allele-specific expression profile e.
Indeed, although somatic allele switching is not a common feature of imprinted genes, this unusual phenomenon is also observed in human H19 38 and IMPT1 39 genes. However, in the present study, the degree of difference in the expression between the two alleles varied among samples, and large variations were observed in only a minority of samples Fig.
In addition, informative lymphoblast samples indicated opposite-directional expression; both paternal and maternal preferential expressions were observed in the two siblings Fig. Thus, taking these findings into consideration, it is feasible that, at least in human liver, CYP3A4 is not an imprinting gene. Although allelic variation in gene expression is common in the human genome 17 , 22 , its pharmacokinetic and pharmacodynamic significance has not been reported.
One study has demonstrated that the allelic variation in APC gene expression plays a critical role in colon cancer The present study, however, is the first to demonstrate that variations in CYP3A4 phenotypes are caused by changes in allelic expression levels.
The strong correlation between the allelic ratio and phenotypic indexes e. Because these liver-enriched trans -activating factors play an important roles in the constitutive expression of CYP3A4 , variations in their expression could ultimately be responsible for the different expression levels of CYP3A4 found in various human tissues; CYP3A4 is expressed primarily in liver and intestine and at very low and physiologically insignificant levels in lymphoblasts Thus, these results suggest that it is unlikely that the transcriptional elements described earlier are involved in the allelic imbalance.
These results suggest that the post-transcriptional regulation of CYP3A4 gene expression is likely to be similar for both alleles. As a majority of the differentially expressed genes have a virtually identical sequence in their mRNAs 46 , transcriptional initiation by cis -acting components is one of the most important controls in regulating the variation in allelic gene expression. The deviation of allelic variation varied among samples Fig. One example is tricyclic antidepressant use:.
Modifying pharmacotherapy for patients who have CYP2D6 or CYP2C19 genomic variants that affect drug efficacy and safety could potentially improve clinical outcomes and reduce the failure rate of initial treatment. Additional guidelines can be found at www.
The US Food and Drug Administration FDA defines a Serious Adverse Drug Reaction SADR as any undesirable or unpredicted medication event that results in death, a life-threatening condition, hospitalization, disability, or congenital anomaly, or that requires intervention to prevent one of these outcomes.
Around classes of drugs carry Black Box Warnings. Genetic testing is advised before prescribing many drugs with Black Box Warnings. For example, in , the FDA announced that patients taking the cardiovascular drug clopidogrel Plavix , who have a specific variation of the CYP2C19 gene, are less likely to respond to the drug, which may place them at continued risk for heart attack and stroke. Integrating pharmacogenomics into clinical care can enable the practitioner to individualize treatment and avoid potential life-threatening adverse events.
Pharmacogenomics can direct the use of FDA-approved drugs which have pharmacogenomics guidance within their monograph. As the utilization of pharmacogenomic testing increases there will be fewer drug reactions, less non-compliance due to side effects and lower healthcare costs.
Over FDA-approved drugs across multiple therapeutic areas have biomarker information in their labeling, encouraging individualization of dosing. Normal variation in human DNA due to substitution of single nucleotide polymorphisms SNPs — a variation in one base of DNA — occur at over 10 million sites within human chromosomes.
Many of these result in a non-functional enzyme or potentially one that has increased activity. Drug Toxicity Fills Emergency Rooms. Genetics And Drug Interactions.
Pharmacogenomic Testing Saves Money. Advice from the Clinical Pharmacogenetic Implementation Consortium. One example is tricyclic antidepressant use:
Genetic and Epigenetic Factors Affecting Cytochrome P450 Phenotype and Their Clinical Relevance
The liver and small intestine have the highest CYP3A4 activity. of the population limits its contribution to overall CYP3A4 variability. Another identified polymorphism is CYP3A4*1B which occurs at a frequency of 2–9% that the population variability is not due to genetic polymorphism of the enzyme itself. Drugs can increase or decrease the activity of one or more CYP enzymes, which alters By far the greatest factor is genetic predisposition due to polymorphic of a second drug taken at the same time by affecting its CYP clearance protein, Activation/inhibition of CYP proteins by a drug need to be determined in order to . Alterations in the activity or expression of this enzyme may account for a major part in liver and gut and to their collective large substrate spectrum (1). . To determine whether the two alleles of the human CYP3A4 gene are.