Dataset Information

Microarray Analysis of Differentially-Expressed Genes Encoding CYP450 and Phase II Drug Metabolizing Enzymes in Psoriasis and Melanoma.

ABSTRACT: Cytochrome P450 drug metabolizing enzymes are implicated in personalized medicine for two main reasons. First, inter-individual variability in CYP3A4 expression is a confounding factor during cancer treatment. Second, inhibition or induction of CYP3A4 can trigger adverse drug-drug interactions. However, inflammation can downregulate CYP3A4 and other drug metabolizing enzymes and lead to altered metabolism of drugs and essential vitamins and lipids. Little is known about effects of inflammation on expression of CYP450 genes controlling drug metabolism in the skin. Therefore, we analyzed seven published microarray datasets, and identified differentially-expressed genes in two inflammatory skin diseases (melanoma and psoriasis). We observed opposite patterns of expression of genes regulating metabolism of specific vitamins and lipids in psoriasis and melanoma samples. Thus, genes controlling the turnover of vitamin D (CYP27B1, CYP24A1), vitamin A (ALDH1A3, AKR1B10), and cholesterol (CYP7B1), were up-regulated in psoriasis, whereas melanomas showed downregulation of genes regulating turnover of vitamin A (AKR1C3), and cholesterol (CYP39A1). Genes controlling abnormal keratinocyte differentiation and epidermal barrier function (CYP4F22, SULT2B1) were up-regulated in psoriasis. The up-regulated CYP24A1, CYP4F22, SULT2B1, and CYP7B1 genes are potential drug targets in psoriatic skin. Both disease samples showed diminished drug metabolizing capacity due to downregulation of the CYP1B1 and CYP3A5 genes. However, melanomas showed greater loss of drug metabolizing capacity due to downregulation of the CYP3A4 gene.

SUBMITTER: Sumantran VN

PROVIDER: S-EPMC4810080 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Microarray Analysis of Differentially-Expressed Genes Encoding CYP450 and Phase II Drug Metabolizing Enzymes in Psoriasis and Melanoma.

Sumantran Venil N VN Mishra Pratik P Bera Rakesh R Sudhakar Natarajan N

Pharmaceutics 20160217 1

Cytochrome P450 drug metabolizing enzymes are implicated in personalized medicine for two main reasons. First, inter-individual variability in CYP3A4 expression is a confounding factor during cancer treatment. Second, inhibition or induction of CYP3A4 can trigger adverse drug-drug interactions. However, inflammation can downregulate CYP3A4 and other drug metabolizing enzymes and lead to altered metabolism of drugs and essential vitamins and lipids. Little is known about effects of inflammation o ...[more]

PMID: 26901218

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Project description:BackgroundMicroarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).ResultsWe hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile.ConclusionsFactors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

Project description:BACKGROUND: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework. RESULTS: The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets. CONCLUSION: This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.

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Dataset Information

Microarray Analysis of Differentially-Expressed Genes Encoding CYP450 and Phase II Drug Metabolizing Enzymes in Psoriasis and Melanoma.

Publications

Microarray Analysis of Differentially-Expressed Genes Encoding CYP450 and Phase II Drug Metabolizing Enzymes in Psoriasis and Melanoma.

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