J Mol Med DOI 10.1007/s00109-011-0818-3
ORIGINAL ARTICLE
Differentially expressed genes in human peripheral blood as potential markers for statin response Hong-Hee Won & Suk Ran Kim & Oh Young Bang & Sang-Chol Lee & Wooseong Huh & Jae-Wook Ko & Hyung-Gun Kim & Howard L. McLeod & Thomas M. O’Connell & Jong-Won Kim & Soo-Youn Lee
Received: 30 June 2011 / Revised: 25 August 2011 / Accepted: 12 September 2011 # Springer-Verlag 2011
Abstract There is a considerable inter-individual variation in response to statin therapy and one third of patients do not meet their treatment goals. We aimed to identify differentially expressed genes that might be involved in the effects of statin treatment and to suggest potential markers to guide statin therapy. Forty-six healthy Korean subjects received atorvastatin; their whole-genome expression profiles in peripheral blood were analyzed before and after atorvastatin administration in relation with changes in lipid profiles. The expression patterns of the differentially expressed genes
were also compared with the data of familial hypercholesterolemia (FH) patients and controls. Pairwise comparison analyses revealed differentially expressed genes involved in diverse biological processes and molecular functions related with immune responses. Atorvastain mainly affected antigen binding, immune or inflammatory response including interleukin pathways. Similar expression patterns of the genes were observed in patients with FH and controls. The Charcol–Leyden crystal (CLC), CCR2, CX3CR1, LRRN3, FOS, LDLR, HLA-DRB1, ERMN, and TCN1 genes were
Electronic supplementary material The online version of this article (doi:10.1007/s00109-011-0818-3) contains supplementary material, which is available to authorized users. H.-H. Won Samsung Biomedical Research Institute, Samsung Medical Center, Seoul 135-710, South Korea H.-H. Won Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, South Korea S. R. Kim : J.-W. Kim : S.-Y. Lee (*) Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Gangnam-gu, Seoul 135-710, South Korea e-mail:
[email protected] O. Y. Bang Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, South Korea S.-C. Lee : W. Huh Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, South Korea
W. Huh : J.-W. Ko : S.-Y. Lee Department of Clinical Pharmacology and Therapeutics, Samsung Medical Center, Seoul 135-710, South Korea
H.-G. Kim Department of Pharmacology, College of Medicine, Dankook University, Chonan 330-715, South Korea
H. L. McLeod UNC Institute for Pharmacogenomics and Individualized Therapy, University of North Carolina, Chapel Hill, NC 27599, USA
T. M. O’Connell Division of Pharmacotherapy and Experimental Therapeutics, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
J Mol Med
significantly associated with cholesterol levels or statin response. Interestingly, the CLC gene, which was significantly altered by atorvastatin administration and differentially expressed between FH patients and controls, showed much bigger change in high-responsive group than in lowresponsive group. We identified differentially expressed genes that might be involved in mechanisms underlying the known pleiotropic effects of atorvastatin, baseline cholesterol levels, and drug response. Our findings suggest CLC as a new candidate marker for statin response, and further validation is needed. Keywords Cholesterol . Statin . Pharmacogenomics . Gene array analysis . Gene expression
Introduction Statins are widely used drugs for lowering cholesterol levels by inhibiting 3-hydroxy-3-methylglutaryl-coenzyme A reductase [1], a rate-limiting enzyme involved in cholesterol synthesis [2]. In addition to cholesterol reduction, there are additional pleiotropic effects associated with statins [3] including anti-inflammatory and immunomodulatory [4–6], antithrombotic [7], and vascular effects [8], which suggest the potential for many complex genetic interactions to be involved in statin reactions. Recent studies revealed that antiarrhythmic effects of atorvastatin treatment was linked with modulation of myocardial intercellular coupling protein connexin-43 [9] and that simvastatin inhibited cardiac hypertrophy and fibrosis, and myocardial inflammation by increasing peroxisome proliferator-activated receptor [10, 11]. Therefore, a global view of the change in gene expression patterns caused by treatment with statins is important for identifying the unknown genes involved in the underlying biological mechanisms associated with the effects of statins. A considerable inter-individual variation in response to statin therapy has led to numerous studies to identify the contribution of genetic factors on statin response. Previous candidate gene approaches have mainly targeted a limited number of genes involved in cholesterol efflux or cholesterol homeostasis pathways [12, 13]. On the other hand, chip analyses of gene expression showed differentially expressed genes that were correlated with the various effects of statins. For example, atorvastatin has been shown to affect the expression of several genes involved in different processes of hemostasis, inflammation, apoptosis, and atherosclerosis [14]. In addition, genes encoding the inflammatory response proteins, such as orosomucoid and the interleukin 18 receptor, were moderately downregulated by rosuvastatin [15]. Furthermore, the complement and coagulation cascades, hematopoietic cell lineage, and
the arachidonic acid metabolism pathways were noted to be associated with statins on gene expression and pathway analyses [16]. Nevertheless, further studies are needed to identify and confirm meaningful pharmacogenomic predictors. In this study, 87 gene expression profiles on peripheral blood samples from 46 subjects before and after atorvastatin administration were analyzed. The goals of our study were to determine: first, the effect of atorvastatin exposure on gene expression; second, the difference in gene expression according to the baseline low-density lipoprotein (LDL) cholesterol level; and third, individual genetic differences in lowering LDL cholesterol. Gene ontology (GO) enrichment analysis revealed a variety of biological processes and molecular functions associated with differentially expressed genes, based on the pre- and posttreatment evaluations. The significant genes identified might be related not only to the mechanism associated with reducing LDL cholesterol but also the additional diverse effects of atorvastatin.
Materials and methods Subjects Forty-six healthy Korean male volunteers (aged from 20 to 27) were enrolled for the pre-atorvastatin and postatorvastatin administration comparisons within the same individual. Five unpaired samples (five subjects) and 41 paired samples (pre-administration and post-administration samples of 41 subjects) were available. The unpaired samples included four pre-administration and one postadministration. The subjects received a single 80-mg oral dose of atorvastatin calcium (Lipitor, Pfizer, South Korea). RNA samples were obtained before administration of atorvastatin and at 48 h after administration. The protocol was approved by the Institutional Review Board of Dankook University and Samsung Medical Center, Seoul, Korea, and informed consent was obtained from the participants. Pharmacodynamic measurements The total cholesterol (TC), high-density lipoprotein (HDL) cholesterol, LDL cholesterol, and triglyceride (TG) were measured with the Hitachi 7600–110 chemistry analyzer (Hitachi, Tokyo, Japan). Plasma lipid levels were measured at 0, 24, and 48 h after atorvastatin administration. Gene expression analysis Total RNA was isolated from peripheral blood samples using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA).
J Mol Med
The samples were amplified and biotinylated sensestranded DNA targets were prepared from the total RNA (200 ng) from each sample using the Affymetrix GeneChip® Whole Transcript Sense Target Labeling Assay (Affymetrix, Santa Clara, CA, USA). They were then hybridized to the Affymetrix GeneChip® Human Gene 1.0 Sense Target microarrays, which offer whole-transcript coverage of 28,869 genes. The arrays were scanned with the Affymetrix GeneChip® Scanner 3000 7G and the images obtained were analyzed using the Affymetrix Expression Console version 1.1.
for five patients with familial hypercholesterolemia (FH) and five controls (age, gender, body mass index, and smoking matched) were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih. gov/geo/query/acc.cgi?acc=GSE13985). The expression patterns of the differentially expressed genes were compared among the subjects of this study with the same genes from the FH data.
Statistical analysis
The differentially expressed genes after atorvastatin treatment
The GeneSpring GX 11.0 software (Agilent, Santa Clara, CA, USA) and the statistical analysis software R 2.9.1 were used for the analyses. Differences in the plasma lipid levels due to atorvastatin administration were statistically evaluated using the Mann–Whitney paired test. Statistical analyses included evaluation of differentially expressed genes between: (1) pre-administration and postadministration of all the 41 paired samples (drug-exposure effects), (2) the 10 samples with high LDL cholesterol levels but in normal range and 10 samples with low LDL cholesterol levels pre-administration (genetic difference between baseline high LDL and low LDL cholesterol groups), and (3) the group with high LDL decrease and the group with low decrease post-administration (variability in drug response). The gene expression data were normalized using the Quantile algorithm and summarized by the RMA algorithm [17]. The 17,605 probes with normalized expression standard deviation at ≥0.04 were included in the analysis. To select the differentially expressed genes, the Mann–Whitney paired or unpaired test was used. p Values were corrected with the Benjamini–Hochberg false discovery rate. Using a volcano plot in GeneSpring GX, different significant gene sets were selected according to the absolute fold change (FC) cutoff (>1.2) and corrected p value (
