Effects of typical and atypical antipsychotic drugs on gene expression profiles in the liver of schizophrenia subjects

Choi, Kwang Ho; Higgs, Brandon W.; Weis, Serge; Song, Jian; Llenos, Ida C.; Dulay, Jeannette R.; Yolken, Robert H.; Webster, Maree J.

doi:10.1186/1471-244x-9-57

Cited by 39 publications

(38 citation statements)

References 64 publications

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“…RNA was purified using the PureLink Total RNA Purification System (Invitrogen, Carlsbad, California), and complementary DNA was synthesized using reverse-transcriptase polymerase chain reaction (PCR) with oligo dT primers as previously described (46). Predesigned and validated QuantiTect SYBR primers (Qiagen) were used for the quantitative polymerase chain reaction (qPCR): HBS1L (QT00052626, NM_006620), HLA-DPB1 (QT00079338, NM_002121), PCLO (QT01843730, NM_014510), and POLR1D (QT01339856, NM_152705).…”

Section: Methodsmentioning

confidence: 99%

Gene Expression and Genetic Variation Data Implicate PCLO in Bipolar Disorder

Choi

Higgs²,

Wendland

et al. 2011

Biological Psychiatry

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Background Genetic variation may contribute to differential gene expression in the brain of individuals with psychiatric disorders. To test this hypothesis, we identified genes that were differentially expressed in individuals with bipolar disorder, along with nearby single nucleotide polymorphisms (SNPs) that were associated with expression of the same genes. We then tested these SNPs for association with bipolar disorder in large case-control samples. Methods We used the Stanley Genomics Database to extract gene expression and SNP microarray data from individuals with bipolar disorder (n = 40) and unaffected controls (n = 43). We identified 367 genes that were differentially expressed in the prefrontal cortex of cases vs. controls (fold change > 1.3 and FDR q-value < .05) and 45 nearby SNPs that were associated with expression of those same genes (FDR q-value < .05). We tested these SNPs for association with bipolar disorder in a meta-analysis of genome-wide association studies (GWAS) including 4,936 cases and 6,654 healthy controls. Results We identified 45 SNPs that were associated with expression of differentially expressed genes, including HBS1L (15 SNPs), HLA-DPB1 (15 SNPs), AMFR (8 SNPs), PCLO (2 SNPs) and WDR41 (2 SNPs). Of these, one SNP (rs13438494), in an intron of the piccolo (PCLO) gene, was significantly associated with bipolar disorder (FDR adjusted p < .05) in the meta-analysis of GWAS. Conclusions These results support the previous findings implicating PCLO in mood disorders and demonstrate the utility of combining gene expression and genetic variation data to improve our understanding of the genetic contribution to bipolar disorder.

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Section: Methodsmentioning

confidence: 99%

Gene Expression and Genetic Variation Data Implicate PCLO in Bipolar Disorder

Choi

Higgs²,

Wendland

et al. 2011

Biological Psychiatry

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“…Analgesics are being tested that inactivate the mGluR5 receptor, which is coupled with a Gq protein. Via phospholipase C activation and inositoltriphosphate/diacylglycerol signalling, activation of this receptor leads to liberation of calcium from the endoplasmic reticulum into the intracellular space and to activation of protein kinase C. GRM5 variants were associated with schizophrenia (Devon et al, 2001;Choi et al, 2009) …”

Section: G Protein Coupled Receptorsmentioning

confidence: 99%

Pharmacogenetics of new analgesics

Lötsch

Geißlinger

2011

British J Pharmacology

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Patient phenotypes in pharmacological pain treatment varies between individuals, which could be partly assigned to their genotypes regarding the targets of classical analgesics (OPRM1, PTGS2) or associated signalling pathways (KCNJ6). Translational and genetic research have identified new targets, for which new analgesics are being developed. This addresses voltage-gated sodium, calcium and potassium channels, for which SCN9A, CACNA1B, KCNQ2 and KCNQ3, respectively, are primary gene candidates because they code for the subunits of the respective channels targeted by analgesics currently in clinical development. Mutations in voltage gated transient receptor potential (TRPV) channels are known from genetic pain research and may modulate the effects of analgesics under development targeting TRPV1 or TRPV3. To this add ligand-gated ion channels including nicotinic acetylcholine receptors, ionotropic glutamate-gated receptors and ATP-gated purinergic P2X receptors with most important subunits coded by CHRNA4, GRIN2B and P2RX7. Among G protein coupled receptors, d-opioid receptors (coded by OPRD1), cannabinoid receptors (CNR1 and CNR2), metabotropic glutamate receptors (mGluR5 coded by GRM5), bradykinin B1 (BDKRB1) and 5-HT1A (HTR1A) receptors are targeted by new analgesic substances. Finally, nerve growth factor (NGFB), its tyrosine kinase receptor (NTRK1) and the fatty acid amide hydrolase (FAAH) have become targets of interest. For most of these genes, functional variants have been associated with neuro-psychiatric disorders and not yet with analgesia. However, research on the genetic modulation of pain has already identified variants in these genes, relative to pain, which may facilitate the pharmacogenetic assessments of new analgesics. The increased number of candidate pharmacogenetic modulators of analgesic actions may open opportunities for the broader clinical implementation of genotyping information. AbbreviationsAMPA, a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate; CB1, cannabinoid receptor 1; CB2, cannabinoid receptor 2; COX-2, prostaglandin endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase); FAAH, fatty acid amide hydrolase; Il-1, interleukin 1; MAO, monoamine oxidase; mGluR5, metabotropic glutamate receptor 5; nAChR, neuronal nicotinic acetylcholine receptors; Nav, voltage-gated sodium channels; NGF, nerve growth factor; NMDA, N-methyl-D-aspartic acid; NR2B, NR2B Subunit of the NMDA receptor; P2X7, purinergic receptor P2X, ligand-gated ion channel, 7; TRPV1, transient receptor potential cation channel, subfamily V, member 1; TRPV3, transient receptor potential cation channel, subfamily V, member 3

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“…Comparison between FGA and SGA further identified genes associated with lipid metabolism and mitochondrial function. Analyses on individual antipsychotics identified a set of genes (151 transcripts) that are differentially regulated by four antipsychotics, particularly by phenothiazines, in the liver of SCZ patients 98 …”

Section: Pharmacodynamics Of Antipsychoticsmentioning

confidence: 99%

Pharmacogenomics of antipsychotics efficacy for schizophrenia

Cacabelos

Hashimoto

Takeda

2011

Psychiatry Clin Neurosci

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Central nervous system disorders are the third greatest health problem in developed countries, and schizophrenia represents some of the most disabling ailments in young individuals. There is an abuse and/or misuse of antipsychotics, and recent advances in pharmacogenomics pose new challenges for the clinical management of this complex disorder. Schizophrenia is a multi-factorial/polygenic complex disorder in which hundreds of different genes are potentially involved, leading to the phenotypic expression of the disease in conjunction with epigenetic and environmental phenomena. Consequently, structural and functional genomic changes induce proteomic and metabolomic defects associated with the disease phenotype.Disease-related genomic profiles and genetic variants in genes involved in drug metabolism are responsible for drug efficacy and safety. About 20% of Caucasians are defective in CYP2D6 enzymes, which participate in the metabolism of 25-30% of central nervous system drugs. Approximately 40% of antipsychotics are substrates of CYP2D6 enzymes, 23% are substrates of CYP3A4, and 18% are substrates of CYP1A2. In order to achieve a mature discipline of pharmacogenomics of schizophrenia it would be effective to accelerate: (i) the education of physicians and the public in the use of genomic screening in daily clinical practice; (ii) the standardization of genetic testing for major categories of drugs; (iii) the validation of pharmacogenomic procedures according to drug category and pathology; (iv) the regulation of ethical, social, and economic issues; and (v) the incorporation of pharmacogenomic procedures of drugs in development and drugs on the market in order to optimize therapeutics.

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Effects of typical and atypical antipsychotic drugs on gene expression profiles in the liver of schizophrenia subjects

Abstract: Background: Although much progress has been made on antipsychotic drug development, precise mechanisms behind the action of typical and atypical antipsychotics are poorly understood.

Cited by 39 publications

References 64 publications

Gene Expression and Genetic Variation Data Implicate PCLO in Bipolar Disorder

Gene Expression and Genetic Variation Data Implicate PCLO in Bipolar Disorder

Pharmacogenetics of new analgesics

Pharmacogenomics of antipsychotics efficacy for schizophrenia

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