Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models.
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MOTIVATION: The information model chosen to store biological data affects the types of queries possible, database performance, and difficulty in updating that information model. Genetic sequence data for pharmacogenetics studies can be complex, and the best information model to use may change over time. As experimental and analytical methods change, and as biological knowledge advances, the data storage requirements and types of queries needed may also change. RESULTS: We developed a model for genetic sequence and polymorphism data, and used XML Schema to specify the elements and attributes required for this model. We implemented this model as an ontology in a frame-based representation and as a relational model in a database system. We collected genetic data from two pharmacogenetics resequencing studies, and formulated queries useful for analysing these data. We compared the ontology and relational models in terms of query complexity, performance, and difficulty in changing the information model. Our results demonstrate benefits of evolving the schema for storing pharmacogenetics data: ontologies perform well in early design stages as the information model changes rapidly and simplify query formulation, while relational models offer improved query speed once the information model and types of queries needed stabilize. (+info)
Monoamine transporter gene structure and polymorphisms in relation to psychiatric and other complex disorders.
(50/1551)
The norepinephrine, dopamine and serotonin transporters (NET, DAT and SERT, respectively), limit cellular signaling by recapturing released neurotransmitter, and serve as targets for antidepressants and drugs of abuse, emphasizing the integral role these molecules play in neurotransmission and pathology. This has compelled researchers to search for polymorphisms in monoamine (MA) transporter genes. Studies support linkage and association of MA transporter genetic variation in psychiatric and other complex disorders. Understanding the contribution of MA transporter polymorphisms to human behavior, disease susceptibility and response to pharmacotherapies will involve further progress in linkage and association that will be aided by both definition of highly selective phenotypes and utilization of a large number of polymorphic markers. The relationship of polymorphisms to alterations in transport capacity, likely a complex interaction, involving genetic background, disease state, and medication, will elucidate the means by which MA transporter genetic variability contributes to our individuality. (+info)
Genomic studies are producing large databases of molecular information on cancers and other cell and tissue types. Hence, we have the opportunity to link these accumulating data to the drug discovery processes. Our previous efforts at 'information-intensive' molecular pharmacology have focused on the relationship between patterns of gene expression and patterns of drug activity. In the present study, we take the process a step further-relating gene expression patterns, not just to the drugs as entities, but to approximately 27,000 substructures and other chemical features within the drugs. This coupling of genomic information with structure-based data mining can be used to identify classes of compounds for which detailed experimental structure-activity studies may be fruitful. Using a systematic substructure analysis coupled with statistical correlations of compound activity with differential gene expression, we have identified two subclasses of quinones whose patterns of activity in the National Cancer Institute's 60-cell line screening panel (NCI-60) correlate strongly with the expression patterns of particular genes: (i) The growth inhibitory patterns of an electron-withdrawing subclass of benzodithiophenedione-containing compounds over the NCI-60 are highly correlated with the expression patterns of Rab7 and other melanoma-specific genes; (ii) the inhibitory patterns of indolonaphthoquinone-containing compounds are highly correlated with the expression patterns of the hematopoietic lineage-specific gene HS1 and other leukemia genes. As illustrated by these proof-of-principle examples, we introduce here a set of conceptual tools and fluent computational methods for projecting directly from gene expression patterns to drug substructures and vice versa. The analysis is presented in terms of the NCI-60 cell lines and microarray-based gene expression patterns, but the concept and methods are broadly applicable to other large-scale pharmacogenomic database sets as well. The approach (SAT for Structure-Activity-Target) provides a systematic way to mine databases for the design of further structure-activity studies, particularly to aid in target and lead identification. (+info)
Pharmacogenetics and the future of medical practice.
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Pharmacogenetic approaches are widely expected to bring about a "revolution" in medicine. While the application of molecular genetic approaches to disease research will provide us with new opportunities for progressively more targeted and, hopefully, more effective treatments, these developments will be evolutionary in nature and will, for their realization, still require the painstaking process that discovering and developing a new drug entails. It is also quintessential for the realization of these promises that we support a more rational understanding and more realistic expectations in the public at large through dialogue and information. (+info)
Ranking genes with respect to differential expression.
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BACKGROUND: In the pharmaceutical industry and in academia substantial efforts are made to make the best use of the promising microarray technology. The data generated by microarrays are more complex than most other biological data attracting much attention at this point. A method for finding an optimal test statistic with which to rank genes with respect to differential expression is outlined and tested. At the heart of the method lies an estimate of the false negative and false positive rates. Both investing in false positives and missing true positives lead to a waste of resources. The procedure sets out to minimise these errors. For calculation of the false positive and negative rates a simulation procedure is invoked. RESULTS: The method outperforms commonly used alternatives when applied to simulated data modelled after real cDNA array data as well as when applied to real oligonucleotide array data. In both cases the method comes out as the over-all winner. The simulated data are analysed both exponentiated and on the original scale, thus providing evidence of the ability to cope with normal and lognormal distributions. In the case of the real life data it is shown that the proposed method will tend to push the differentially expressed genes higher up on a test statistic based ranking list than the competitors. CONCLUSIONS: The approach of making use of information concerning both the false positive and false negative rates in the inference adds a useful tool to the toolbox available to scientists in functional genomics. (+info)
Pharmacogenomics in schizophrenia: the quest for individualized therapy.
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There is strong evidence to suggest that genetic variation plays an important role in inter-individual differences in medication response and toxicity. The rapidly evolving disciplines of pharmacogenetics and pharmacogenomics seek to uncover this genetic variation in order to predict treatment outcomes. The goal is to be able to select the drugs with the greatest likelihood of benefit and the least likelihood of harm in individual patients, based on their genetic make-up-individualized therapy. Pharmacogenomic studies utilize genomic technologies to identify chromosomal areas of interest and novel putative drug targets, while pharmacogenetic strategies rely on studying sequence variations in candidate genes suspected of affecting drug response or toxicity. The candidate gene variants that affect function of the gene or its protein product have the highest priority for investigation. This review will provide demonstrative examples of functional candidate gene variants studied in a variety of antipsychotic response phenotypes in the treatment of schizophrenia. Serotonin and dopamine receptor gene variants in clozapine response will be examined, and in the process the need for sub-phenotypes will be pointed out. Our recent pharmacogenetic studies of the subphenotype of neurocognitive functioning following clozapine treatment and the dopamine D(1) receptor gene (DRD1) will be presented, highlighting our novel neuroimaging data via [(18)F]fluoro-2-deoxy-D-glucose (FDG) metabolism position emission tomography (PET) that demonstrates hypofunctioning of several brain regions in patients with specific dopamine D(1) genotype. Preliminary candidate gene studies investigating the side-effect of clozapine-induced weight gain are also presented. The antipsychotic adverse reaction of tardive dyskinesia and its association with the dopamine D(3) receptor will be critically examined, as well as the added influence of antipsychotic metabolism via the cytochrome P450 1A2 gene (CYP1A2 ). Results that delineate the putative gene-gene interaction between DRD3 and CYP1A2 are also presented. We have also utilized FDG-PET subphenotyping to demonstrate increased brain region activity in patients who have the dopamine D(3) genotype that confers increased risk for antipsychotic induced tardive dyskinesia. The merits and weaknesses of neuroimaging technologies as applied to pharmacogenetic analyses are discussed. To the extent that the above data become more widely verified and replicated, the field of psychiatry will move closer to clinically meaningful tests that will be useful in deciding the best drug for each individual patient. (+info)
Pharmacogenomic biomarkers.
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Pharmacogenomic biomarkers hold great promise for the future of medicine and have been touted as a means to personalize prescriptions. Genetic biomarkers for disease susceptibility including both Mendelian and complex disease promise to result in improved understanding of the pathophysiology of disease, identification of new potential therapeutic targets, and improved molecular classification of disease. However essential to fulfilling the promise of individualized therapeutic intervention is the identification of drug activity biomarkers that stratify individuals based on likely response to a particular therapeutic, both positive response, efficacy, and negative response, development of side effect or toxicity. Prior to the widespread clinical application of a genetic biomarker multiple scientific studies must be completed to identify the genetic variants and delineate their functional significance in the pathophysiology of a carefully defined phenotype. The applicability of the genetic biomarker in the human population must then be verified through both retrospective studies utilizing stored or clinical trial samples, and through clinical trials prospectively stratifying patients based on the biomarker. The risk conferred by the polymorphism and the applicability in the general population must be clearly understood. Thus, the development and widespread application of a pharmacogenomic biomarker is an involved process and for most disease states we are just at the beginning of the journey towards individualized therapy and improved clinical outcome. (+info)
Pharmacogenetic evidence that cd36 is a key determinant of the metabolic effects of pioglitazone.
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Pioglitazone, like other thiazolidinediones, is an insulin-sensitizing agent that activates the peroxisome proliferator-activated receptor gamma and influences the expression of multiple genes involved in carbohydrate and lipid metabolism. However, it is unknown which of these many target genes play primary roles in determining the antidiabetic and hypolipidemic effects of thiazolidinediones. To specifically investigate the role of the Cd36 fatty acid transporter gene in the insulin-sensitizing actions of thiazolidinediones, we studied the metabolic effects of pioglitazone in spontaneously hypertensive rats (SHR) that harbor a deletion mutation in Cd36 in comparison to congenic and transgenic strains of SHR that express wild-type Cd36. In congenic and transgenic SHR with wild-type Cd36, administration of pioglitazone was associated with significantly lower circulating levels of fatty acids, triglycerides, and insulin as well as lower hepatic triglyceride levels and epididymal fat pad weights than in SHR harboring mutant Cd36. Additionally, insulin-stimulated glucose oxidation in isolated soleus muscle was significantly augmented in pioglitazone-fed rats with wild-type Cd36 versus those with mutant Cd36. The Cd36 genotype had no effect on pioglitazone-induced changes in blood pressure. These findings provide direct pharmacogenetic evidence that in the SHR model, Cd36 is a key determinant of the insulin-sensitizing actions of a thiazolidinedione ligand of peroxisome proliferator-activated receptor gamma. (+info)