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      Evaluating genetic markers and neurobiochemical analytes for fluoxetine response using a panel of mouse inbred strains

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          Abstract

          Rationale

          Identification of biomarkers that establish diagnosis or treatment response is critical to the advancement of research and management of patients with depression.

          Objective

          Our goal was to identify biomarkers that can potentially assess fluoxetine response and risk to poor treatment outcome.

          Methods

          We measured behavior, gene expression, and the levels of 36 neurobiochemical analytes across a panel of genetically diverse mouse inbred lines after chronic treatment with water or fluoxetine.

          Results

          Glyoxylase 1 (GLO1) and guanine nucleotide-binding protein 1 (GNB1) mostly account for baseline anxiety-like and depressive-like behavior, indicating a common biological link between depression and anxiety. Fluoxetine-induced biochemical alterations discriminated positive responders, while baseline neurobiochemical differences differentiated negative responders ( p < 0.006). Results show that glial fibrillary acidic protein, S100 beta protein, GLO1, and histone deacetylase 5 contributed most to fluoxetine response. These proteins are linked within a cellular growth/proliferation pathway, suggesting the involvement of cellular genesis in fluoxetine response. Furthermore, a candidate genetic locus that associates with baseline depressive-like behavior contains a gene that encodes for cellular proliferation/adhesion molecule ( Cadm1), supporting a genetic basis for the role of neuro/gliogenesis in depression.

          Conclusion

          We provided a comprehensive analysis of behavioral, neurobiochemical, and transcriptome data across 30 mouse inbred strains that has not been accomplished before. We identified biomarkers that influence fluoxetine response, which, altogether, implicate the importance of cellular genesis in fluoxetine treatment. More broadly, this approach can be used to assess a wide range of drug response phenotypes that are challenging to address in human samples.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s00213-011-2574-z) contains supplementary material, which is available to authorized users.

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          Most cited references113

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          Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

          T. Golub (1999)
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            Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

            We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.
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              Animal models of neuropsychiatric disorders.

              Modeling of human neuropsychiatric disorders in animals is extremely challenging given the subjective nature of many symptoms, the lack of biomarkers and objective diagnostic tests, and the early state of the relevant neurobiology and genetics. Nonetheless, progress in understanding pathophysiology and in treatment development would benefit greatly from improved animal models. Here we review the current state of animal models of mental illness, with a focus on schizophrenia, depression and bipolar disorder. We argue for areas of focus that might increase the likelihood of creating more useful models, at least for some disorders, and for explicit guidelines when animal models are reported.
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                Author and article information

                Contributors
                +1-617-6655463 , Mathew.Pletcher@pfizer.com
                +1-919-8435820 , timw@unc.edu
                Journal
                Psychopharmacology (Berl)
                Psychopharmacology (Berl.)
                Psychopharmacology
                Springer-Verlag (Berlin/Heidelberg )
                0033-3158
                1432-2072
                24 November 2011
                24 November 2011
                May 2012
                : 221
                : 2
                : 297-315
                Affiliations
                [1 ]Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel Hill, Genetic Medicine Building, 120 Mason Farm Road, Chapel Hill, NC 27599 USA
                [2 ]Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
                [3 ]Department of Neuroscience, The Scripps Research Institute, Florida, Jupiter, FL USA
                [4 ]Orphan and Genetic Diseases Research Unit, Pfizer Global Research and Development, 200 Cambridge Park Drive, Cambridge, MA 02140 USA
                Article
                2574
                10.1007/s00213-011-2574-z
                3337404
                22113448
                d6653c39-636a-4933-960e-8bc6648f1f7a
                © The Author(s) 2011
                History
                : 22 July 2011
                : 3 November 2011
                Categories
                Original Investigation
                Custom metadata
                © Springer-Verlag 2012

                Pharmacology & Pharmaceutical medicine
                biomedicine,ssri,pharmacology/toxicology,psychiatry,genetic,fluoxetine,biochemical,neurosciences,depression,biomarker,anxiety

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