Literature DB >> 21054686

Using the Phenogen website for 'in silico' analysis of morphine-induced analgesia: identifying candidate genes.

Paula L Hoffman1, Beth Bennett, Laura M Saba, Sanjiv V Bhave, Phyllis J Carosone-Link, Cheryl K Hornbaker, Katerina J Kechris, Robert W Williams, Boris Tabakoff.   

Abstract

The identification of genes that contribute to polygenic (complex) behavioral phenotypes is a key goal of current genetic research. One approach to this goal is to combine gene expression information with genetic information, i.e. to map chromosomal regions that regulate gene expression levels. This approach has been termed 'genetical genomics', and, when used in conjunction with the identification of genomic regions (QTLs) that regulate the complex physiological trait under investigation, provides a strong basis for candidate gene discovery. In this paper, we describe the implementation of the genetical genomic/phenotypic approach to identify candidate genes for sensitivity to the analgesic effect of morphine in BXD recombinant inbred mice. Our analysis was performed 'in silico', using an online interactive resource called PhenoGen (http://phenogen.ucdenver.edu). We describe in detail the use of this resource, which identified a set of candidate genes, some of whose products regulate the cellular localization and activity of the mu opiate receptor. The results demonstrate how PhenoGen can be used to identify a novel set of genes that can be further investigated for their potential role in pain, morphine analgesia and/or morphine tolerance.
© 2010 The Authors, Addiction Biology © 2010 Society for the Study of Addiction.

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Year:  2010        PMID: 21054686      PMCID: PMC3115429          DOI: 10.1111/j.1369-1600.2010.00254.x

Source DB:  PubMed          Journal:  Addict Biol        ISSN: 1355-6215            Impact factor:   4.280


  51 in total

1.  Crystal structure of murine CstF-77: dimeric association and implications for polyadenylation of mRNA precursors.

Authors:  Yun Bai; Thierry C Auperin; Chi-Yuan Chou; Gu-Gang Chang; James L Manley; Liang Tong
Journal:  Mol Cell       Date:  2007-03-23       Impact factor: 17.970

2.  The NCBI dbGaP database of genotypes and phenotypes.

Authors:  Matthew D Mailman; Michael Feolo; Yumi Jin; Masato Kimura; Kimberly Tryka; Rinat Bagoutdinov; Luning Hao; Anne Kiang; Justin Paschall; Lon Phan; Natalia Popova; Stephanie Pretel; Lora Ziyabari; Moira Lee; Yu Shao; Zhen Y Wang; Karl Sirotkin; Minghong Ward; Michael Kholodov; Kerry Zbicz; Jeffrey Beck; Michael Kimelman; Sergey Shevelev; Don Preuss; Eugene Yaschenko; Alan Graeff; James Ostell; Stephen T Sherry
Journal:  Nat Genet       Date:  2007-10       Impact factor: 38.330

3.  Activation of p38 mitogen-activated protein kinase in spinal microglia mediates morphine antinociceptive tolerance.

Authors:  Yu Cui; Yu Chen; Jun-Li Zhi; Rui-Xian Guo; Jian-Qiang Feng; Pei-Xi Chen
Journal:  Brain Res       Date:  2006-01-03       Impact factor: 3.252

4.  Association of ABCB1/MDR1 and OPRM1 gene polymorphisms with morphine pain relief.

Authors:  D Campa; A Gioia; A Tomei; P Poli; R Barale
Journal:  Clin Pharmacol Ther       Date:  2007-09-26       Impact factor: 6.875

5.  15d-prostaglandin J2 inhibits inflammatory hypernociception: involvement of peripheral opioid receptor.

Authors:  Marcelo H Napimoga; Guilherme R Souza; Thiago M Cunha; Luiz F Ferrari; Juliana T Clemente-Napimoga; Carlos A Parada; Waldiceu A Verri; Fernando Q Cunha; Sérgio H Ferreira
Journal:  J Pharmacol Exp Ther       Date:  2007-10-10       Impact factor: 4.030

Review 6.  The role of mu opioid receptor desensitization and endocytosis in morphine tolerance and dependence.

Authors:  Lene Martini; Jennifer L Whistler
Journal:  Curr Opin Neurobiol       Date:  2007-12-18       Impact factor: 6.627

7.  Multiple actions of spinophilin regulate mu opioid receptor function.

Authors:  Joanna J Charlton; Patrick B Allen; Kassi Psifogeorgou; Sumana Chakravarty; Ivone Gomes; Rachael L Neve; Lakshmi A Devi; Paul Greengard; Eric J Nestler; Venetia Zachariou
Journal:  Neuron       Date:  2008-04-24       Impact factor: 17.173

8.  Evidence for an important role of protein phosphatases in the mechanism of morphine tolerance.

Authors:  Bichoy H Gabra; Chris P Bailey; Eamonn Kelly; Amanda V Sanders; Graeme Henderson; Forrest L Smith; William L Dewey
Journal:  Brain Res       Date:  2007-05-21       Impact factor: 3.252

9.  The single nucleotide polymorphism A118G alters functional properties of the human mu opioid receptor.

Authors:  Thomas Kroslak; K Steven Laforge; Robert J Gianotti; Ann Ho; David A Nielsen; Mary Jeanne Kreek
Journal:  J Neurochem       Date:  2007-10       Impact factor: 5.372

10.  The PhenoGen informatics website: tools for analyses of complex traits.

Authors:  Sanjiv V Bhave; Cheryl Hornbaker; Tzu L Phang; Laura Saba; Razvan Lapadat; Katherina Kechris; Jeanette Gaydos; Daniel McGoldrick; Andrew Dolbey; Sonia Leach; Brian Soriano; Allison Ellington; Eric Ellington; Kendra Jones; Jonathan Mangion; John K Belknap; Robert W Williams; Lawrence E Hunter; Paula L Hoffman; Boris Tabakoff
Journal:  BMC Genet       Date:  2007-08-30       Impact factor: 2.797

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  10 in total

1.  A systems genetic analysis of alcohol drinking by mice, rats and men: influence of brain GABAergic transmission.

Authors:  Laura M Saba; Beth Bennett; Paula L Hoffman; Kelsey Barcomb; Takao Ishii; Katerina Kechris; Boris Tabakoff
Journal:  Neuropharmacology       Date:  2010-12-23       Impact factor: 5.250

2.  Genetics of gene expression characterizes response to selective breeding for alcohol preference.

Authors:  P L Hoffman; L M Saba; S Flink; N J Grahame; K Kechris; B Tabakoff
Journal:  Genes Brain Behav       Date:  2014-09-29       Impact factor: 3.449

Review 3.  Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology.

Authors:  M A O'Brien; B N Costin; M F Miles
Journal:  Int Rev Neurobiol       Date:  2012       Impact factor: 3.230

4.  Genetical genomic analysis of complex phenotypes using the PhenoGen website.

Authors:  Beth Bennett; Laura M Saba; Cheryl K Hornbaker; Katerina J Kechris; Paula Hoffman; Boris Tabakoff
Journal:  Behav Genet       Date:  2010-12-24       Impact factor: 2.805

5.  Hands-on workshops as an effective means of learning advanced technologies including genomics, proteomics and bioinformatics.

Authors:  Nichole Reisdorph; Robert Stearman; Katerina Kechris; Tzu Lip Phang; Richard Reisdorph; Jessica Prenni; David J Erle; Christopher Coldren; Kevin Schey; Alexey Nesvizhskii; Mark Geraci
Journal:  Genomics Proteomics Bioinformatics       Date:  2013-12-06       Impact factor: 7.691

Review 6.  Convergent functional genomics in addiction research - a translational approach to study candidate genes and gene networks.

Authors:  Rainer Spanagel
Journal:  In Silico Pharmacol       Date:  2013-12-13

7.  miRNA-regulated transcription associated with mouse strains predisposed to hypnotic effects of ethanol.

Authors:  B Vestal; P Russell; R A Radcliffe; L Bemis; L M Saba; K Kechris
Journal:  Brain Behav       Date:  2018-04-30       Impact factor: 2.708

Review 8.  Rat Genome and Model Resources.

Authors:  Mary Shimoyama; Jennifer R Smith; Elizabeth Bryda; Takashi Kuramoto; Laura Saba; Melinda Dwinell
Journal:  ILAR J       Date:  2017-07-01

9.  Predictive modeling of miRNA-mediated predisposition to alcohol-related phenotypes in mouse.

Authors:  Pratyaydipta Rudra; Wen J Shi; Pamela Russell; Brian Vestal; Boris Tabakoff; Paula Hoffman; Katerina Kechris; Laura Saba
Journal:  BMC Genomics       Date:  2018-08-29       Impact factor: 3.969

10.  Condition-adaptive fused graphical lasso (CFGL): An adaptive procedure for inferring condition-specific gene co-expression network.

Authors:  Yafei Lyu; Lingzhou Xue; Feipeng Zhang; Hillary Koch; Laura Saba; Katerina Kechris; Qunhua Li
Journal:  PLoS Comput Biol       Date:  2018-09-21       Impact factor: 4.475

  10 in total

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