Dysbindin and D-amino-acid-oxidase gene polymorphisms associated with positive and negative symptoms in schizophrenia
ABSTRACT Schizophrenia is a genetically complex disorder with an unknown pathophysiology. Several genes implicated in glutamate metabolism have been associated with the disorder. Recent studies of polymorphisms in the dystrobrevin-binding protein 1 gene (DTNBP1; dysbindin) and D-amino-acid-oxidase (DAO) gene, both involved in glutamate receptor function, reported associations with negative symptoms and with anxiety and depression, respectively, when measured with the Positive and Negative Syndrome Scale (PANSS).
In the present study, the suggested association between dysbindin and DAO single nucleotide polymorphisms (SNPs) and PANSS scores was analyzed in 155 Norwegian schizophrenia patients.
There was a significant association between the dysbindin SNP rs3213207 and severity of both negative symptoms and total symptom load, as well as between the DAO SNP rs2070587 and total symptom score and severity of anxiety and depression.
The present association of dysbindin SNPs with negative symptoms and DAO SNPs with anxiety and depression is a replication of earlier findings and strengthens the hypothesis of a genetic association. It further indicates involvement of glutamate abnormalities in schizophrenia pathophysiology, as suggested by previous studies, and suggests that polymorphisms may be associated with subgroups of clinical characteristics in schizophrenia.
Full-textDOI: · Available from: Ingrid Melle, Jul 26, 2015
- SourceAvailable from: C. Phillip Morris
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- "Sanacora et al., 2008). Furthermore, considering reports of dysbindin gene variation to be associated with a wide continuum of mental disorders such as schizophrenia (e.g., Straub et al., 2002; Schwab et al., 2003; van den Oord et al., 2003), schizophrenia with anxiety/depression symptoms (Wirgenes et al., 2009), bipolar disorder with or without psychotic features (Raybould et al., 2005; Pae et al., 2007b), major depression (Kim et al., 2008; but: Wray et al., 2008; Zill et al., 2004), Table 4 Association of DTNBP1 gene haplotypes with psychotic depression. "
ABSTRACT: Previous studies yielded evidence for dysbindin (DTNBP1) to impact the pathogenesis of schizophrenia on the one hand and affective disorders such as bipolar or major depressive disorder (MDD) on the other. Thus, in the present study we investigated whether DTNBP1 variation was associated with psychotic depression as a severe clinical manifestation of MDD possibly constituting an overlapping phenotype between affective disorders and schizophrenia. A sample of 243 Caucasian inpatients with MDD (SCID-I) was genotyped for 12 SNPs spanning 92% of the DTNBP1 gene region. Differences in DTNBP1 genotype distributions across diagnostic subgroups of psychotic (N = 131) vs. non-psychotic depression were estimated by Pearson Chi(2) test and logistic regression analyses adjusted for age, gender, Beck Depression Inventory (BDI) and the Global Assessment of Functioning Scale (GAF). Overall, patients with psychotic depression presented with higher BDI and lower GAF scores expressing a higher severity of the illness as compared to depressed patients without psychotic features. Four DTNBP1 SNPs, particularly rs1997679 and rs9370822, and the corresponding haplotypes, respectively, were found to be significantly associated with the risk of psychotic depression in an allele-dose fashion. In summary, the present results provide preliminary support for dysbindin (DTNBP1) gene variation, particularly SNPs rs1997679 and rs9370822, to be associated with the clinical phenotype of psychotic depression suggesting a possible neurobiological mechanism for an intermediate trait on the continuum between affective disorders and schizophrenia.Journal of Psychiatric Research 10/2010; 45(5):588-95. DOI:10.1016/j.jpsychires.2010.09.014 · 4.09 Impact Factor
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ABSTRACT: Describes an algorithm for collision avoidance between multiple mobile robots. As robots actually exchange information, algorithms are based on two hypotheses: (i) the working space is decomposed into cells and a limited number of robots is affected to each cell-thus a robot exchanges information only with robots in its vicinity and (ii) the trajectory of a robot is decomposed into sections-thus robots exchange only relevant information among themselves. A pipeline algorithm is proposed where a robot computes collisions with other robots on the section S <sub>j+1</sub> while the it moves along the section S <sub>j</sub>. These algorithms are fully integrated in a more general architecture for mobile robots. Examples are described to show the interest of this approachIntelligent Robots and Systems '91. 'Intelligence for Mechanical Systems, Proceedings IROS '91. IEEE/RSJ International Workshop on; 12/1991
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ABSTRACT: Attention deficits belong to the main cognitive symptoms of schizophrenia and come along with altered neural activity in previously described cerebral networks. Given the high heritability of schizophrenia the question arises if impaired function of these networks is modulated by susceptibility genes and detectable in healthy risk allele carriers. The present event-related fMRI study investigated the effect of the single nucleotide polymorphism (SNP) rs1018381 of the DTNBP1 (dystrobrevin-binding protein 1) gene on brain activity in 80 subjects while performing the attention network test (ANT). In this reaction time task three domains of attention are probed simultaneously: alerting, orienting and executive control of attention. Risk allele carriers showed impaired performance in the executive control condition associated with reduced neural activity in the left superior frontal gyrus [Brodmann area (BA) 9]. Risk allele carriers did not show alterations in the alerting and orienting networks. BA 9 is a key region of schizophrenia pathology and belongs to a network that has been shown previously to be involved in impaired executive control mechanisms in schizophrenia. Our results identified the impact of DTNBP1 on the development of a specific attention deficit via modulation of a left prefrontal network.Behavioral and Brain Functions 09/2010; 6:54. DOI:10.1186/1744-9081-6-54 · 2.00 Impact Factor