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  • br Introduction Pattern separation is the ability to make

    2019-10-09


    Introduction Pattern separation is the ability to make distinct representations from highly overlapping information, a process which is important for memory encoding (Clelland et al., U 73343 2009). For correct pattern separation, old information needs to be retrieved and compared to new information. If the information is similar, but not exactly the same, it needs to be stored separately (Kirwan & Stark, 2007). The concept of pattern separation was initially described from computational-neuronal models (Marr, Willshaw, & McNaughton, 1991) and only recently it has been shown to be an integral part of normal neuronal functioning (Clelland et al., 2009). In turn, the study of pattern separation gained more interest due to evidence indicating that pattern separation is one of the underlying cognitive processes that are impaired in neurodegenerative and psychiatric disorders, like anxiety (Kheirbek, Klemenhagen, Sahay, & Hen, 2012) and schizophrenia (Tamminga, Stan, & Wagner, 2010). It is suggested that impairments in pattern separation is an endophenotype of these disorders and being expressed as the inability of individuals to distinguish between similar daily cues, resulting in panic attacks and psychotic behavior (Das et al., 2014, Kheirbek et al., 2012, Mineka and Zinbarg, 2006, Tamminga et al., 2010). Taking this into account, pattern separation could be a promising test for future diagnosis and treatment of mental disorders. Pattern separation is a process that takes place in the hippocampus and more specifically in the dentate gyrus (DG) and Cornu Ammonis region 3 (CA3) (Morris, Churchwell, Kesner, & Gilbert, 2012). The main source of input in the hippocampus is derived from the enthorinal cortex (EC) that mainly projects to the granule U 73343 in the DG. From the DG the information is sent to the CA3, and from CA3 to Cornu Ammonis region 1 (CA1) (Myers & Scharfman, 2011). The process of forming distinct representations out of overlapping stimuli can only be accomplished because the DG granule cells have small place fields and can therefore disperse the input from the EC. Subsequently, the information is relayed to the CA3 region via the mossy fiber synapses (Kheirbek et al., 2012). Animal studies showed that pattern separation is based on two types of neuronal processing between the DG and CA3 (Leutgeb, 2008). The encoding of small differences at a given location takes place at the granule cells of DG. The CA3 region adds another level of pattern separation when the differences in a location are more pronounced, by activating different neuronal subpopulations (Leutgeb, Leutgeb, Moser, & Moser, 2007). Learning and memory involving pattern separation requires changes in synaptic plasticity and associated neuronal gene expression (Feng et al., 2010), the latter of which has been shown to depend on epigenetic alterations (Feng, Fouse, & Fan, 2007). As such, the orchestrated action of DNA methylation and demethylation could define transcription of genes related to mnemonic processes. DNA methylation is controlled by DNA methyltransferases (DNMTs), which catalyze the transfer of a methyl-group at CpG sites of DNA. Accordingly, a high degree of DNA methylation, especially at the promoter region of a gene, is often associated with reduced gene expression by preventing transcription factor binding (Watt & Molloy, 1988) or by recruitment of methyl-CpG binding domain (MBD) proteins. These proteins form a complex with histone deacetylases (HDACs), promoting histone tail deacetylation, which subsequently leads to a transformation of chromatin into a condensed, repressive state (Guoping & Hutnick, 2005). However, it remains to be elucidated whether such epigenetic mechanisms may directly affect pattern separation. In the present study, we aimed to investigate the effect of the non-specific DNMT inhibitor RG108 on pattern separation performance and therefore provide first evidence regarding the influence of an epigenetic mechanism on pattern separation memory. Next, in order to get more insight into the effect of DNMT inhibition, we analyzed the hippocampal expression of relevant target genes for plasticity and memory function after treatment with RG108. The genes of interest were histone deacetylase 2 (Hdac2), brain-derived neurotrophic factor 1, 4 and 9 (Bdnf1, 4 and 9) and glutamate ionotropic receptor AMPA type subunit 1 (Gria1). We chose to determine the expression of Hdac2 because -HDAC2 works in close concert with DNMT’s, while the remaining genes were selected due to their association with memory function. Our results indicate that administration of RG108 increases the expression of Bdnf1, while the expression levels of the other genes remained unaltered. Finally, we opted to get a first indication whether the increase in Bdnf1 expression is accompanied with differences in the methylation pattern, by analyzing the methylation levels of 14 CpG loci at its promoter region.