Animal models of epigenetic regulation in neuropsychiatric disorders.

Epigenetics describes the phenomenon of heritable changes in gene regulation that are governed by non-Mendelian processes, primarily through biochemical modifications to chromatin structure that occur during cell development and differentiation. Numerous lines of evidence link abnormal levels of chromatin modifications (either to DNA, histones, or both) in patients with a wide variety of diseases including cancer, psychiatry, neurodegeneration, metabolic and inflammatory disorders. Drugs that target the proteins controlling chromatin modifications can modulate the expression of clusters of genes, potentially offering higher therapeutic efficacy than classical agents with single target pharmacologies that are susceptible to biochemical pathway degeneracy. Here, we summarize recent research linking epigenetic dysregulation with diseases in neurosciences, the application of relevant animal models, and the potential for small molecule modulator development to facilitate target discovery, validation and translation into clinical treatments

Neurogenetics and Pharmacology of Learning, Motivation, and Cognition

Many of the individual differences in cognition, motivation, and learning—and the disruption of these processes in neurological conditions—are influenced by genetic factors. We provide an integrative synthesis across human and animal studies, focusing on a recent spate of evidence implicating a role for genes controlling dopaminergic function in frontostriatal circuitry, including COMT, DARPP-32, DAT1, DRD2, and DRD4. These genetic effects are interpreted within theoretical frameworks developed in the context of the broader cognitive and computational neuroscience literature, constrained by data from pharmacological, neuroimaging, electrophysiological, and patient studies. In this framework, genes modulate the efficacy of particular neural computations, and effects of genetic variation are revealed by assays designed to be maximally sensitive to these computations. We discuss the merits and caveats of this approach and outline a number of novel candidate genes of interest for future study