Molecular Genetics and Gene Expression in Psychiatric Disorders

描述（由申请人提供）：精神分裂症、双相情感障碍和抑郁症等主要精神疾病是由遗传和环境因素相互作用引起的复杂疾病。 这些影响在基因表达水平上交叉，并产生复杂的表型，这些表型是不同大脑区域的基因组共同影响的结果。 本研究将调查海马齿状回（DG），并使用大规模的基因表达谱模型的DG颗粒细胞几乎同质群体的基因之间的调控关系。 我们假设，5-羟色胺转运体（SERT）基因的遗传多态性导致海马齿状回（DG）基因表达的改变，从而导致精神疾病，从而破坏5-羟色胺系统。 我们将比较健康和精神病患者死后脑组织中DG的基因调控网络，以及不同SERT单倍型的精神健康携带者。 这将使我们能够验证这样一个假设，即某些SERT多态性可以通过干扰基因表达的局部模式来增加精神疾病的风险。 将使用激光捕获显微切割、aRNA扩增和与Affyssine寡核苷酸微阵列杂交，从海马中部收获齿状回颗粒细胞的同质细胞群。 基因表达谱将用于创建DG中基因调控网络的模型。 这些模型将使我们能够预测DG基因网络中的关键调控元件，这些预测将通过定量RT-PCR进行测试。 这项研究与公共卫生的相关性：主要精神疾病是慢性疾病，在人类痛苦和治疗费用和生产力损失造成的经济损失方面都带来了非常沉重的疾病负担。 先前的遗传关联研究表明，SERT的等位基因变体可以与环境因素结合影响疾病风险。 在这项研究中，我们将比较受试者组与主要精神疾病和正常对照组之间的基因表达，在基因调控网络中建立基因相互作用的模型，并确定遗传和环境影响交叉的关键调控元件。 这将增加我们对精神疾病的遗传风险因素，基因-环境相互作用的理解，并突出可能的新治疗策略。

DESCRIPTION (provided by applicant): Major psychiatric illnesses such as schizophrenia, bipolar disorder and depression are complex diseases caused by the interaction of genetic and environmental factors. These influences intersect at the level of gene expression and create complex phenotypes that are the result of collective influences by groups of genes acting in concert across different brain regions. This study will investigate the dentate gyrus (DG) of the hippocampus and use large-scale gene expression profiling to model the regulatory relationships between genes in nearly homogeneous populations of DG granule cells. We hypothesize that disruption in the serotonin system by genetic polymorphisms of the serotonin transporter (SERT) gene results in alterations in gene expression in the dentate gyrus (DG) of the hippocampus which contribute to psychiatric disorders. We will compare gene regulatory networks in the DG in postmortem brain tissue from healthy and mentally ill subjects, and in mentally healthy carriers of different SERT haplotypes. This will allow us to test the hypothesis that certain SERT polymorphisms can raise the risk for mental illness by interfering with local patterns of gene expression. Homogeneous cell populations of dentate gyrus granule cells from mid-hippocampus will be harvested using laser capture microdissection, aRNA amplification, and hybridization to Affymetrix oligonucleotide microarrays. Gene expression profiles will be used to create models of gene regulatory networks in the DG. These models will allow us to make predictions about key regulatory elements in the DG gene network, and these predictions will be tested by quantitative RT-PCR. Relevance of this research to public health: Major psychiatric illnesses are chronic diseases that carry a very heavy disease burden both in terms of human suffering and as financial losses due to treatment costs and lost productivity. Previous genetic association studies have shown that allelic variants of SERT can influence disease risk in combination with environmental factors. In this study we will compare gene expression between groups of subjects with major psychiatric illnesses and normal controls, model the interaction of genes in a gene regulatory network, and identify key regulatory elements where genetic and environmental influences can intersect. This will increase our understanding of genetic risk factors for mental illness, gene-environment interactions, and highlight possible new treatment strategies.