Inter-tissue Networks Between the Basal Forebrain, Hippocampus, and Prefrontal Cortex in a Model for Depression Caused by Disturbed Sleep

前额叶皮质 海马体 神经科学 睡眠(系统调用) 基底前脑 前脑 生物 5-羟色胺能 心理学 血清素 胆碱能的 中枢神经系统 遗传学 认知 受体 计算机科学 操作系统
作者
Markus Lagus,Natalia Gass,Juha Saharinen,Sergey A. Savelyev,Tarja Porkka‐Heiskanen,Tiina Paunio
出处
期刊:Journal of Neurogenetics [Taylor & Francis]
卷期号:26 (3-4): 397-412 被引量:5
标识
DOI:10.3109/01677063.2012.694932
摘要

Disturbances in sleep are encountered in the majority of patients with depressive disorder. To elucidate the molecular mechanisms behind this relationship, we examined gene expression changes in a rodent model for disturbed sleep and depression. The animals were treated with daily injections of clomipramine to affect their sleep during early infancy. This early interference with sleep is known to induce depression-like behavior in adult animals. After 2 weeks of treatment, the change in gene expression was examined using the Affymetrix Rat 230.2 chip. We studied the gene expression in the basal forebrain, hippocampus, and frontal cortex and combined the results to reveal the otherwise indissectible networks between and around the tissues. The major disrupted pathways between the three brain areas were related to synaptic transmission, regulation of translation, and ubiquitinylation. The involved pathways were within the cellular components of the axons, growth cones, melanosomes, and pigment granules. A network analysis allowing for additional interactors, in the form of chemicals or gene products, revealed a disturbed communicational network between the different brain areas. This disturbed network is centered around serotonin, Mn(II), and Rhoa. The findings elucidate inter-tissue pathways and networks in the brain that are involved in sleep and mood regulation. The findings are of uttermost interest, some are quite predictable and obvious, but some are novel or have only been proposed by rare theoretical speculations (such as the melanosome and Mn(II) involvement). Equally important as the findings are the methods described in this article. In this study, we present two novel simple ways to perform system biological analysis based on gene expression array data. We used two already existing tools in a new way, and by careful planning of the input data, managed to extrapolate intricate hidden inter-tissue networks to build a molecular picture of disease.

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