Global state measures of the dentate gyrus gene expression system predict antidepressant-sensitive behaviors.
Global state measures of the dentate gyrus gene expression system predict antidepressant-sensitive behaviors.
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DOI：
10.1371/journal.pone.0085136
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Alter M
中科院分区：
文献类型：
--
作者：
Samuels BA;Leonardo ED;Dranovsky A;Williams A;Wong E;Nesbitt AM;McCurdy RD;Hen R;Alter M
选择性5-羟色胺再摄取抑制剂（SSRIs），如氟西汀，是治疗抑郁症最常见的药物。然而，大约50%的抑郁症患者未能获得有效的治疗反应。了解基因表达系统如何对治疗作出反应可能对了解抗抑郁药抵抗至关重要。我们采取了一种新的方法来解决这个问题，通过证明齿状回的基因表达系统对氟西汀（FLX）（一种常用的抗抑郁药物）的反应，以一种刻板的方式涉及数千个基因表达水平的变化。用主成分分析（PCA）量化这种大规模全身反应的聚集行为，从而产生全局基因表达系统状态的单一定量测量。在氟西汀治疗的抑郁症小鼠模型中，系统状态的定量测量与抗抑郁药敏感行为水平的变异性高度相关。对同一只小鼠的背侧和腹侧齿状回样本的分析表明，尽管这些区域报告了功能差异，但系统状态在这些区域之间存在协变。当使用不同的微阵列数据处理算法时，甚至当使用完全不同的基因表达水平组进行计算时，基因表达系统状态的总测量值非常稳健并且保持不变。系统状态测量提供了一种稳健的方法来量化全局基因表达系统状态变异性并将其与行为和治疗相关联。状态变异性还表明，报告的治疗（如氟西汀）引起的基因表达水平变化的多样性可能代表了统一但嘈杂的全球基因表达系统状态水平响应的不同观点。在州一级研究基因表达系统的调控可能有助于指导传统抗抑郁治疗的新方法。
Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common form of medication treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance. We take a novel approach to this problem by demonstrating that the gene expression system of the dentate gyrus responds to fluoxetine (FLX), a commonly used antidepressant medication, in a stereotyped-manner involving changes in the expression levels of thousands of genes. The aggregate behavior of this large-scale systemic response was quantified with principal components analysis (PCA) yielding a single quantitative measure of the global gene expression system state. Quantitative measures of system state were highly correlated with variability in levels of antidepressant-sensitive behaviors in a mouse model of depression treated with fluoxetine. Analysis of dorsal and ventral dentate samples in the same mice indicated that system state co-varied across these regions despite their reported functional differences. Aggregate measures of gene expression system state were very robust and remained unchanged when different microarray data processing algorithms were used and even when completely different sets of gene expression levels were used for their calculation. System state measures provide a robust method to quantify and relate global gene expression system state variability to behavior and treatment. State variability also suggests that the diversity of reported changes in gene expression levels in response to treatments such as fluoxetine may represent different perspectives on unified but noisy global gene expression system state level responses. Studying regulation of gene expression systems at the state level may be useful in guiding new approaches to augmentation of traditional antidepressant treatments.
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影响因子：
16.2
作者：
Fanselow, Michael S.;Dong, Hong-Wei
通讯作者：
Dong, Hong-Wei
DOI：
10.1073/pnas.97.18.10101
发表时间：
2000-08-29
影响因子：
11.1
作者：
Alter, O;Brown, PO;Botstein, D
通讯作者：
Botstein, D
影响因子：
3.1
作者：
Alme MN;Wibrand K;Dagestad G;Bramham CR
通讯作者：
Bramham CR
基因共表达网络分析的几何解释。
影响因子：
4.3
作者：
Horvath, Steve;Dong, Jun
通讯作者：
Dong, Jun
影响因子：
3.7
作者：
Böck M;Ogishima S;Tanaka H;Kramer S;Kaderali L
通讯作者：
Kaderali L