Critical factors in gene expression in postmortem human brain: Focus on studies in schizophrenia
Critical factors in gene expression in postmortem human brain: Focus on studies in schizophrenia
复制标题
DOI：
10.1016/j.biopsych.2006.06.019
复制
发表时间：
2006-09-15
影响因子：
10.6
通讯作者：
Kleinman, Joel E.
中科院分区：
文献类型：
--
作者：
Lipska, Barbara K.;Deep-Soboslay, Amy;Kleinman, Joel E.
背景：对人类死后大脑的研究对于研究神经精神疾病的潜在致病分子机制具有重要意义。然而，它们被死前和死后的因素所混淆。本研究的目的是确定变异的来源，以便更好地设计基因表达研究和提高基因表达数据的可靠性。我们通过对大量（N = 143）来自精神分裂症患者和正常对照者的海马和背外侧前额叶皮质（DLPFC）白质和灰质的尸检样本进行多元回归分析，评估了多重变量对参考基因（内参基因）信使RNA （mRNA）表达的贡献。结果：基因表达的最强预测因子是总RNA质量。其他重要因素包括pH值、死后间隔、年龄和agonal状态持续时间，但这些因素的重要性取决于转录物测量、脑区域分析和诊断。从DLPFC白质中获得的RNA质量也受到吸烟的不利影响。结论：我们的研究结果表明，应该使用多个管家基因的几何平均值对靶基因的表达数据进行归一化，以控制样品之间RNA质量的差异。结果还表明，准确评估其他混杂因素并将其作为回归因子纳入分析，对于获得可靠和准确的mRNA表达定量至关重要。
Background: Studies of postmortem human brain are important for investigating underlying pathogenic molecular mechanisms of neuropsychiatric disorders. They are, however, confounded by pre- and postmortem factors. The purpose of this study was to identify sources of variation that will enable a better design of gene expression studies and higher reliability of gene expression data.Methods. We assessed the contribution of multiple variables to messenger RNA (mRNA) expression of reference (housekeeping) genes measured by reverse transcriptase-polymerase chain reaction (RT-PCR) by multiple regression analysis in a large number (N = 143) of autopsy samples from the hippocampus and white and grey matter of the dorsolateral Prefrontal cortex (DLPFC) of patients with schizophrenia and normal control subjects.Results: The strongest predictor of gene expression was total RNA quality. Other significant factors included pH, postmortem interval, age and the duration of the agonal state, but the importance of these factors depended on transcript measured, brain region analyzed, and diagnosis. The quality of RNA obtained from the DLPFC white matter was also adversely affected by smoking.Conclusions: Our results show that normalization of expression data of target genes with a geometric mean of multiple housekeeping genes should be used to control for differences in RNA quality between samples. The results also suggest that accurate assessment Of other confounding factors and their inclusion as regressors in the analysis is critical for obtaining reliable and accurate quantification of mRNA expression.