Using gene expression and systems biology to interrogate auditory hallucinations in schizophrenic patients

G. López-Campos, J. Gilabert-Juan, N. Sebastia-Ortega, Rocío González-Martínez, J. Nácher, J. Sanjuán, M. Moltó
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Abstract

Schizophrenia is a severe mental disorder affecting around 1% of the population. This disease presents a complex aetiology that has not been completely unveiled yet. Auditory hallucinations are a very significant and disruptive symptom of schizophrenia affecting between 60% and 80% of schizophrenic patients. In this paper we have used a network-based transcriptomic analysis aiming to identify differences in gene expression between schizophrenic patients with and without auditory hallucinations. Gene expression data from blood samples drained from 30 schizophrenia patients were generated using Affymetrix Human Gene 2.0 ST Genechips. Affymetrix Expression console was used for normalization and quality control purposes. The RMA normalization method was applied for gene summarization and then a filter applied to keep only the most variably expressed probesets (4,508). These dataset was analysed using the weighted gene co-expression network analysis (WGCNA) package in R. The gene co-expression network analyses allowed us to identify eleven different gene modules based on their topological overlap. These modules were related to the relevant phenotypic information and allowing us to identify modules related with different phenotypic traits of interest. Gene co-expression network analysis is a useful tool for the analysis of gene expression analysis. Its application in the analysis of schizophrenia gene expression provides an insight on the molecular mechanisms related with this disease and the differences at the molecular level between patients presenting auditory hallucinations and those that do not. In our analysis we have been able to identify different gene modules containing genes expression profiles that can be related with clinically relevant phenotypes. These gene modules could be functionally annotated and related with different pathways and gene ontology terms that are relevant in the context of this analysis.
利用基因表达和系统生物学研究精神分裂症患者的幻听
精神分裂症是一种严重的精神障碍,影响了大约1%的人口。这种疾病呈现出一种尚未完全揭示的复杂病因学。幻听是精神分裂症非常显著的破坏性症状,影响了60%至80%的精神分裂症患者。在本文中,我们使用了一种基于网络的转录组学分析,旨在确定有幻听和无幻听的精神分裂症患者之间基因表达的差异。使用Affymetrix Human Gene 2.0 ST基因芯片生成30例精神分裂症患者血液样本的基因表达数据。Affymetrix Expression控制台用于规范化和质量控制目的。RMA归一化方法应用于基因汇总,然后应用过滤器只保留变量表达最多的问题集(4,508)。这些数据集使用r中的加权基因共表达网络分析(WGCNA)包进行分析。基因共表达网络分析使我们能够根据它们的拓扑重叠识别出11个不同的基因模块。这些模块与相关表型信息相关,使我们能够识别与感兴趣的不同表型性状相关的模块。基因共表达网络分析是基因表达分析的有效工具。它在精神分裂症基因表达分析中的应用提供了与该疾病相关的分子机制以及出现幻听和没有幻听的患者在分子水平上的差异的见解。在我们的分析中,我们已经能够识别出不同的基因模块,其中包含与临床相关表型相关的基因表达谱。这些基因模块可以进行功能注释,并与本分析上下文相关的不同途径和基因本体术语相关联。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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