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在基于模型的分类中,开关样基因的表达谱可准确分类组织和感染性疾病表型。

Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification.

作者信息

Gormley Michael, Tozeren Aydin

机构信息

School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA.

出版信息

BMC Bioinformatics. 2008 Nov 17;9:486. doi: 10.1186/1471-2105-9-486.

Abstract

BACKGROUND

Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.

RESULTS

Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response.

CONCLUSION

Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens.

摘要

背景

跨多种生物学表型的基因表达微阵列数据集的大规模汇编,提供了一种以识别和注释人类和小鼠基因组中的双峰基因为形式来收集先验知识的方法。这些类似开关的基因占已知人类基因的15%,并且富含编码细胞外和膜蛋白的基因。确定双峰基因在大规模数据集中进行类别发现的预测潜力很有意义。

结果

基于模型的聚类算法的使用,根据双峰基因表达将400多个微阵列样本准确分类为19种不同的组织类型。在基于模型的微阵列数据聚类中,双峰表达模式在区分传染病方面也非常有效。在独立的用于验证的测试数据集中,对仅限于类似开关基因的特征选择进行监督分类,也识别出了组织特异性和传染病特异性特征。确定各种组织和疾病中类似开关基因的“开”和“关”状态,有助于识别激活/失活的途径。神经、骨骼肌和心肌组织中激活的类似开关基因往往具有组织特异性作用。传染病中大多数激活的基因都参与与免疫反应相关的过程。

结论

类似开关的双峰基因集从健康和传染病中的微阵列数据中捕获全基因组特征。编码细胞外和膜蛋白的双峰基因子集与组织特异性相关,这表明如果它们在疾病发作时表达发生改变,那么它们作为生物标志物具有潜在作用。此外,我们提供证据表明,双峰基因参与了包括组织特异性功能以及免疫系统对入侵病原体的反应等时间和空间上的活跃机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/562a/2620272/cf3ee5c1650d/1471-2105-9-486-1.jpg

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