Department of Systems Biology and Translational Medicine, Texas A&M Health Science Center College of Medicine, Temple, Texas, United States of America.
PLoS One. 2010 Feb 4;5(2):e9056. doi: 10.1371/journal.pone.0009056.
Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers.
METHODOLOGY/PRINCIPAL FINDINGS: In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as 'brain group' and 'non-brain group'; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes.
CONCLUSIONS/SIGNIFICANCE: The methodology employed here may be used to facilitate disease-specific biomarker discovery.
识别具有开关特性的基因将有助于发现这些特性所基于的调控机制，并为布尔网络在基因调控模型中的恰当应用提供知识。由于开关样行为可能与组织特异性表达有关，因此这些基因产物有望成为组织特异性生物标志物的合理候选者。
方法/主要发现：在对基因的系统分类和生物标志物的搜索中，分析了来自 2145 个小鼠阵列样本的超过 16000 个基因的基因表达谱 (GEP)。使用四个分布度量标准（平均值、标准差、峰度和偏度）将 GEP 分为四类：主要关闭型、主要开启型、渐变（恒定型）和开关型基因。所研究的阵列还按组织类型进行分组和检查。例如，将阵列分类为“大脑组”和“非大脑组”；然后使用柯尔莫哥洛夫-斯米尔诺夫距离和皮尔逊相关系数比较每个基因在大脑和非大脑之间的 GEP。我们因此能够识别出组织特异性的生物标志物候选基因。
结论/意义：这里采用的方法可用于促进疾病特异性生物标志物的发现。
