Dynamic networks reveal key players in aging
Dynamic networks reveal key players in aging
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DOI：
10.1093/bioinformatics/btu089
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发表时间：
2014-06-15
期刊：
影响因子：
5.8
通讯作者：
Milenkovic, Tijana
中科院分区：
文献类型：
--
作者：
Faisal, Fazle E.;Milenkovic, Tijana
动机：因为对疾病的易感性随着年龄的增长而增加，研究衰老变得重要。基因表达或序列数据的分析对于研究衰老是必不可少的，但却仅限于孤立地研究基因及其蛋白质产物，而忽视了它们之间的联系。然而，蛋白质通过与其他蛋白质相互作用来发挥作用，这正是生物网络（BN）的模型。因此，分析蛋白质的BN拓扑结构有助于理解衰老。当前用于分析系统级BN的方法处理它们的静态表示，即使细胞是动态的。出于这个原因，因为不同的数据类型可以提供互补的生物学见解，我们将当前的静态BN与衰老相关的基因表达数据相结合，以构建动态的年龄特异性BN。然后，我们采用敏感的拓扑措施的动态BN研究细胞的变化与aging.Results：虽然全球BN拓扑结构不显着改变与年龄，一些基因的局部拓扑结构。我们预测这些基因与衰老有关。我们通过以下方式证明了我们预测的可信度：（i）观察我们预测的衰老相关基因和“地面实况”衰老相关基因之间的显著重叠;（ii）观察在我们的衰老相关预测中丰富的功能和疾病之间的显著重叠，以及那些在“地面实况”衰老相关数据中丰富的功能和疾病;（iii）提供证据，证明我们与衰老相关的预测中丰富的疾病与人类衰老有关;（iv）验证我们在文献中的高分新预测。
Motivation: Because susceptibility to diseases increases with age, studying aging gains importance. Analyses of gene expression or sequence data, which have been indispensable for investigating aging, have been limited to studying genes and their protein products in isolation, ignoring their connectivities. However, proteins function by interacting with other proteins, and this is exactly what biological networks (BNs) model. Thus, analyzing the proteins' BN topologies could contribute to the understanding of aging. Current methods for analyzing systems-level BNs deal with their static representations, even though cells are dynamic. For this reason, and because different data types can give complementary biological insights, we integrate current static BNs with aging-related gene expression data to construct dynamic age-specific BNs. Then, we apply sensitive measures of topology to the dynamic BNs to study cellular changes with age.Results: While global BN topologies do not significantly change with age, local topologies of a number of genes do. We predict such genes to be aging-related. We demonstrate credibility of our predictions by (i) observing significant overlap between our predicted aging-related genes and 'ground truth' aging-related genes; (ii) observing significant overlap between functions and diseases that are enriched in our aging-related predictions and those that are enriched in 'ground truth' aging-related data; (iii) providing evidence that diseases which are enriched in our aging-related predictions are linked to human aging; and (iv) validating our high-scoring novel predictions in the literature.