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首页> 外文期刊>PLoS Computational Biology >Phenotypic Robustness and the Assortativity Signature of Human Transcription Factor Networks
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Phenotypic Robustness and the Assortativity Signature of Human Transcription Factor Networks

机译:表型鲁棒性和人类转录因子网络的分类性签名。

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Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs — such as their degree distribution — with the robustness of a TFN's gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFN's assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFN's assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of the assortativity signature contributes to this robustness.
机译:许多发育,生理和行为过程取决于基因在时空上的精确表达。这种时空基因表达表型是由于序列特异性转录因子(TFs)与DNA的结合,以及对这种结合引起的附近基因的调控而产生的。这些附近的基因本身可能会编码TF,从而产生一个转录因子网络(TFN),其中节点代表TF,而有向边则表示TF之间的调控相互作用。计算研究已将TFN的几种拓扑特性(例如它们的度数分布)与TFN的基因表达表型对遗传和环境扰动的鲁棒性联系起来。另一个重要的拓扑属性是分类性,它度量具有相似边数的节点的连接趋势。在有向网络中,分类性包括四个不同的组件,它们共同形成一个分类性签名。我们对TFN的分类性签名如何影响其基因表达表型对干扰的鲁棒性了解甚少。虽然最近的理论结果表明,增加TFN的分类特征的一个特定成分会导致表型鲁棒性的提高,但是由于尚未对真实世界TFN的分类特征进行表征,因此该发现的生物学背景目前受到限制。因此,尚不清楚这些较早的理论发现是否与生物学相关。此外,还不知道分类特征的其他三个成分如何促进TFN的表型稳健性。在这里,我们使用公开可用的DNaseI-seq数据来测量41种不同的人类细胞和组织类型中全基因组TFN的分类特征。我们发现,所有TFN都有一个共同的分类签名,并且该签名赋予了TFN模型表型鲁棒性。最后,我们确定分类签名的四个组成部分中每个组成部分对这种鲁棒性的贡献程度。

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