Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models.
Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models.
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DOI：
10.1371/journal.pone.0057180
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Shea LD
中科院分区：
文献类型：
--
作者：
Siletz A;Schnabel M;Kniazeva E;Schumacher AJ;Shin S;Jeruss JS;Shea LD
上皮-间质转化（EMT）是细胞分化的一个复杂变化，使乳腺癌细胞获得侵袭性。EMT涉及一系列调节变化，这些变化使上皮表型不稳定并允许间充质特征显现。由于转录因子（TF）是导致表型变化的全基因组表达变化的上游效应子，因此了解EMT期间TF活性的顺序变化提供了关于该过程机制的丰富信息。由于分子间的相互作用会随着细胞从上皮分化到间充质分化的进程而变化，因此需要动态网络来捕获分子过程的变化背景。在这项研究中，我们应用了一个新兴的高通量，动态TF活性阵列来定义TF活性网络的变化，在三个基于细胞的模型EMT乳腺癌的基础上HMLE扭曲ER和MCF-7乳腺上皮细胞。TF阵列区分保守的模型特异性TF活性变化的三个模型。时间依赖性数据用于识别具有显著正相关或负相关的TF活性对，表明在整个六天研究期间相互依赖的TF活性。动态TF活性模式被聚类成TF组，TF组沿着基因表达变化和侵袭能力的获得而变化。将时间依赖性TF活性数据与TF相互作用的先验知识相结合，以构建TF活性网络的动态模型，因为上皮细胞获得侵袭性特征。这些分析显示EMT从一个独特的和有针对性的Vantage，并可能最终有助于诊断和治疗。
The epithelial-mesenchymal transition (EMT) is a complex change in cell differentiation that allows breast carcinoma cells to acquire invasive properties. EMT involves a cascade of regulatory changes that destabilize the epithelial phenotype and allow mesenchymal features to manifest. As transcription factors (TFs) are upstream effectors of the genome-wide expression changes that result in phenotypic change, understanding the sequential changes in TF activity during EMT provides rich information on the mechanism of this process. Because molecular interactions will vary as cells progress from an epithelial to a mesenchymal differentiation program, dynamic networks are needed to capture the changing context of molecular processes. In this study we applied an emerging high-throughput, dynamic TF activity array to define TF activity network changes in three cell-based models of EMT in breast cancer based on HMLE Twist ER and MCF-7 mammary epithelial cells. The TF array distinguished conserved from model-specific TF activity changes in the three models. Time-dependent data was used to identify pairs of TF activities with significant positive or negative correlation, indicative of interdependent TF activity throughout the six-day study period. Dynamic TF activity patterns were clustered into groups of TFs that change along a time course of gene expression changes and acquisition of invasive capacity. Time-dependent TF activity data was combined with prior knowledge of TF interactions to construct dynamic models of TF activity networks as epithelial cells acquire invasive characteristics. These analyses show EMT from a unique and targetable vantage and may ultimately contribute to diagnosis and therapy.
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影响因子：
4
作者：
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通讯作者：
Cano, A
影响因子：
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作者：
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影响因子：
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作者：
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Wade PA
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作者：
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影响因子：
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作者：
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通讯作者：
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