Characterizing disease states from topological properties of transcriptional regulatory networks.
Characterizing disease states from topological properties of transcriptional regulatory networks.
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从转录调节网络的拓扑特性中表征疾病状态。
DOI：
10.1186/1471-2105-7-236
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发表时间：
2006-05-02
影响因子：
3
通讯作者：
Kluger, Yuval
中科院分区：
文献类型：
--
作者：
Tuck, David P.;Kluger, Harriet M.;Kluger, Yuval
高通量基因表达实验产生大量数据，除了对样本进行分类之外，还可以增强我们对疾病过程的理解。在这里，我们提出了基于使用序列预测、基于文献的数据和基因表达研究构建正常和异常细胞的转录调控网络的数据分离的新范例。我们分析了许多患病和正常细胞的表达数据集，包括不同类型的急性白血病和具有不同临床结果的乳腺癌。我们构建了样本特异性调控网络，以识别转录因子 (TF) 和区分健康和患病状态的调控基因之间的联系。这种方法的优点是可以识别健康和患病状态之间具有差异活性的关键转录因子-基因对，而不是仅仅使用基因表达谱，从而暗示可能涉及基因失调的过程。然后，我们通过研究指向受调节基因或源自一个 TF 的多个调节链接的功能的同时变化（或分别由其入度或出度测量定义的基因中心性的变化）来推广这种方法。我们发现，基于这些基因中心性度量，通常可以比使用单独的链接更可靠地分离样本。我们检查了转录网络中基因子集的距离分布（遍历每对基因之间的路径所需的链接数量），其集体表达谱可以最好地将每个数据集分成预定义的组。我们发现对样本进行最佳分类的基因集中在基因调控网络的邻域中。这表明在疾病状态下失调的基因表现出显着程度的连接性。转录因子调节的基因链接和基因在转录网络上的中心地位可用于区分细胞类型。转录网络蓝图可以作为进一步研究疾病状态下基因失调的基础。
High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity. Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.
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影响因子：
7
作者：
Lyons-Weiler, J;Patel, S;Bhattacharya, S
通讯作者：
Bhattacharya, S
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
64.8
作者：
Jeong, H;Mason, SP;Oltvai, ZN
通讯作者：
Oltvai, ZN
DOI：
10.1073/pnas.2136632100
发表时间：
2003-12-23
影响因子：
11.1
作者：
Liao, JC;Boscolo, R;Roychowdhury, VP
通讯作者：
Roychowdhury, VP
影响因子：
168.9
作者：
Huang, E;Cheng, SH;Huang, AT
通讯作者：
Huang, AT