A factor graph nested effects model to identify networks from genetic perturbations.
A factor graph nested effects model to identify networks from genetic perturbations.
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DOI：
10.1371/journal.pcbi.1000274
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发表时间：
2009-01
影响因子：
4.3
通讯作者：
Stuart, Joshua M.
中科院分区：
文献类型：
--
作者：
Vaske, Charles J.;House, Carrie;Luu, Truong;Frank, Bryan;Yeang, Chen-Hsiang;Lee, Norman H.;Stuart, Joshua M.
复杂的表型，如正常细胞群转化为癌组织，是由一系列分子触发因素出错造成的。我们描述了一种方法，该方法搜索与在细胞中具有共同作用的一组基因（如疾病表型）敲除下观察到的表达变化一致的遗传网络。该方法扩展了Markowetz等人（2005）的嵌套效应模型，通过使用概率因子图来搜索代表这些沉默基因之间相互作用的网络。该方法还通过在特定的下游点附加新基因来扩展网络，为随后的扰动提供候选基因，以进一步表征该途径。我们研究了因子图方法提供的扩展，其中模型区分抑制和刺激相互作用。我们发现，扩展在恢复模拟和酿酒酵母网络结构方面产生了显着的改进。我们应用该方法发现了参与人类结肠癌细胞侵袭途径的基因之间的信号网络。该方法预测了在侵袭过程中具有新作用的几个基因。我们敲除了通过我们的方法识别的两个基因，发现这两个敲除都会导致结肠癌细胞系中侵袭性潜能的丧失。嵌套效应模型可能是推断在正常和疾病相关过程中运作的调节连接和基因的有力工具。生物过程是许多基因及其编码的蛋白质的作用和相互作用的结果。我们对许多生物过程的相互作用的了解是有限的，特别是对于癌症，基因组改变可能会产生正常组织中不存在的全新途径。干扰基因表达（例如，通过删除一个基因）长期以来一直被用作分子生物学中阐明相互作用的工具，但这是非常昂贵和劳动密集型的。寻找可能参与其中的新基因可能是一次令人生畏的“钓鱼考察”。我们设计了一种工具，可以使用高通量基因表达数据自动推断相互作用。当一个基因沉默时，它会引起其他基因的开启或关闭，从而提供有关该基因作用途径的线索。我们的方法使用全基因组的开/关状态作为指纹来检测一组沉默基因之间的相互作用。我们能够阐明与转移性结肠癌有关的几个基因的相互作用网络。新发现的连接到网络的基因在癌细胞入侵人类细胞中起作用，验证了这种方法。因此，该方法能够有效地发现诸如癌变等生物过程的基础网络。
Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry. We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell, such as a disease phenotype. The method extends the Nested Effects Model of Markowetz et al. (2005) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes. The method also expands the network by attaching new genes at specific downstream points, providing candidates for subsequent perturbations to further characterize the pathway. We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions. We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks. We applied the approach to discover a signaling network among genes involved in a human colon cancer cell invasiveness pathway. The method predicts several genes with new roles in the invasiveness process. We knocked down two genes identified by our approach and found that both knock-downs produce loss of invasive potential in a colon cancer cell line. Nested effects models may be a powerful tool for inferring regulatory connections and genes that operate in normal and disease-related processes. Biological processes are the result of the actions and interactions of many genes and the proteins that they encode. Our knowledge of interactions for many biological processes is limited, especially for cancer where genomic alterations may create entirely novel pathways not present in normal tissue. Perturbing gene expression (for example, by deleting a gene) has long been used as a tool in molecular biology to elucidate interactions but is very expensive and labor intensive. The search for new genes that may participate can be a daunting “fishing expedition.” We have devised a tool that automatically infers interactions using high-throughput gene expression data. When a gene is silenced, it causes other genes to be switched on or off, which provide clues about the pathway(s) in which the gene acts. Our method uses the genomewide on/off states as a fingerprint to detect interactions among a set of silenced genes. We were able to elucidate a network of interactions for several genes implicated in metastatic colon cancer. Genes newly connected to the network were found to operate in cancer cell invasion in human cells, validating the approach. Thus, the method enables an efficient discovery of the networks that underlie biological processes such as carcinogenesis.
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影响因子：
7.5
作者：
Iaquinta, Phillip J.;Lees, Jacqueline A.
通讯作者：
Lees, Jacqueline A.
影响因子：
11.2
作者：
Balasenthil, Seetharaman;Gururaj, Anupama E.;Kumar, Rakesh
通讯作者：
Kumar, Rakesh
影响因子：
56.9
作者：
Brummelkamp, TR;Bernards, R;Agami, R
通讯作者：
Agami, R
影响因子：
14.9
作者：
Breitkreutz BJ;Stark C;Reguly T;Boucher L;Breitkreutz A;Livstone M;Oughtred R;Lackner DH;Bähler J;Wood V;Dolinski K;Tyers M
通讯作者：
Tyers M
DOI：
10.1073/pnas.95.13.7357
发表时间：
1998-06-23
影响因子：
11.1
作者：
de Nadal, E;Clotet, J;Ariño, J
通讯作者：
Ariño, J