Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets.
Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets.
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DOI：
10.1186/1752-0509-4-84
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发表时间：
2010-06-15
影响因子：
--
通讯作者：
Ganesan AK
中科院分区：
文献类型：
--
作者：
Ho H;Milenković T;Memisević V;Aruri J;Przulj N;Ganesan AK
基于rna介导干扰（RNAi）的功能基因组学是一种系统级方法，用于鉴定控制生物表型的新基因。现有的计算方法可以从调控给定生物过程的RNAi数据集中识别单个基因。然而，目前可用的方法无法确定哪些RNAi筛选“命中”是已知的调节被询问表型的具有良好特征的生物学途径的新组分。在这项研究中，我们描述了一种从RNAi数据集中识别基因的方法，这些基因是已知生物途径的新组成部分。我们在最近完成的RNAi筛选中通过实验验证了我们的方法，以确定黑色素生成的新调节因子。在这项研究中，我们利用基于PPI网络拓扑的方法来识别我们的RNAi数据集中可能是已知黑色素形成调控途径组成部分的靶标。我们的计算方法确定了一组筛选目标，这些目标在已知的色素调节剂内皮素受体B型（EDNRB）的人类PPI网络中拓扑聚类。验证性研究表明，这些基因影响色素黑色素瘤细胞（MNT-1）和正常黑色素细胞中的色素生成和EDNRB信号传导。我们提出了一种方法，可以从功能基因组学数据集中识别出现有统计和计算方法无法识别的具有良好特征的生物学途径的新成分。
RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis. In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes. We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches.
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影响因子：
14.9
作者：
Lee K;Chuang HY;Beyer A;Sung MK;Huh WK;Lee B;Ideker T
通讯作者：
Ideker T
影响因子：
64.8
作者：
LEV, S;MORENO, H;SCHLESSINGER, J
通讯作者：
SCHLESSINGER, J
影响因子：
4.8
作者：
Imokawa, G;Kobayasi, T;Miyagishi, M
通讯作者：
Miyagishi, M
影响因子：
64.5
作者：
Konig, Renate;Zhou, Yingyao;Elleder, Daniel;Diamond, Tracy L.;Bonamy, Ghislain M. C.;Irelan, Jeffrey T.;Chiang, Chih-Yuan;Tu, Buu P.;De Jesus, Paul D.;Lilley, Caroline E.;Seidel, Shannon;Opaluch, Amanda M.;Caldwell, Jeremy S.;Weitzman, Matthew D.;Kuhen, Kelli L.;Bandyopadhyay, Sourav;Ideker, Trey;Orth, Anthony P.;Miraglia, Loren J.;Bushman, Frederic D.;Young, John A.;Chanda, Sumit K.
通讯作者：
Chanda, Sumit K.
影响因子：
4.6
作者：
Hou, L;Pavan, WJ;Arnheiter, H
通讯作者：
Arnheiter, H