Dataset Information

Identification of a functional interaction between the transmembrane protein Smoothened and the kinesin-related protein Costal2.

ABSTRACT: The hedgehog (Hh) family of morphogens plays important instructional roles in the development of numerous metazoan structures. Consistent with the role Hh homologs play in cell fate determination, aberrant Hh signaling results in numerous human pathologies. Hh signal transduction is initiated when Hh binds to its receptor Patched (Ptc), activating the transmembrane protein Smoothened (Smo). Smo transmits its activation signal to a microtubule-associated Hedgehog signaling complex (HSC). At a minimum, the HSC consists of the Kinesin-related protein Costal2 (Cos2), the protein kinase Fused (Fu), and the transcription factor Cubitus interruptus (Ci). In response to HSC activation, the ratio between repressor and activator forms of Ci is altered, determining the expression levels of various Hh target genes. The steps between Smo activation and signaling to the HSC have not been described. Here, we describe a functional interaction between Smo and Cos2, which is necessary for Hh signaling. We propose that this interaction is direct and allows for activation of Ci in response to Hh. This work fills in the last major gap in our understanding of the Hh signal transduction pathway by suggesting that no intermediate signal is required to connect Smo to the HSC.

SUBMITTER: Ogden SK

PROVIDER: S-EPMC3711143 | biostudies-literature | 2003 Nov

REPOSITORIES: biostudies-literature

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Identification of a functional interaction between the transmembrane protein Smoothened and the kinesin-related protein Costal2.

Ogden Stacey K SK Ascano Manuel M Stegman Melanie A MA Suber Liza M LM Hooper Joan E JE Robbins David J DJ

Current biology : CB 20031101 22

The hedgehog (Hh) family of morphogens plays important instructional roles in the development of numerous metazoan structures. Consistent with the role Hh homologs play in cell fate determination, aberrant Hh signaling results in numerous human pathologies. Hh signal transduction is initiated when Hh binds to its receptor Patched (Ptc), activating the transmembrane protein Smoothened (Smo). Smo transmits its activation signal to a microtubule-associated Hedgehog signaling complex (HSC). At a min ...[more]

PMID: 14614827

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Project description:MotivationIdentifying functional modules in protein-protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of 'higher than expected connectivity', those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks.ResultsIn this article, we propose a novel optimization formulation LCP(2) (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP(2) by random walk. A spectral approximate algorithm SLCP(2) is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP(2) to a new algorithm (greedy algorithm for LCP(2)) GLCP(2) to identify overlapping functional modules. We compare SLCP(2) and GLCP(2) with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP(2) outperform all other compared algorithms.Availability and implementationAll data and code are available at http://www.cse.usf.edu/~xqian/fmi/slcp2hop/.

Project description:In combination with gene expression profiles, the protein interaction network (PIN) constructs a dynamic network that includes multiple functional modules. Previous studies have demonstrated that rifampin can influence drug metabolism by regulating drug-metabolizing enzymes, transporters, and microRNAs (miRNAs). Rifampin induces gene expression, at least in part, by activating the pregnane X receptor (PXR), which induces gene expression; however, the impact of rifampin on global gene regulation has not been examined under the molecular network frameworks.In this study, we extracted rifampin-induced significant differentially expressed genes (SDG) based on the gene expression profile. By integrating the SDG and human protein interaction network (HPIN), we constructed the rifampin-regulated protein interaction network (RrPIN). Based on gene expression measurements, we extracted a subnetwork that showed enriched changes in molecular activity. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified the crucial rifampin-regulated biological pathways and associated genes. In addition, genes targeted by miRNAs that were significantly differentially expressed in the miRNA expression profile were extracted based on the miRNA-gene prediction tools. The miRNA-regulated PIN was further constructed using associated genes and miRNAs. For each miRNA, we further evaluated the potential impact by the gene interaction network using pathway analysis. RESULTS AND DISCCUSSION: We extracted the functional modules, which included 84 genes and 89 interactions, from the RrPIN, and identified 19 key rifampin-response genes that are associated with seven function pathways that include drug response and metabolism, and cancer pathways; many of the pathways were supported by previous studies. In addition, we identified that a set of 6 genes (CAV1, CREBBP, SMAD3, TRAF2, KBKG, and THBS1) functioning as gene hubs in the subnetworks that are regulated by rifampin. It is also suggested that 12 differentially expressed miRNAs were associated with 6 biological pathways.Our results suggest that rifampin contributes to changes in the expression of genes by regulating key molecules in the protein interaction networks. This study offers valuable insights into rifampin-induced biological mechanisms at the level of miRNAs, genes and proteins.

Dataset Information

Identification of a functional interaction between the transmembrane protein Smoothened and the kinesin-related protein Costal2.

Publications

Identification of a functional interaction between the transmembrane protein Smoothened and the kinesin-related protein Costal2.

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