Dataset Information

A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface.

ABSTRACT: The centrosome is the primary microtubule organizing center of the cells and templates the formation of cilia, thereby operating at a nexus of critical cellular functions. Here, we use proximity-dependent biotinylation (BioID) to map the centrosome-cilium interface; with 58 bait proteins we generate a protein topology network comprising >7,000 interactions. Analysis of interaction profiles coupled with high resolution phenotypic profiling implicates a number of protein modules in centriole duplication, ciliogenesis, and centriolar satellite biogenesis and highlights extensive interplay between these processes. By monitoring dynamic changes in the centrosome-cilium protein interaction landscape during ciliogenesis, we also identify satellite proteins that support cilia formation. Systematic profiling of proximity interactions combined with functional analysis thus provides a rich resource for better understanding human centrosome and cilia biology. Similar strategies may be applied to other complex biological structures or pathways.

SUBMITTER: Gupta GD

PROVIDER: S-EPMC5089374 | biostudies-other | 2015 Dec

REPOSITORIES: biostudies-other

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Publications

A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface.

Gupta Gagan D GD Coyaud Étienne É Gonçalves João J Gonçalves João J Mojarad Bahareh A BA Liu Yi Y Wu Qianzhu Q Gheiratmand Ladan L Comartin David D Tkach Johnny M JM Cheung Sally W T SW Bashkurov Mikhail M Hasegan Monica M Knight James D JD Lin Zhen-Yuan ZY Schueler Markus M Hildebrandt Friedhelm F Moffat Jason J Gingras Anne-Claude AC Raught Brian B Pelletier Laurence L

Cell 20151201 6

The centrosome is the primary microtubule organizing center of the cells and templates the formation of cilia, thereby operating at a nexus of critical cellular functions. Here, we use proximity-dependent biotinylation (BioID) to map the centrosome-cilium interface; with 58 bait proteins we generate a protein topology network comprising >7,000 interactions. Analysis of interaction profiles coupled with high resolution phenotypic profiling implicates a number of protein modules in centriole dupli ...[more]

PMID: 26638075

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Project description:Proteins involved in interactions throughout the course of evolution tend to co-evolve and compensatory changes may occur in interacting proteins to maintain or refine such interactions. However, certain residue pair alterations may prove to be detrimental for functional interactions. Hence, determining co-evolutionary pairings that could be structurally or functionally relevant for maintaining the conservation of an inter-protein interaction is important. Inter-protein co-evolution analysis in several complexes utilizing multiple existing methodologies suggested that co-evolutionary pairings can occur in spatially proximal and distant regions in inter-protein interactions. Subsequently, the Co-Var (Correlated Variation) method based on mutual information and Bhattacharyya coefficient was developed, validated, and found to perform relatively better than CAPS and EV-complex. Interestingly, while applying the Co-Var measure and EV-complex program on a set of protein-protein interaction complexes, co-evolutionary pairings were obtained in interface and non-interface regions in protein complexes. The Co-Var approach involves determining high degree co-evolutionary pairings that include multiple co-evolutionary connections between particular co-evolved residue positions in one protein with multiple residue positions in the binding partner. Detailed analyses of high degree co-evolutionary pairings in protein-protein complexes involved in cancer metastasis suggested that most of the residue positions forming such co-evolutionary connections mainly occurred within functional domains of constituent proteins and substitution mutations were also common among these positions. The physiological relevance of these predictions suggested that Co-Var can predict residues that could be crucial for preserving functional protein-protein interactions. Finally, Co-Var web server (http://www.hpppi.iicb.res.in/ishi/covar/index.html) that implements this methodology identifies co-evolutionary pairings in intra and inter-protein interactions.

Project description:Gene expression profiles have been frequently integrated with the human protein interactome to uncover functional modules under specific conditions like disease state. Beyond traditional differential expression analysis, differential co-expression analysis has emerged as a robust approach to reveal condition-specific network modules, with successful applications in a few human disease studies. Hepatocellular carcinoma (HCC), which is often interrelated with the Hepatitis C virus, typically develops through multiple stages. A comprehensive investigation of HCC progression-specific differential co-expression modules may advance our understanding of HCC's pathophysiological mechanisms.Compared with differentially expressed genes, differentially co-expressed genes were found more likely enriched with Hepatitis C virus binding proteins and cancer-mutated genes, and they were clustered more densely in the human reference protein interaction network. These observations indicated that a differential co-expression approach could outperform the standard differential expression network analysis in searching for disease-related modules. We then proposed a differential co-expression network approach to uncover network modules involved in HCC development. Specifically, we discovered subnetworks that enriched differentially co-expressed gene pairs in each HCC transition stage, and further resolved modules with coherent co-expression change patterns over all HCC developmental stages. Our identified network modules were enriched with HCC-related genes and implicated in cancer-related biological functions. In particular, APC and YWHAZ were highlighted as two most remarkable genes in the network modules, and their dynamic interaction partnership was resolved in HCC development.We demonstrated that integration of differential co-expression with the protein interactome could outperform the traditional differential expression approach in discovering network modules of human diseases. In our application of this approach to HCC's gene expression data, we successfully identified subnetworks with marked differential co-expression in individual HCC stage transitions and network modules with coherent co-expression change patterns over all HCC developmental stages. Our results shed light on subtle HCC mechanisms, including temporal activation and dismissal of pivotal functions and dynamic interaction partnerships of key genes.

Project description:BackgroundMost proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently.DescriptionWe introduce a large-scale protein domain interaction interface database called InterPare http://interpare.net. It contains both inter-chain (between chains) interfaces and intra-chain (within chain) interfaces. InterPare uses three methods to detect interfaces: 1) the geometric distance method for checking the distance between atoms that belong to different domains, 2) Accessible Surface Area (ASA), a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3) the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website.ConclusionInterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance), 10,431 (ASA), and 11,010 (Voronoi diagram) entries in the Protein Data Bank (PDB) containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain-domain interaction interfaces and 12,758 intra-chain domain-domain interfaces.

Dataset Information

A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface.

Publications

A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface.

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