Dataset Information

Identification of Drosophila centromere associated proteins by quantitative affinity purification-mass spectrometry.

ABSTRACT: Centromeres of higher eukaryotes are epigenetically defined by the centromere specific histone H3 variant CENP-A(CID). CENP-A(CID) builds the foundation for the assembly of a large network of proteins. In contrast to mammalian systems, the protein composition of Drosophila centromeres has not been comprehensively investigated. Here we describe the proteome of Drosophila melanogaster centromeres as analyzed by quantitative affinity purification-mass spectrometry (AP-MS). The AP-MS input chromatin material was prepared from D. melanogaster cell lines expressing CENP-A(CID) or H3.3 fused to EGFP as baits. Centromere chromatin enriched proteins were identified based on their relative abundance in CENP-A(CID)-GFP compared to H3.3-GFP or mock affinity-purifications. The analysis yielded 86 proteins specifically enriched in centromere chromatin preparations. The data accompanying the manuscript on this approach (Barth et al., 2015, Proteomics 14:2167-78, DOI: 10.1002/pmic.201400052) has been deposited to the ProteomeXchange Consortium (http://www.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD000758.

SUBMITTER: Barth TK

PROVIDER: S-EPMC4536286 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Identification of Drosophila centromere associated proteins by quantitative affinity purification-mass spectrometry.

Barth Teresa K TK Schade Georg O M GO Schmidt Andreas A Vetter Irene I Wirth Marc M Heun Patrick P Imhof Axel A Thomae Andreas W AW

Data in brief 20150726

Centromeres of higher eukaryotes are epigenetically defined by the centromere specific histone H3 variant CENP-A(CID). CENP-A(CID) builds the foundation for the assembly of a large network of proteins. In contrast to mammalian systems, the protein composition of Drosophila centromeres has not been comprehensively investigated. Here we describe the proteome of Drosophila melanogaster centromeres as analyzed by quantitative affinity purification-mass spectrometry (AP-MS). The AP-MS input chromatin ...[more]

PMID: 26306323

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Project description:Many species possess an endogenous circadian clock to synchronize internal physiology with an oscillating external environment. In plants, the circadian clock coordinates growth, metabolism and development over daily and seasonal time scales. Many proteins in the circadian network form oscillating complexes that temporally regulate myriad processes, including signal transduction, transcription, protein degradation and post-translational modification. In Arabidopsis thaliana, a tripartite complex composed of EARLY FLOWERING 4 (ELF4), EARLY FLOWERING 3 (ELF3), and LUX ARRHYTHMO (LUX), named the evening complex, modulates daily rhythms in gene expression and growth through transcriptional regulation. However, little is known about the physical interactions that connect the circadian system to other pathways. We used affinity purification and mass spectrometry (AP-MS) methods to identify proteins that associate with the evening complex in A. thaliana. New connections within the circadian network as well as to light signaling pathways were identified, including linkages between the evening complex, TIMING OF CAB EXPRESSION1 (TOC1), TIME FOR COFFEE (TIC), all phytochromes and TANDEM ZINC KNUCKLE/PLUS3 (TZP). Coupling genetic mutation with affinity purifications tested the roles of phytochrome B (phyB), EARLY FLOWERING 4, and EARLY FLOWERING 3 as nodes connecting the evening complex to clock and light signaling pathways. These experiments establish a hierarchical association between pathways and indicate direct and indirect interactions. Specifically, the results suggested that EARLY FLOWERING 3 and phytochrome B act as hubs connecting the clock and red light signaling pathways. Finally, we characterized a clade of associated nuclear kinases that regulate circadian rhythms, growth, and flowering in A. thaliana. Coupling mass spectrometry and genetics is a powerful method to rapidly and directly identify novel components and connections within and between complex signaling pathways.

Project description:BACKGROUND: Recent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species. An important challenge for the analysis of these data is to extract functional modules such as protein complexes and biological processes from networks which are characterised by the present of a significant number of false positives. Various computational techniques have been applied in recent years. However, most of them treat protein interaction as binary. Co-complex relations derived from affinity purification/mass spectrometry (AP-MS) experiments have been largely ignored. METHODS: This paper presents a new algorithm for detecting protein complexes from AP-MS data. The algorithm intends to detect groups of prey proteins that are significantly co-associated with the same set of bait proteins. We first construct AP-MS data as a bipartite network, where one set of nodes consists of bait proteins and the other set is composed of prey proteins. We then calculate pair-wise similarities of bait proteins based on the number of their commonly shared neighbours. A hierarchical clustering algorithm is employed to cluster bait proteins based on the similarities and thus a set of 'seed' clusters is obtained. Starting from these 'seed' clusters, an expansion process is developed to identify prey proteins which are significantly associated with the same set of bait proteins. Then, a set of complete protein complexes is derived. In application to two real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes and well-characterized cellular component annotation from Gene Ontology (GO). Several statistical metrics have been applied for evaluation. RESULTS: Experimental results show that, the proposed algorithm achieves significant improvement in detecting protein complexes from AP-MS data. In comparison to the well-known MCL algorithm, our algorithm improves the accuracy rate by about 20% in detecting protein complexes in both networks and increases the F-Measure value by about 50% in Krogan_2006 network. Greater precision and better accuracy have been achieved and the identified complexes are demonstrated to match well with existing curated protein complexes. CONCLUSIONS: Our study highlights the significance of taking co-complex relations into account when extracting protein complexes from AP-MS data. The algorithm proposed in this paper can be easily extended to the analysis of other biological networks which can be conveniently represented by bipartite graphs such as drug-target networks.

Dataset Information

Identification of Drosophila centromere associated proteins by quantitative affinity purification-mass spectrometry.

Publications

Identification of Drosophila centromere associated proteins by quantitative affinity purification-mass spectrometry.

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