A noncanonical PWI domain in the N-terminal helicase-associated region of the spliceosomal Brr2 protein

The spliceosomal RNA helicase Brr2 is required for the assembly of a catalytically active spliceosome on a messenger RNA precursor. Brr2 exhibits an unusual organization with tandem helicase units, each comprising dual RecA-like domains and a Sec63 homology unit, preceded by a more than 400-residue N-terminal helicase-associated region. Whereas recent crystal structures have provided insights into the molecular architecture and regulation of the Brr2 helicase region, little is known about the structural organization and function of its N-terminal part. Here, a near-atomic resolution crystal structure of a PWI-like domain that resides in the N-terminal region of Chaetomium thermophilum Brr2 is presented. CD spectroscopic studies suggested that this domain is conserved in the yeast and human Brr2 orthologues. Although canonical PWI domains act as low-specificity nucleic acid-binding domains, no significant affinity of the unusual PWI domain of Brr2 for a broad spectrum of DNAs and RNAs was detected in band-shift assays. Consistently, the C. thermophilum Brr2 PWI-like domain, in the conformation seen in the present crystal structure, lacks an expanded positively charged surface patch as observed in at least one canonical, nucleic acid-binding PWI domain. Instead, in a comprehensive yeast two-hybrid screen against human spliceosomal proteins, fragments of the N-terminal region of human Brr2 were found to interact with several other spliceosomal proteins. At least one of these interactions, with the Prp19 complex protein SPF27, depended on the presence of the PWI-like domain. The results suggest that the N-terminal region of Brr2 serves as a versatile protein-protein interaction platform in the spliceosome and that some interactions require or are reinforced by the PWI-like domain

A Y2H-seq approach defines the human protein methyltransferase interactome

To accelerate high-density interactome mapping, we developed a yeast two-hybrid interaction screening approach involving short-read second-generation sequencing (Y2H-seq) with improved sensitivity and a quantitative scoring readout allowing rapid interaction validation. We applied Y2H-seq to investigate enzymes involved in protein methylation, a largely unexplored post-translational modification. The reported network of 523 interactions involving 22 methyltransferases or demethylases is comprehensively annotated and validated through coimmunoprecipitation experiments and defines previously undiscovered cellular roles of nonhistone protein methylation

Introduction of Empirical Topology in Construction of Relationship Networks of Informative Objects

Understanding the structure of relationships between objects in a given database is one of the most important problems in the field of data mining. The structure can be defined for a set of single objects (clustering) or a set of groups of objects (network mapping). We propose a method for discovering relationships between individuals (single or groups) that is based on what we call the empirical topology, a system-theoretic measure of functional proximity. To illustrate the suitability and efficiency of the method, we apply it to an astronomical data base

UniHI 4: new tools for query, analysis and visualization of the human protein–protein interactome

Human protein interaction maps have become important tools of biomedical research for the elucidation of molecular mechanisms and the identification of new modulators of disease processes. The Unified Human Interactome database (UniHI, http://www.unihi.org) provides researchers with a comprehensive platform to query and access human protein–protein interaction (PPI) data. Since its first release, UniHI has considerably increased in size. The latest update of UniHI includes over 250 000 interactions between ∼22 300 unique proteins collected from 14 major PPI sources. However, this wealth of data also poses new challenges for researchers due to the complexity of interaction networks retrieved from the database. We therefore developed several new tools to query, analyze and visualize human PPI networks. Most importantly, UniHI allows now the construction of tissue-specific interaction networks and focused querying of canonical pathways. This will enable researchers to target their analysis and to prioritize candidate proteins for follow-up studies

ToppGene Suite for gene list enrichment analysis and candidate gene prioritization

ToppGene Suite (http://toppgene.cchmc.org; this web site is free and open to all users and does not require a login to access) is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome. The protein–protein interaction network (PPIN)-based disease candidate gene prioritization uses social and Web networks analysis algorithms (extended versions of the PageRank and HITS algorithms, and the K-Step Markov method). We demonstrate the utility of ToppGene Suite using 20 recently reported GWAS-based gene–disease associations (including novel disease genes) representing five diseases. ToppGene ranked 19 of 20 (95%) candidate genes within the top 20%, while ToppNet ranked 12 of 16 (75%) candidate genes among the top 20%

Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

UniHI: an entry gate to the human protein interactome

Systematic mapping of protein–protein interactions has become a central task of functional genomics. To map the human interactome, several strategies have recently been pursued. The generated interaction datasets are valuable resources for scientists in biology and medicine. However, comparison reveals limited overlap between different interaction networks. This divergence obstructs usability, as researchers have to interrogate numerous heterogeneous datasets to identify potential interaction partners for proteins of interest. To facilitate direct access through a single entry gate, we have started to integrate currently available human protein interaction data in an easily accessible online database. It is called UniHI (Unified Human Interactome) and is available at . At present, it is based on 10 major interaction maps derived by computational and experimental methods. It includes more than 150 000 distinct interactions between more than 17 000 unique human proteins. UniHI provides researchers with a flexible integrated tool for finding and using comprehensive information about the human interactome

GPR61 anchoring of PKA consolidates GPCR and cAMP signaling

Scaffolding proteins organize the information flow from activated G protein-coupled receptors (GPCRs) to intracellular effector cascades both spatially and temporally. By this means, signaling scaffolds, such as A-kinase anchoring proteins (AKAPs), compartmentalize kinase activity and ensure substrate selectivity. Using a phosphoproteomics approach we identified a physical and functional connection between protein kinase A (PKA) and Gpr161 (an orphan GPCR) signaling. We show that Gpr161 functions as a selective high-affinity AKAP for type I PKA regulatory subunits (RI). Using cell-based reporters to map protein–protein interactions, we discovered that RI binds directly and selectively to a hydrophobic protein–protein interaction interface in the cytoplasmic carboxyl-terminal tail of Gpr161. Furthermore, our data demonstrate that a binary complex between Gpr161 and RI promotes the compartmentalization of Gpr161 to the plasma membrane. Moreover, we show that Gpr161, functioning as an AKAP, recruits PKA RI to primary cilia in zebrafish embryos. We also show that Gpr161 is a target of PKA phosphorylation, and that mutation of the PKA phosphorylation site affects ciliary receptor localization. Thus, we propose that Gpr161 is itself an AKAP and that the cAMP-sensing Gpr161:PKA complex acts as cilium-compartmentalized signalosome, a concept that now needs to be considered in the analyzing, interpreting, and pharmaceutical targeting of PKA-associated functions

Bridging topological and functional information in protein interaction networks by short loops profiling

Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.This research was supported by the Biotechnology and Biological Sciences Research Council (BB/H018409/1 to AP, ACCC and FF, and BB/J016284/1 to NSBT) and by the Leukaemia & Lymphoma Research (to NSBT and FF). SSC is funded by a Leukaemia & Lymphoma Research Gordon Piller PhD Studentship

An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis

Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is a major cause of cancer-associated mortality. Secondary bone cancer (SBC) is a complex disease caused by metastasis of tumor cells from their primary site and is characterized by intricate interplay of molecular interactions. Identification of targets for multifactorial diseases such as SBC, the most frequent complication of breast and prostate cancers, is a challenge. Towards achieving our aim of identification of targets specific to SBC, we constructed a 'Cancer Genes Network', a representative protein interactome of cancer genes. Using graph theoretical methods, we obtained a set of key genes that are relevant for generic mechanisms of cancers and have a role in biological essentiality. We also compiled a curated dataset of 391 SBC genes from published literature which serves as a basis of ontological correlates of secondary bone cancer. Building on these results, we implement a strategy based on generic cancer genes, SBC genes and gene ontology enrichment method, to obtain a set of targets that are specific to bone metastasis. Through this study, we present an approach for probing one of the major complications in cancers, namely, metastasis. The results on genes that play generic roles in cancer phenotype, obtained by network analysis of 'Cancer Genes Network', have broader implications in understanding the role of molecular regulators in mechanisms of cancers. Specifically, our study provides a set of potential targets that are of ontological and regulatory relevance to secondary bone cancer.Comment: 54 pages (19 pages main text; 11 Figures; 26 pages of supplementary information). Revised after critical reviews. Accepted for Publication in PLoS ON