Human Protein Reference Database--2009 update

BioNet: an R-Package for the functional analysis of biological networks

MOTIVATION

Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules.

RESULTS

The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization.

AVAILABILITY

The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.

The power of protein interaction networks for associating genes with diseases

Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques.

Results: We assessed the utility of physical protein interactions for determining gene–disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required.

Availability: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet

Contact:carlk@cs.umd.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Construction and analysis of protein-protein interaction networks

Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.

Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state "signature". These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.

Extracting consistent knowledge from highly inconsistent cancer gene data sources

BACKGROUND

Hundreds of genes that are causally implicated in oncogenesis have been found and collected in various databases. For efficient application of these abundant but diverse data sources, it is of fundamental importance to evaluate their consistency.

RESULTS

First, we showed that the lists of cancer genes from some major data sources were highly inconsistent in terms of overlapping genes. In particular, most cancer genes accumulated in previous small-scale studies could not be rediscovered in current high-throughput genome screening studies. Then, based on a metric proposed in this study, we showed that most cancer gene lists from different data sources were highly functionally consistent. Finally, we extracted functionally consistent cancer genes from various data sources and collected them in our database F-Census.

CONCLUSIONS

Although they have very low gene overlapping, most cancer gene data sources are highly consistent at the functional level, which indicates that they can separately capture partial genes in a few key pathways associated with cancer. Our results suggest that the sample sizes currently used for cancer studies might be inadequate for consistently capturing individual cancer genes, but could be sufficient for finding a number of cancer genes that could represent functionally most cancer genes. The F-Census database provides biologists with a useful tool for browsing and extracting functionally consistent cancer genes from various data sources.

Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

MOTIVATION

Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values.

RESULTS

The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes.

CONTACT

dargenio@bmsr.usc.edu

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Role for protein-protein interaction databases in human genetics

Proteomics and the study of protein-protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein-protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein-protein interactions in human genetics and genetic epidemiology. Since protein-protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.

Biana: a software framework for compiling biological interactions and analyzing networks

Background

The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.

Results

We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php.

Conclusions

BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

Detection of characteristic sub pathway network for angiogenesis based on the comprehensive pathway network

Background

Pathways in biological system often cooperate with each other to function. Changes of interactions among pathways tightly associate with alterations in the properties and functions of the cell and hence alterations in the phenotype. So, the pathway interactions and especially their changes over time corresponding to specific phenotype are critical to understanding cell functions and phenotypic plasticity.

Methods

With prior-defined pathways and incorporated protein-protein interaction (PPI) data, we counted PPIs between corresponding gene sets of each pair of distinct pathways to construct a comprehensive pathway network. Then we proposed a novel concept, characteristic sub pathway network (CSPN), to realize the phenotype-specific pathway interactions. By adding gene expression data regarding a given phenotype, angiogenesis, active PPIs corresponding to stimulation of interleukin-1 (IL-1) and tumor necrosis factor α (TNF-α) on human umbilical vein endothelial cells (HUVECs) respectively were derived. Two kinds of CSPN, namely the static or the dynamic CSPN, were detected by counting active PPIs.

Results

A comprehensive pathway network containing 37 signalling pathways as nodes and 263 pathway interactions were obtained. Two phenotype-specific CSPNs for angiogenesis, corresponding to stimulation of IL-1 and TNF-α on HUVEC respectively, were addressed. From phenotype-specific CSPNs, a static CSPN involving interactions among B cell receptor, T cell receptor, Toll-like receptor, MAPK, VEGF, and ErbB signalling pathways, and a dynamic CSPN involving interactions among TGF-β, Wnt, p53 signalling pathways and cell cycle pathway, were detected for angiogenesis on HUVEC after stimulation of IL-1 and TNF-α respectively. We inferred that, in certain case, the static CSPN maintains related basic functions of the cells, whereas the dynamic CSPN manifests the cells' plastic responses to stimulus and therefore reflects the cells' phenotypic plasticity.

Conclusion

The comprehensive pathway network helps us realize the cooperative behaviours among pathways. Moreover, two kinds of potential CSPNs found in this work, the static CSPN and the dynamic CSPN, are helpful to deeply understand the specific function of HUVEC and its phenotypic plasticity in regard to angiogenesis.

An ontology-based search engine for protein-protein interactions

BACKGROUND

Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database.

RESULTS

We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions.

CONCLUSION

Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.

NetPath: a public resource of curated signal transduction pathways

NetPath, a novel community resource of curated human signaling pathways is presented and its utility demonstrated using immune signaling data.

We have developed NetPath as a resource of curated human signaling pathways. As an initial step, NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles. We anticipate NetPath to become a consolidated resource for human signaling pathways that should enable systems biology approaches.

Understanding protein phosphorylation on a systems level

Protein kinase phosphorylation is central to the regulation and control of protein and cellular function. Over the past decade, the development of many high-throughput approaches has revolutionized the understanding of protein phosphorylation and allowed rapid and unbiased surveys of phosphoproteins and phosphorylation events. In addition to this technological advancement, there have also been computational improvements; recent studies on network models of protein phosphorylation have provided many insights into the cellular processes and pathways regulated by phosphorylation. This article gives an overview of experimental and computational techniques for identifying and analyzing protein phosphorylation on a systems level.

Inferring the Sign of Kinase-Substrate Interactions by Combining Quantitative Phosphoproteomics with a Literature-Based Mammalian Kinome Network

Protein phosphorylation is a reversible post-translational modification commonly used by cell signaling networks to transmit information about the extracellular environment into intracellular organelles for the regulation of the activity and sorting of proteins within the cell. For this study we reconstructed a literature-based mammalian kinase-substrate network from several online resources. The interactions within this directed graph network connect kinases to their substrates, through specific phosphosites including kinasekinase regulatory interactions. However, the "signs" of links, activation or inhibition of the substrate upon phosphorylation, within this network are mostly unknown. Here we show how we can infer the "signs" indirectly using data from quantitative phosphoproteomics experiments applied to mammalian cells combined with the literature-based kinase-substrate network. Our inference method was able to predict the sign for 321 links and 153 phosphosites on 120 kinases, resulting in signed and directed subnetwork of mammalian kinase-kinase interactions. Such an approach can rapidly advance the reconstruction of cell signaling pathways and networks regulating mammalian cells.

Knowledge-based data analysis comes of age

The emergence of high-throughput technologies for measuring biological systems has introduced problems for data interpretation that must be addressed for proper inference. First, analysis techniques need to be matched to the biological system, reflecting in their mathematical structure the underlying behavior being studied. When this is not done, mathematical techniques will generate answers, but the values and reliability estimates may not accurately reflect the biology. Second, analysis approaches must address the vast excess in variables measured (e.g. transcript levels of genes) over the number of samples (e.g. tumors, time points), known as the 'large-p, small-n' problem. In large-p, small-n paradigms, standard statistical techniques generally fail, and computational learning algorithms are prone to overfit the data. Here we review the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models. We show that novel biological insights have been gained using these techniques.

GNCPro: navigate human genes and relationships through net-walking

The use of computational applications in biological research is significantly lagging behind other scientific research areas such as physics, mathematics, and geology; more in silico tools are needed. The increasing complexity of biological data makes it more and more difficult for scientists to verify their hypotheses and results against existing discoveries. GNCPro is a free data integration and visualization tool for gaining comprehensive overviews of such complicated biological knowledge. In particular, GNCPro warehouses and encodes biological information as binary relationships. When represented graphically, these binary relationships take on the form of edges that connect the genes and proteins, which are represented by nodes. By using distinguishing features such as colors, shape, and opacity, GNCPro provides a stimulating visual experience in which the user can quickly identify groups of genes by annotations and the types of relationships involved. GNCPro integrates human gene expressions, regulations, gene product modifications, and interactions into one platform while delivering a simple and powerful user interface for systems biology study.

AVAILABILITY

http://GNCPro.sabiosciences.com.

Network of Cancer Genes: a web resource to analyze duplicability, orthology and network properties of cancer genes

The Network of Cancer Genes (NCG) collects and integrates data on 736 human genes that are mutated in various types of cancer. For each gene, NCG provides information on duplicability, orthology, evolutionary appearance and topological properties of the encoded protein in a comprehensive version of the human protein-protein interaction network. NCG also stores information on all primary interactors of cancer proteins, thus providing a complete overview of 5357 proteins that constitute direct and indirect determinants of human cancer. With the constant delivery of results from the mutational screenings of cancer genomes, NCG represents a versatile resource for retrieving detailed information on particular cancer genes, as well as for identifying common properties of precompiled lists of cancer genes. NCG is freely available at: http://bio.ifom-ieo-campus.it/ncg.

Molecular networks for the study of TCM pharmacology

To target complex, multi-factorial diseases more effectively, there has been an emerging trend of multi-target drug development based on network biology, as well as an increasing interest in traditional Chinese medicine (TCM) that applies a more holistic treatment to diseases. Thousands of years' clinic practices in TCM have accumulated a considerable number of formulae that exhibit reliable in vivo efficacy and safety. However, the molecular mechanisms responsible for their therapeutic effectiveness are still unclear. The development of network-based systems biology has provided considerable support for the understanding of the holistic, complementary and synergic essence of TCM in the context of molecular networks. This review introduces available sources and methods that could be utilized for the network-based study of TCM pharmacology, proposes a workflow for network-based TCM pharmacology study, and presents two case studies on applying these sources and methods to understand the mode of action of TCM recipes.

Proteomic, functional and motif-based analysis of C-terminal Src kinase-interacting proteins

C-terminal Src kinase (Csk) that functions as an essential negative regulator of Src family tyrosine kinases (SFKs) interacts with tyrosine-phosphorylated molecules through its Src homology 2 (SH2) domain, allowing it targeting to the sites of SFKs and concomitantly enhancing its kinase activity. Identification of additional Csk-interacting proteins is expected to reveal potential signaling targets and previously undescribed functions of Csk. In this study, using a direct proteomic approach, we identified 151 novel potential Csk-binding partners, which are associated with a wide range of biological functions. Bioinformatics analysis showed that the majority of identified proteins contain one or several Csk-SH2 domain-binding motifs, indicating a potentially direct interaction with Csk. The interactions of Csk with four proteins (partitioning defective 3 (Par3), DDR1, SYK and protein kinase C iota) were confirmed using biochemical approaches and phosphotyrosine 1127 of Par3 C-terminus was proved to directly bind to Csk-SH2 domain, which was consistent with predictions from in silico analysis. Finally, immunofluorescence experiments revealed co-localization of Csk with Par3 in tight junction (TJ) in a tyrosine phosphorylation-dependent manner and overexpression of Csk, but not its SH2-domain mutant lacking binding to phosphotyrosine, promoted the TJ assembly in Madin-Darby canine kidney cells, implying the involvement of Csk-SH2 domain in regulating cellular TJs. In conclusion, the newly identified potential interacting partners of Csk provided new insights into its functional diversity in regulation of numerous cellular events, in addition to controlling the SFK activity.

Tailored graph ensembles as proxies or null models for real networks I: tools for quantifying structure

We study the tailoring of structured random graph ensembles to real networks, with the objective of generating precise and practical mathematical tools for quantifying and comparing network topologies macroscopically, beyond the level of degree statistics. Our family of ensembles can produce graphs with any prescribed degree distribution and any degree-degree correlation function, its control parameters can be calculated fully analytically, and as a result we can calculate (asymptotically) formulae for entropies and complexities, and for information-theoretic distances between networks, expressed directly and explicitly in terms of their measured degree distribution and degree correlations.

PDbase: a database of Parkinson's disease-related genes and genetic variation using substantia nigra ESTs

Background

Parkinson's disease (PD) is one of the most common neurodegenerative disorders, clinically characterized by impaired motor function. Since the etiology of PD is diverse and complex, many researchers have created PD-related research resources. However, resources for brain and PD studies are still lacking. Therefore, we have constructed a database of PD-related gene and genetic variations using the substantia nigra (SN) in PD and normal tissues. In addition, we integrated PD-related information from several resources.

Results

We collected the 6,130 SN expressed sequenced tags (ESTs) from brain SN normal tissues and PD patients SN tissues using full-cDNA library and normalized cDNA library construction methods from our previous study. The SN ESTs were clustered in 2,951 unigene clusters and assigned in 2,678 genes. We then found up-regulated 57 genes and down-regulated 48 genes by comparing normal and PD SN ESTs frequencies with over 0.9 cut-off probability of differential expression based on the Audic and Claverie method. In addition, we integrated disease-related information from public resources. To examine the characteristics of these PD-related genes, we analyzed alternative splicing events, single nucleotide polymorphism (SNP) markers located in the gene regions, repeat elements, gene regulation elements, and pathways and protein-protein interaction networks.

Conclusion

We constructed the PDbase database to capture the PD-related gene, genetic variation, and functional elements. This database contains 2,698 PD-related genes through ESTs discovered from human normal and PD patients SN tissues, and through integrating several public resources. PDbase provides the mitochondrion proteins, microRNA gene regulation elements, single nucleotide polymorphisms (SNPs) markers within PD-related gene structures, repeat elements, and pathways and networks with protein-protein interaction information. The PDbase information can aid in understanding the causation of PD. It is available at http://bioportal.kobic.re.kr/PDbase/. Supplementary data is available at http://bioportal.kobic.re.kr/PDbase/suppl.jsp

Human liver rate-limiting enzymes influence metabolic flux via branch points and inhibitors

Background

Rate-limiting enzymes, because of their relatively low velocity, are believed to influence metabolic flux in pathways. To investigate their regulatory role in metabolic networks, we look at the global organization and interactions between rate-limiting enzymes and compounds such as branch point metabolites and enzyme inhibitors in human liver.

Results

Based on 96 rate-limiting enzymes and 132 branch point compounds from human liver, we found that rate-limiting enzymes surrounded 76.5% of branch points. In a compound conversion network from human liver, the 128 branch points involved showed a dramatically higher average degree, betweenness centrality and closeness centrality as a whole. Nearly half of the in vivo inhibitors were products of rate-limiting enzymes, and covered 75.34% of the inhibited targets in metabolic inhibitory networks.

Conclusion

From global topological organization, rate-limiting enzymes as a whole surround most of the branch points; so they can influence the flux through branch points. Since nearly half of the in vivo enzyme inhibitors are produced by rate-limiting enzymes in human liver, these enzymes can initiate inhibitory regulation and then influence metabolic flux through their natural products.

MicroRNAs: potential regulators involved in human anencephaly

MicroRNAs (miRNAs) are posttranscriptional regulators of messenger RNA activity. Neural tube defects (NTDs) are severe congenital anomalies that substantially impact an infant's morbidity and mortality. The miRNAs are known to be dynamically regulated during neurodevelopment; their role in human NTDs, however, is still unknown. In this study, we show the presence of a specific miRNA expression profile from tissues of fetuses with anencephaly, one of the most severe forms of NTDs. Furthermore, we map the target genes of these miRNAs in the human genome. In comparison to healthy human fetal brain tissues, tissues from fetuses with anencephaly exhibited 97 down-regulated and 116 up-regulated miRNAs. The microarray findings were extended using real-time qRT-PCR for nine miRNAs. Specifically, of these validated miRNAs, miR-126, miR-198, and miR-451 were up-regulated, while miR-9, miR-212, miR-124, miR-138, and miR-103/107 were down-regulated in the tissues of fetuses with anencephaly. A bioinformatic analysis showed 881 potential target genes that are regulated by the validated miRNAs. Seventy-nine of these potential genes are involved in a protein interaction network. There were 6 co-occurrence annotations within the GOSlim process and 7 co-occurrence annotations within the GOSlim function found by GeneCodis 2.0. Our results suggest that miRNA dysregulation is possibly involved in the pathogenesis of anencephaly.

Functionally guided alignment of protein interaction networks for module detection

MOTIVATION

Functional module detection within protein interaction networks is a challenging problem due to the sparsity of data and presence of errors. Computational techniques for this task range from purely graph theoretical approaches involving single networks to alignment of multiple networks from several species. Current network alignment methods all rely on protein sequence similarity to map proteins across species.

RESULTS

Here we carry out network alignment using a protein functional similarity measure. We show that using functional similarity to map proteins across species improves network alignment in terms of functional coherence and overlap with experimentally verified protein complexes. Moreover, the results from functional similarity-based network alignment display little overlap (<15%) with sequence similarity-based alignment. Our combined approach integrating sequence and function-based network alignment alongside graph clustering properties offers a 200% increase in coverage of experimental datasets and comparable accuracy to current network alignment methods.

AVAILABILITY

Program binaries and source code is freely available at http://www.stats.ox.ac.uk/research/bioinfo/resources.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Network properties of complex human disease genes identified through genome-wide association studies

Background

Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias.

Principal findings

We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process.

Conclusions

This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

ELM: the status of the 2010 eukaryotic linear motif resource

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a 'Bar Code' format, which also displays known instances from homologous proteins through a novel 'Instance Mapper' protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation.

Algorithms for effective querying of compound graph-based pathway databases

BACKGROUND

Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.

RESULTS

Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool PATIKAweb (Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases.

CONCLUSION

The PATIKA Project Web site is http://www.patika.org. PATIKAweb version 2.1 is available at http://web.patika.org.

The platelet proteome

PURPOSE OF REVIEW

The proteome is the pool of proteins expressed at a given time and circumstance. The word 'proteomics' summarizes several technologies for visualization, quantitation and identification of these proteins. Recent advances in these techniques are helping to elucidate platelet processes which are relevant to bleeding and clotting disorders, transfusion medicine and regulation of angiogenesis.

RECENT FINDINGS

Over 1100 platelet proteins have been identified using proteomic techniques. Various subproteomes have been characterized, including platelet releasates (the 'secretome'), alpha and dense granules, membrane and cytoskeletal proteins, platelet-derived microparticles, and the platelet 'phosphoproteome'. Proteomic data about platelets have become increasingly available in integrated databases.

SUMMARY

Proteomic experiments in resting and activated platelets have identified novel signaling pathways and secreted proteins which may represent therapeutic targets, as well as potential cancer biomarkers.

HLungDB: an integrated database of human lung cancer research

The human lung cancer database (HLungDB) is a database with the integration of the lung cancer-related genes, proteins and miRNAs together with the corresponding clinical information. The main purpose of this platform is to establish a network of lung cancer-related molecules and to facilitate the mechanistic study of lung carcinogenesis. The entries describing the relationships between molecules and human lung cancer in the current release were extracted manually from literatures. Currently, we have collected 2585 genes and 212 miRNA with the experimental evidences involved in the different stages of lung carcinogenesis through text mining. Furthermore, we have incorporated the results from analysis of transcription factor-binding motifs, the promoters and the SNP sites for each gene. Since epigenetic alterations also play an important role in lung carcinogenesis, genes with epigenetic regulation were also included. We hope HLungDB will enrich our knowledge about lung cancer biology and eventually lead to the development of novel therapeutic strategies. HLungDB can be freely accessed at http://www.megabionet.org/bio/hlung.

PathBuilder--open source software for annotating and developing pathway resources

SUMMARY

We have developed PathBuilder, an open-source web application to annotate biological information pertaining to signaling pathways and to create web-based pathway resources. PathBuilder enables annotation of molecular events including protein-protein interactions, enzyme-substrate relationships and protein translocation events either manually or through automated importing of data from other databases. Salient features of PathBuilder include automatic validation of data formats, built-in modules for visualization of pathways, automated import of data from other pathway resources, export of data in several standard data exchange formats and an application programming interface for retrieving existing pathway datasets.

AVAILABILITY

PathBuilder is freely available for download at http://pathbuilder.sourceforge.net/ under the terms of GNU lesser general public license (LGPL: http://www.gnu.org/copyleft/lesser.html). The software is platform independent and has been tested on Windows and Linux platforms.

CONTACT

pandey@jhmi.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An integrative approach to reveal driver gene fusions from paired-end sequencing data in cancer

Cancer genomes contain many aberrant gene fusions-a few that drive disease and many more that are nonspecific passengers. We developed an algorithm (the concept signature or 'ConSig' score) that nominates biologically important fusions from high-throughput data by assessing their association with 'molecular concepts' characteristic of cancer genes, including molecular interactions, pathways and functional annotations. Copy number data supported candidate fusions and suggested a breakpoint principle for intragenic copy number aberrations in fusion partners. By analyzing lung cancer transcriptome sequencing and genomic data, we identified a novel R3HDM2-NFE2 fusion in the H1792 cell line. Lung tissue microarrays revealed 2 of 76 lung cancer patients with genomic rearrangement at the NFE2 locus, suggesting recurrence. Knockdown of NFE2 decreased proliferation and invasion of H1792 cells. Together, these results present a systematic analysis of gene fusions in cancer and describe key characteristics that assist in new fusion discovery.

Proteomic and phospho-proteomic profile of human platelets in basal, resting state: insights into integrin signaling

During atherogenesis and vascular inflammation quiescent platelets are activated to increase the surface expression and ligand affinity of the integrin αIIbβ3 via inside-out signaling. Diverse signals such as thrombin, ADP and epinephrine transduce signals through their respective GPCRs to activate protein kinases that ultimately lead to the phosphorylation of the cytoplasmic tail of the integrin αIIbβ3 and augment its function. The signaling pathways that transmit signals from the GPCR to the cytosolic domain of the integrin are not well defined. In an effort to better understand these pathways, we employed a combination of proteomic profiling and computational analyses of isolated human platelets. We analyzed ten independent human samples and identified a total of 1507 unique proteins in platelets. This is the most comprehensive platelet proteome assembled to date and includes 190 membrane-associated and 262 phosphorylated proteins, which were identified via independent proteomic and phospho-proteomic profiling. We used this proteomic dataset to create a platelet protein-protein interaction (PPI) network and applied novel contextual information about the phosphorylation step to introduce limited directionality in the PPI graph. This newly developed contextual PPI network computationally recapitulated an integrin signaling pathway. Most importantly, our approach not only provided insights into the mechanism of integrin αIIbβ3 activation in resting platelets but also provides an improved model for analysis and discovery of PPI dynamics and signaling pathways in the future.

Predicting meiotic pathways in human fetal oogenesis

Gene function prediction has proven valuable in formulating testable hypotheses. It is particularly useful for exploring biological processes that are experimentally intractable, such as meiotic initiation and progression in the human fetal ovary. In this study, we developed the first functional gene network for the human fetal ovary, HFOnet, by probabilistically integrating multiple genomic features using a naïve Bayesian model. We demonstrated that this network could accurately recapture known functional connections between genes, as well as predict new connections. Our findings suggest that known meiosis-specific genes (i.e., with functions only in meiotic processes in the germ cells) make either no or a few functional connections but are highly clustered with neighbor genes. In contrast, known nonspecific meiotic genes (i.e., with functions in both meiotic and nonmeiotic processes in the germ cells and somatic cells) exhibit numerous connections but low clustering coefficients, indicating their role as central modulators of diverse pathways, including those in meiosis. We also predicted novel genes that may be involved in meiotic initiation and DNA repair. This global functional network provides a much-needed framework for exploring gene functions and pathway components in early human female meiosis that are difficult to tackle by traditional in vivo mammalian genetics.

Identification of functionally related genes using data mining and data integration: a breast cancer case study

Background

The identification of the organisation and dynamics of molecular pathways is crucial for the understanding of cell function. In order to reconstruct the molecular pathways in which a gene of interest is involved in regulating a cell, it is important to identify the set of genes to which it interacts with to determine cell function. In this context, the mining and the integration of a large amount of publicly available data, regarding the transcriptome and the proteome states of a cell, are a useful resource to complement biological research.

Results

We describe an approach for the identification of genes that interact with each other to regulate cell function. The strategy relies on the analysis of gene expression profile similarity, considering large datasets of expression data. During the similarity evaluation, the methodology determines the most significant subset of samples in which the evaluated genes are highly correlated. Hence, the strategy enables the exclusion of samples that are not relevant for each gene pair analysed. This feature is important when considering a large set of samples characterised by heterogeneous experimental conditions where different pools of biological processes can be active across the samples. The putative partners of the studied gene are then further characterised, analysing the distribution of the Gene Ontology terms and integrating the protein-protein interaction (PPI) data. The strategy was applied for the analysis of the functional relationships of a gene of known function, Pyruvate Kinase, and for the prediction of functional partners of the human transcription factor TBX3. In both cases the analysis was done on a dataset composed by breast primary tumour expression data derived from the literature. Integration and analysis of PPI data confirmed the prediction of the methodology, since the genes identified to be functionally related were associated to proteins close in the PPI network. Two genes among the predicted putative partners of TBX3 (GLI3 and GATA3) were confirmed by in vivo binding assays (crosslinking immunoprecipitation, X-ChIP) in which the putative DNA enhancer sequence sites of GATA3 and GLI3 were found to be bound by the Tbx3 protein.

Conclusion

The presented strategy is demonstrated to be an effective approach to identify genes that establish functional relationships. The methodology identifies and characterises genes with a similar expression profile, through data mining and integrating data from publicly available resources, to contribute to a better understanding of gene regulation and cell function. The prediction of the TBX3 target genes GLI3 and GATA3 was experimentally confirmed.

Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Loss of post-translational modification sites in disease

Understanding and predicting molecular cause of disease is one of the major challenges for biology and medicine. One particular area of interest continues to be computational analyses of disease-associated amino acid substitutions. To this end, various studies have been performed to identify molecular functions disrupted by disease-causing mutations. Here, we investigate the influence of disease-associated mutations on post-translational modifications. In particular, we study the loss of modification target sites as a consequence of disease mutation. We find that about 5% of disease-associated mutations may affect known modification sites, either partially (4%) of fully (1%), compared to about 2% of putatively neutral polymorphisms. Most of the fifteen post-translational modification types analyzed were found to be disrupted at levels higher than expected by chance. Molecular functions and physiochemical properties at sites of disease mutation were also compared to those of neutral polymorphisms involved in the process of post-translational modification site disruption. Disease-associated mutations in the neighborhood of post-translationally modified sites were found to be enriched in mutations that change polarity, charge, and hydrophobicity of the wild-type amino acids. Overall, these results further suggest that disruption of modification sites is an important but not the major cause of human genetic disease.

Tyrosine phosphorylation of nuclear-membrane protein emerin by Src, Abl and other kinases

X-linked recessive Emery-Dreifuss muscular dystrophy (EDMD) is caused by loss of emerin, a nuclear-membrane protein with roles in nuclear architecture, gene regulation and signaling. Phosphoproteomic studies have identified 13 sites of tyrosine phosphorylation in emerin. We validated one study, confirming that emerin is hyper-tyrosine-phosphorylated in Her2-overexpressing cells. We discovered that non-receptor tyrosine kinases Src and Abl each phosphorylate emerin and a related protein, LAP2beta, directly. Src phosphorylated emerin specifically at Y59, Y74 and Y95; the corresponding triple Y-to-F (;FFF') mutation reduced tyrosine phosphorylation by approximately 70% in vitro and in vivo. Substitutions that removed a single hydroxyl moiety either decreased (Y19F, Y34, Y161F) or increased (Y4F) emerin binding to BAF in cells. Y19F, Y34F, Y161F and the FFF mutant also reduced recombinant emerin binding to BAF from HeLa lysates, demonstrating the involvement of both LEM-domain and distal phosphorylatable tyrosines in binding BAF. We conclude that emerin function is regulated by multiple tyrosine kinases, including Her2, Src and Abl, two of which (Her2, Src) regulate striated muscle. These findings suggest roles for emerin as a downstream effector and ;signal integrator' for tyrosine kinase signaling pathway(s) at the nuclear envelope.

Defining elastic fiber interactions by molecular fishing: an affinity purification and mass spectrometry approach

Deciphering interacting networks of the extracellular matrix is a major challenge. We describe an affinity purification and mass spectrometry strategy that has provided new insights into the molecular interactions of elastic fibers, essential extracellular assemblies that provide elastic recoil in dynamic tissues. Using cell culture models, we defined primary and secondary elastic fiber interaction networks by identifying molecular interactions with the elastic fiber molecules fibrillin-1, MAGP-1, fibulin-5, and lysyl oxidase. The sensitivity and validity of our method was confirmed by identification of known interactions with the bait proteins. Our study revealed novel extracellular protein interactions with elastic fiber molecules and delineated secondary interacting networks with fibronectin and heparan sulfate-associated molecules. This strategy is a novel approach to define the macromolecular interactions that sustain complex extracellular matrix assemblies and to gain insights into how they are integrated into their surrounding matrix.

PIAS proteins: pleiotropic interactors associated with SUMO

The interactions and functions of protein inhibitors of activated STAT (PIAS) proteins are not restricted to the signal transducers and activators of transcription (STATs), but PIAS1, -2, -3 and -4 interact with and regulate a variety of distinct proteins, especially transcription factors. Although the majority of PIAS-interacting proteins are prone to modification by small ubiquitin-related modifier (SUMO) proteins and the PIAS proteins have the capacity to promote the modification as RING-type SUMO ligases, they do not function solely as SUMO E3 ligases. Instead, their effects are often independent of their Siz/PIAS (SP)-RING finger, but dependent on their capability to noncovalently interact with SUMOs or DNA through their SUMO-interacting motif and scaffold attachment factor-A/B, acinus and PIAS domain, respectively. Here, we present an overview of the cellular regulation by PIAS proteins and propose that many of their functions are due to their capability to mediate and facilitate SUMO-linked protein assemblies.

Scaffolds: interaction platforms for cellular signalling circuits

Scaffold proteins influence cellular signalling by binding to multiple signalling enzymes, receptors or ion channels. Although normally devoid of catalytic activity, they have a big impact on controlling the flow of signalling information. By assembling signalling proteins into complexes, they play the part of signal processing hubs. As we learn more about the way signalling components are linked into natural signalling circuits, researchers are becoming interested in building non-natural signalling pathways to test our knowledge and/or to intentionally reprogram cellular behaviour. In this review, we discuss the role of scaffold proteins as efficient tools for assembling intracellular signalling complexes, both natural and artificial.

Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.

Identification of c-Src tyrosine kinase substrates in platelet-derived growth factor receptor signaling

c-Src non-receptor tyrosine kinase is an important component of the platelet-derived growth factor (PDGF) receptor signaling pathway. c-Src has been shown to mediate the mitogenic response to PDGF in fibroblasts. However, the exact components of PDGF receptor signaling pathway mediated by c-Src remain unclear. Here, we used stable isotope labeling with amino acids in cell culture (SILAC) coupled with mass spectrometry to identify Src-family kinase substrates involved in PDGF signaling. Using SILAC, we were able to detect changes in tyrosine phosphorylation patterns of 43 potential c-Src kinase substrates in PDGF receptor signaling. This included 23 known c-Src kinase substrates, of which 16 proteins have known roles in PDGF signaling while the remaining 7 proteins have not previously been implicated in PDGF receptor signaling. Importantly, our analysis also led to identification of 20 novel Src-family kinase substrates, of which 5 proteins were previously reported as PDGF receptor signaling pathway intermediates while the remaining 15 proteins represent novel signaling intermediates in PDGF receptor signaling. In validation experiments, we demonstrated that PDGF indeed induced the phosphorylation of a subset of candidate Src-family kinase substrates - Calpain 2, Eps15 and Trim28 - in a c-Src-dependent fashion.