The Biomolecular Interaction Network Database and related tools 2005 update

Pseudomonas Genome Database: facilitating user-friendly, comprehensive comparisons of microbial genomes

Pseudomonas aeruginosa is a well-studied opportunistic pathogen that is particularly known for its intrinsic antimicrobial resistance, diverse metabolic capacity, and its ability to cause life threatening infections in cystic fibrosis patients. The Pseudomonas Genome Database (http://www.pseudomonas.com) was originally developed as a resource for peer-reviewed, continually updated annotation for the Pseudomonas aeruginosa PAO1 reference strain genome. In order to facilitate cross-strain and cross-species genome comparisons with other Pseudomonas species of importance, we have now expanded the database capabilities to include all Pseudomonas species, and have developed or incorporated methods to facilitate high quality comparative genomics. The database contains robust assessment of orthologs, a novel ortholog clustering method, and incorporates five views of the data at the sequence and annotation levels (Gbrowse, Mauve and custom views) to facilitate genome comparisons. A choice of simple and more flexible user-friendly Boolean search features allows researchers to search and compare annotations or sequences within or between genomes. Other features include more accurate protein subcellular localization predictions and a user-friendly, Boolean searchable log file of updates for the reference strain PAO1. This database aims to continue to provide a high quality, annotated genome resource for the research community and is available under an open source license.

STRING 8--a global view on proteins and their functional interactions in 630 organisms

Functional partnerships between proteins are at the core of complex cellular phenotypes, and the networks formed by interacting proteins provide researchers with crucial scaffolds for modeling, data reduction and annotation. STRING is a database and web resource dedicated to protein–protein interactions, including both physical and functional interactions. It weights and integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections, thus acting as a meta-database that maps all interaction evidence onto a common set of genomes and proteins. The most important new developments in STRING 8 over previous releases include a URL-based programming interface, which can be used to query STRING from other resources, improved interaction prediction via genomic neighborhood in prokaryotes, and the inclusion of protein structures. Version 8.0 of STRING covers about 2.5 million proteins from 630 organisms, providing the most comprehensive view on protein–protein interactions currently available. STRING can be reached at http://string-db.org/.

Proteome-wide prediction of PKA phosphorylation sites in eukaryotic kingdom

Protein phosphorylation is one of the most essential post-translational modifications (PTMs), and orchestrates a variety of cellular functions and processes. Besides experimental studies, numerous computational predictors implemented in various algorithms have been developed for phosphorylation sites prediction. However, large-scale predictions of kinase-specific phosphorylation sites have not been successfully pursued and remained to be a great challenge. In this work, we raised a "kiss farewell" model and conducted a high-throughput prediction of cAMP-dependent kinase (PKA) phosphorylation sites. Since a protein kinase (PK) should at least "kiss" its substrates and then run away, we proposed a PKA-binding protein to be a potential PKA substrate if at least one PKA site was predicted. To improve the prediction specificity, we reduced false positive rate (FPR) less than 1% when the cut-off value was set as 4. Successfully, we predicted 1387, 630, 568 and 912 potential PKA sites from 410, 217, 173 and 260 PKA-interacting proteins in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens, respectively. Most of these potential phosphorylation sites remained to be experimentally verified. In addition, we detected two sites in one of PKA regulatory subunits to be conserved in eukaryotes as potentially ancient regulatory signals. Our prediction results provide an excellent resource for delineating PKA-mediated signaling pathways and their system integration underlying cellular dynamics and plasticity.

The interactome: predicting the protein-protein interactions in cells

The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.

Network-based model weighting to detect multiple loci influencing complex diseases

For genome-wide association studies, it has been increasingly recognized that the popular locus-by-locus search for DNA variants associated with disease susceptibility may not be effective, especially when there are interactions between or among multiple loci, for which a multi-loci search strategy may be more productive. However, even if computationally feasible, a genome-wide search over all possible multiple loci requires exploring a huge model space and making costly adjustment for multiple testing, leading to reduced statistical power. On the other hand, there are accumulating data suggesting that protein products of many disease-causing genes tend to interact with each other, or cluster in the same biological pathway. To incorporate this prior knowledge and existing data on gene networks, we propose a gene network-based method to improve statistical power over that of the exhaustive search by giving higher weights to models involving genes nearby in a network. We use simulated data under realistic scenarios, including a large-scale human protein-protein interaction network and 23 known ataxia-causing genes, to demonstrate potential gain by our proposed method when disease-genes are clustered in a network.

Structure of a signal transduction regulator, RACK1, from Arabidopsis thaliana

The receptor for activated C-kinase 1 (RACK1) is a highly conserved WD40 repeat scaffold protein found in a wide range of eukaryotic species from Chlamydymonas to plants and humans. In tissues of higher mammals, RACK1 is ubiquitously expressed and has been implicated in diverse signaling pathways involving neuropathology, cellular stress, protein translation, and developmental processes. RACK1 has established itself as a scaffold protein through physical interaction with a myriad of signaling proteins ranging from kinases, phosphatases, ion channels, membrane receptors, G proteins, IP3 receptor, and with widely conserved structural proteins associated with the ribosome. In the plant Arabidopsis thaliana, RACK1A is implicated in diverse developmental and environmental stress pathways. Despite the functional conservation of RACK1-mediated protein-protein interaction-regulated signaling modes, the structural basis of such interactions is largely unknown. Here we present the first crystal structure of a RACK1 protein, RACK1 isoform A from Arabidopsis thaliana, at 2.4 A resolution, as a C-terminal fusion of the maltose binding protein. The structure implicates highly conserved surface residues that could play critical roles in protein-protein interactions and reveals the surface location of proposed post-transcriptionally modified residues. The availability of this structure provides a structural basis for dissecting RACK1-mediated cellular signaling mechanisms in both plants and animals.

iRefIndex: a consolidated protein interaction database with provenance

Background

Interaction data for a given protein may be spread across multiple databases. We set out to create a unifying index that would facilitate searching for these data and that would group together redundant interaction data while recording the methods used to perform this grouping.

Results

We present a method to generate a key for a protein interaction record and a key for each participant protein. These keys may be generated by anyone using only the primary sequence of the proteins, their taxonomy identifiers and the Secure Hash Algorithm. Two interaction records will have identical keys if they refer to the same set of identical protein sequences and taxonomy identifiers. We define records with identical keys as a redundant group. Our method required that we map protein database references found in interaction records to current protein sequence records. Operations performed during this mapping are described by a mapping score that may provide valuable feedback to source interaction databases on problematic references that are malformed, deprecated, ambiguous or unfound. Keys for protein participants allow for retrieval of interaction information independent of the protein references used in the original records.

Conclusion

We have applied our method to protein interaction records from BIND, BioGrid, DIP, HPRD, IntAct, MINT, MPact, MPPI and OPHID. The resulting interaction reference index is provided in PSI-MITAB 2.5 format at . This index may form the basis of alternative redundant groupings based on gene identifiers or near sequence identity groupings.

A genomewide functional network for the laboratory mouse

Establishing a functional network is invaluable to our understanding of gene function, pathways, and systems-level properties of an organism and can be a powerful resource in directing targeted experiments. In this study, we present a functional network for the laboratory mouse based on a Bayesian integration of diverse genetic and functional genomic data. The resulting network includes probabilistic functional linkages among 20,581 protein-coding genes. We show that this network can accurately predict novel functional assignments and network components and present experimental evidence for predictions related to Nanog homeobox (Nanog), a critical gene in mouse embryonic stem cell pluripotency. An analysis of the global topology of the mouse functional network reveals multiple biologically relevant systems-level features of the mouse proteome. Specifically, we identify the clustering coefficient as a critical characteristic of central modulators that affect diverse pathways as well as genes associated with different phenotype traits and diseases. In addition, a cross-species comparison of functional interactomes on a genomic scale revealed distinct functional characteristics of conserved neighborhoods as compared to subnetworks specific to higher organisms. Thus, our global functional network for the laboratory mouse provides the community with a key resource for discovering protein functions and novel pathway components as well as a tool for exploring systems-level topological and evolutionary features of cellular interactomes. To facilitate exploration of this network by the biomedical research community, we illustrate its application in function and disease gene discovery through an interactive, Web-based, publicly available interface at http://mouseNET.princeton.edu.

Functionally related proteins interact in diverse ways to carry out biological processes, and each protein often participates in multiple pathways. Proteins are therefore organized into a complex network through which different functions of the cell are carried out. An accurate description of such a network is invaluable to our understanding of both the system-level features of a cell and those of an individual biological process. In this study, we used a probabilistic model to combine information from diverse genome-scale studies as well as individual investigations to generate a global functional network for mouse. Our analysis of the global topology of this network reveals biologically relevant systems-level characteristics of the mouse proteome, including conservation of functional neighborhoods and network features characteristic of known disease genes and key transcriptional regulators. We have made this network publicly available for search and dynamic exploration by researchers in the community. Our Web interface enables users to easily generate hypotheses regarding potential functional roles of uncharacterized proteins, investigate possible links between their proteins of interest and disease, and identify new players in specific biological processes.

Determining nucleolar association from sequence by leveraging protein-protein interactions

Controlled intra-nuclear organization of proteins is critical for sustaining correct function of the cell. Proteins and RNA are transported by passive diffusion and associate with compartments by virtue of diverse molecular interactions--presenting a challenging problem for data-driven model building. An increasing inventory of proteins with known intra-nuclear destination and proliferation of molecular interaction data motivate an integrative method, leveraging the existing evidence to build accurate models of intranuclear trafficking. Kernel canonical correlation analysis (KCCA) enables the construction of predictors based on genomic sequence data, but leverages other knowledge sources during training. The approach specifically involves the induction of protein sequence features and relations most pertinent to the recovery of nucleolar associated protein-protein interactions. With success rates of about 78%, the classification of nucleolar association from KCCA-induced features surpasses that of baseline approaches. We observe that the coalescence of protein-protein interaction data with sequence data enhances the prediction of highly interconnected, key ribosomal and RNA-related nucleolar proteins. For supplementary material, see www.itee.uq.edu.au/~ pprowler/nucleoli.

The use of Gene Ontology terms for predicting highly-connected 'hub' nodes in protein-protein interaction networks

BACKGROUND

Protein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) 12. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets.Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions.

RESULTS

A hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species.

CONCLUSION

The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.

Knowledge environments representing molecular entities for the virtual physiological human

In essence, the virtual physiological human (VPH) is a multiscale representation of human physiology spanning from the molecular level via cellular processes and multicellular organization of tissues to complex organ function. The different scales of the VPH deal with different entities, relationships and processes, and in consequence the models used to describe and simulate biological functions vary significantly. Here, we describe methods and strategies to generate knowledge environments representing molecular entities that can be used for modelling the molecular scale of the VPH. Our strategy to generate knowledge environments representing molecular entities is based on the combination of information extraction from scientific text and the integration of information from biomolecular databases. We introduce @neuLink, a first prototype of an automatically generated, disease-specific knowledge environment combining biomolecular, chemical, genetic and medical information. Finally, we provide a perspective for the future implementation and use of knowledge environments representing molecular entities for the VPH.

MPI-LIT: a literature-curated dataset of microbial binary protein--protein interactions

Prokaryotic protein-protein interactions are underrepresented in currently available databases. Here, we describe a 'gold standard' dataset (MPI-LIT) focusing on microbial binary protein-protein interactions and associated experimental evidence that we have manually curated from 813 abstracts and full texts that were selected from an initial set of 36 852 abstracts. The MPI-LIT dataset comprises 1237 experimental descriptions that describe a non-redundant set of 746 interactions of which 659 (88%) are not reported in public databases. To estimate the curation quality, we compared our dataset with a union of microbial interaction data from IntAct, DIP, BIND and MINT. Among common abstracts, we achieve a sensitivity of up to 66% for interactions and 75% for experimental methods. Compared with these other datasets, MPI-LIT has the lowest fraction of interaction experiments per abstract (0.9) and the highest coverage of strains (92) and scientific articles (813). We compared methods that evaluate functional interactions among proteins (such as genomic context or co-expression) which are implemented in the STRING database. Most of these methods discriminate well between functionally relevant protein interactions (MPI-LIT) and high-throughput data.

AVAILABILITY

http://www.jcvi.org/mpidb/interaction.php?dbsource=MPI-LIT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

InnateDB: facilitating systems-level analyses of the mammalian innate immune response

Although considerable progress has been made in dissecting the signaling pathways involved in the innate immune response, it is now apparent that this response can no longer be productively thought of in terms of simple linear pathways. InnateDB (www.innatedb.ca) has been developed to facilitate systems-level analyses that will provide better insight into the complex networks of pathways and interactions that govern the innate immune response. InnateDB is a publicly available, manually curated, integrative biology database of the human and mouse molecules, experimentally verified interactions and pathways involved in innate immunity, along with centralized annotation on the broader human and mouse interactomes. To date, more than 3500 innate immunity-relevant interactions have been contextually annotated through the review of 1000 plus publications. Integrated into InnateDB are novel bioinformatics resources, including network visualization software, pathway analysis, orthologous interaction network construction and the ability to overlay user-supplied gene expression data in an intuitively displayed molecular interaction network and pathway context, which will enable biologists without a computational background to explore their data in a more systems-oriented manner.

Identifying components of complexes

Identifying and analyzing components of complexes is essential to understand the activities and organization of the cell. Moreover, it provides additional information on the possible function of proteins involved in these complexes. Two bioinformatics approaches are usually used for this purpose. The first is based on the identification, by clustering algorithms, of full or densely connected sub-graphs in protein-protein interaction networks derived from experimental sources that might represent complexes. The second approach consists of the integration of genomic and proteomic data by using Bayesian networks or decision trees. This approach is based on the hypothesis that proteins involved in a complex usually share common properties.

Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

An integrative analysis combining genetic interactions and protein interactions can be used to identify candidate genes/proteins for type 1 diabetes and other complex diseases.

We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases.

Bioinformatics: databasing and gene annotation

"Omics" experiments amass large amounts of data requiring integration of several data sources for data interpretation. For instance, microarray, metabolomic, and proteomic experiments may at most yield a list of active genes, metabolites, or proteins, respectively. More generally, the experiments yield active features that represent subsequences of the gene, a chemical shift within a complex mixture, or peptides, respectively. Thus, in the best-case scenario, the investigator is left to identify the functional significance, but more likely the investigator must first identify the larger context of the feature (e.g., which gene, metabolite, or protein is being represented by the feature). To completely annotate function, several different databases are required, including sequence, genome, gene function, protein, and protein interaction databases. Because of the limited coverage of some microarrays or experiments, biological data repositories may be consulted, in the case of microarrays, to complement results. Many of the data sources and databases available for gene function characterization, including tools from the National Center for Biotechnology Information, Gene Ontology, and UniProt, are discussed.

Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genome-wide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene-gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available.

Not all scale-free networks are born equal: the role of the seed graph in PPI network evolution

The (asymptotic) degree distributions of the best-known "scale-free" network models are all similar and are independent of the seed graph used; hence, it has been tempting to assume that networks generated by these models are generally similar. In this paper, we observe that several key topological features of such networks depend heavily on the specific model and the seed graph used. Furthermore, we show that starting with the "right" seed graph (typically a dense subgraph of the protein-protein interaction network analyzed), the duplication model captures many topological features of publicly available protein-protein interaction networks very well.

Conservation and topology of protein interaction networks under duplication-divergence evolution

Genomic duplication-divergence processes are the primary source of new protein functions and thereby contribute to the evolutionary expansion of functional molecular networks. Yet, it is still unclear to what extent such duplication-divergence processes also restrict by construction the emerging properties of molecular networks, regardless of any specific cellular functions. We address this question, here, focusing on the evolution of protein-protein interaction (PPI) networks. We solve a general duplication-divergence model, based on the statistically necessary deletions of protein-protein interactions arising from stochastic duplications at various genomic scales, from single-gene to whole-genome duplications. Major evolutionary scenarios are shown to depend on two global parameters only: (i) a protein conservation index (M), which controls the evolutionary history of PPI networks, and (ii) a distinct topology index (M') controlling their resulting structure. We then demonstrate that conserved, nondense networks, which are of prime biological relevance, are also necessarily scale-free by construction, irrespective of any evolutionary variations or fluctuations of the model parameters. It is shown to result from a fundamental linkage between individual protein conservation and network topology under general duplication-divergence evolution. By contrast, we find that conservation of network motifs with two or more proteins cannot be indefinitely preserved under general duplication-divergence evolution (independently from any network rewiring dynamics), in broad agreement with empirical evidence between phylogenetically distant species. All in all, these evolutionary constraints, inherent to duplication-divergence processes, appear to have largely controlled the overall topology and scale-dependent conservation of PPI networks, regardless of any specific biological function.

Prediction of protein-protein interactions using protein signature profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain interactions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as “interacting”. In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis elegans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area under the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs increased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on average 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.

Assessing the functional structure of genomic data

MOTIVATION

The availability of genome-scale data has enabled an abundance of novel analysis techniques for investigating a variety of systems-level biological relationships. As thousands of such datasets become available, they provide an opportunity to study high-level associations between cellular pathways and processes. This also allows the exploration of shared functional enrichments between diverse biological datasets, and it serves to direct experimenters to areas of low data coverage or with high probability of new discoveries.

RESULTS

We analyze the functional structure of Saccharomyces cerevisiae datasets from over 950 publications in the context of over 140 biological processes. This includes a coverage analysis of biological processes given current high-throughput data, a data-driven map of associations between processes, and a measure of similar functional activity between genome-scale datasets. This uncovers subtle gene expression similarities in three otherwise disparate microarray datasets due to a shared strain background. We also provide several means of predicting areas of yeast biology likely to benefit from additional high-throughput experimental screens.

AVAILABILITY

Predictions are provided in supplementary tables; software and additional data are available from the authors by request.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MPIDB: the microbial protein interaction database

Summary: The microbial protein interaction database (MPIDB) aims to collect and provide all known physical microbial interactions. Currently, 22 530 experimentally determined interactions among proteins of 191 bacterial species/strains can be browsed and downloaded. These microbial interactions have been manually curated from the literature or imported from other databases (IntAct, DIP, BIND, MINT) and are linked to 24 060 experimental evidences (PubMed ID, PSI-MI methods). In contrast to these databases, interactions in MPIDB are further supported by 8150 additional evidences based on interaction conservation, co-purification and 3D domain contacts (iPfam, 3did).

Availability:http://www.jcvi.org/mpidb/

Contact:jgoll@jcvi.org

Semantic role labeling for protein transport predicates

BACKGROUND

Automatic semantic role labeling (SRL) is a natural language processing (NLP) technique that maps sentences to semantic representations. This technique has been widely studied in the recent years, but mostly with data in newswire domains. Here, we report on a SRL model for identifying the semantic roles of biomedical predicates describing protein transport in GeneRIFs - manually curated sentences focusing on gene functions. To avoid the computational cost of syntactic parsing, and because the boundaries of our protein transport roles often did not match up with syntactic phrase boundaries, we approached this problem with a word-chunking paradigm and trained support vector machine classifiers to classify words as being at the beginning, inside or outside of a protein transport role.

RESULTS

We collected a set of 837 GeneRIFs describing movements of proteins between cellular components, whose predicates were annotated for the semantic roles AGENT, PATIENT, ORIGIN and DESTINATION. We trained these models with the features of previous word-chunking models, features adapted from phrase-chunking models, and features derived from an analysis of our data. Our models were able to label protein transport semantic roles with 87.6% precision and 79.0% recall when using manually annotated protein boundaries, and 87.0% precision and 74.5% recall when using automatically identified ones.

CONCLUSION

We successfully adapted the word-chunking classification paradigm to semantic role labeling, applying it to a new domain with predicates completely absent from any previous studies. By combining the traditional word and phrasal role labeling features with biomedical features like protein boundaries and MEDPOST part of speech tags, we were able to address the challenges posed by the new domain data and subsequently build robust models that achieved F-measures as high as 83.1. This system for extracting protein transport information from GeneRIFs performs well even with proteins identified automatically, and is therefore more robust than the rule-based methods previously used to extract protein transport roles.

Detection, imputation, and association analysis of small deletions and null alleles on oligonucleotide arrays

Copy-number variation (CNV) is a major contributor to human genetic variation. Recently, CNV associations with human disease have been reported. Many genome-wide association (GWA) studies in complex diseases have been performed with sets of biallelic single-nucleotide polymorphisms (SNPs), but the available CNV methods are still limited. We present a new method (TriTyper) that can infer genotypes in case-control data sets for deletion CNVs, or SNPs with an extra, untyped allele at a high-resolution single SNP level. By accounting for linkage disequilibrium (LD), as well as intensity data, calling accuracy is improved. Analysis of 3102 unrelated individuals with European descent, genotyped with Illumina Infinium BeadChips, resulted in the identification of 1880 SNPs with a common untyped allele, and these SNPs are in strong LD with neighboring biallelic SNPs. Simulations indicate our method has superior power to detect associations compared to biallelic SNPs that are in LD with these SNPs, yet without increasing type I errors, as shown in a GWA analysis in celiac disease. Genotypes for 1204 triallelic SNPs could be fully imputed, with only biallelic-genotype calls, permitting association analysis of these SNPs in many published data sets. We estimate that 682 of the 1655 unique loci reflect deletions; this is on average 99 deletions per individual, four times greater than those detected by other methods. Whereas the identified loci are strongly enriched for known deletions, 61% have not been reported before. Genes overlapping with these loci more often have paralogs (p = 0.006) and biologically interact with fewer genes than expected (p = 0.004).

Beyond tissueInfo: functional prediction using tissue expression profile similarity searches

We present and validate tissue expression profile similarity searches (TEPSS), a computational approach to identify transcripts that share similar tissue expression profiles to one or more transcripts in a group of interest. We evaluated TEPSS for its ability to discriminate between pairs of transcripts coding for interacting proteins and non-interacting pairs. We found that ordering protein-protein pairs by TEPSS score produces sets significantly enriched in reported pairs of interacting proteins [interacting versus non-interacting pairs, Odds-ratio (OR) = 157.57, 95% confidence interval (CI) (36.81-375.51) at 1% coverage, employing a large dataset of about 50 000 human protein interactions]. When used with multiple transcripts as input, we find that TEPSS can predict non-obvious members of the cytosolic ribosome. We used TEPSS to predict S-nitrosylation (SNO) protein targets from a set of brain proteins that undergo SNO upon exposure to physiological levels of S-nitrosoglutathione in vitro. While some of the top TEPSS predictions have been validated independently, several of the strongest SNO TEPSS predictions await experimental validation. Our data indicate that TEPSS is an effective and flexible approach to functional prediction. Since the approach does not use sequence similarity, we expect that TEPSS will be useful for various gene discovery applications. TEPSS programs and data are distributed at http://icb.med.cornell.edu/crt/tepss/index.xml.

Searching, viewing, and visualizing data in the Biomolecular Interaction Network Database (BIND)

The Biomolecular Interaction Network Database (BIND) comprises data from peer-reviewed literature and direct submissions. BIND's data model was the first of its kind to be peer-reviewed prior to database development, and is now a mature standard data format spanning molecular interactions, small molecule chemical reactions, and interfaces from three-dimensional structures, pathways, and genetic interaction networks. BIND supports additional file formats to achieve compatibility with other database efforts, including the HUPO PSI Level 2. BIND's latest software spans over 2000 metadata fields and is constructed using the Java Enterprise Systems software platform. Protocols are provided for searching BIND via the Internet, as well as for viewing and exporting search results or individual records. Furthermore, a protocol is provided for visualizing biomolecular interactions within BIND or for transferring this information to the visualization tools Cytoscape and Cn3D.

GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy

Identification of protein phosphorylation sites with their cognate protein kinases (PKs) is a key step to delineate molecular dynamics and plasticity underlying a variety of cellular processes. Although nearly 10 kinase-specific prediction programs have been developed, numerous PKs have been casually classified into subgroups without a standard rule. For large scale predictions, the false positive rate has also never been addressed. In this work, we adopted a well established rule to classify PKs into a hierarchical structure with four levels, including group, family, subfamily, and single PK. In addition, we developed a simple approach to estimate the theoretically maximal false positive rates. The on-line service and local packages of the GPS (Group-based Prediction System) 2.0 were implemented in Java with the modified version of the Group-based Phosphorylation Scoring algorithm. As the first stand alone software for predicting phosphorylation, GPS 2.0 can predict kinase-specific phosphorylation sites for 408 human PKs in hierarchy. A large scale prediction of more than 13,000 mammalian phosphorylation sites by GPS 2.0 was exhibited with great performance and remarkable accuracy. Using Aurora-B as an example, we also conducted a proteome-wide search and provided systematic prediction of Aurora-B-specific substrates including protein-protein interaction information. Thus, the GPS 2.0 is a useful tool for predicting protein phosphorylation sites and their cognate kinases and is freely available on line.

An integrated genetic, genomic and systems approach defines gene networks regulated by the interaction of light and carbon signaling pathways in Arabidopsis

BACKGROUND

Light and carbon are two important interacting signals affecting plant growth and development. The mechanism(s) and/or genes involved in sensing and/or mediating the signaling pathways involving these interactions are unknown. This study integrates genetic, genomic and systems approaches to identify a genetically perturbed gene network that is regulated by the interaction of carbon and light signaling in Arabidopsis.

RESULTS

Carbon and light insensitive (cli) mutants were isolated. Microarray data from cli186 is analyzed to identify the genes, biological processes and gene networks affected by the integration of light and carbon pathways. Analysis of this data reveals 966 genes regulated by light and/or carbon signaling in wild-type. In cli186, 216 of these light/carbon regulated genes are misregulated in response to light and/or carbon treatments where 78% are misregulated in response to light and carbon interactions. Analysis of the gene lists show that genes in the biological processes "energy" and "metabolism" are over-represented among the 966 genes regulated by carbon and/or light in wild-type, and the 216 misregulated genes in cli186. To understand connections among carbon and/or light regulated genes in wild-type and the misregulated genes in cli186, the microarray data is interpreted in the context of metabolic and regulatory networks. The network created from the 966 light/carbon regulated genes in wild-type, reveals that cli186 is affected in the light and/or carbon regulation of a network of 60 connected genes, including six transcription factors. One transcription factor, HAT22 appears to be a regulatory "hub" in the cli186 network as it shows regulatory connections linking a metabolic network of genes involved in "amino acid metabolism", "C-compound/carbohydrate metabolism" and "glycolysis/gluconeogenesis".

CONCLUSION

The global misregulation of gene networks controlled by light and carbon signaling in cli186 indicates that it represents one of the first Arabidopsis mutants isolated that is specifically disrupted in the integration of both carbon and light signals to control the regulation of metabolic, developmental and regulatory genes. The network analysis of misregulated genes suggests that CLI186 acts to integrate light and carbon signaling interactions and is a master regulator connecting the regulation of a host of downstream metabolic and regulatory processes.

Walking the interactome for prioritization of candidate disease genes

The identification of genes associated with hereditary disorders has contributed to improving medical care and to a better understanding of gene functions, interactions, and pathways. However, there are well over 1500 Mendelian disorders whose molecular basis remains unknown. At present, methods such as linkage analysis can identify the chromosomal region in which unknown disease genes are located, but the regions could contain up to hundreds of candidate genes. In this work, we present a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks. We tested our method on 110 disease-gene families with a total of 783 genes and achieved an area under the ROC curve of up to 98% on simulated linkage intervals of 100 genes surrounding the disease gene, significantly outperforming previous methods based on local distance measures. Our results not only provide an improved tool for positional-cloning projects but also add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves.

Resolving the network of cell signaling pathways using the evolving yeast two-hybrid system

In 1983, while investigators had identified a few human proteins as important regulators of specific biological outcomes, how these proteins acted in the cell was essentially unknown in almost all cases. Twenty-five years later, our knowledge of the mechanistic basis of protein action has been transformed by our increasingly detailed understanding of protein-protein interactions, which have allowed us to define cellular machines. The advent of the yeast two-hybrid (Y2H) system in 1989 marked a milestone in the field of proteomics. Exploiting the modular nature of transcription factors, the Y2H system allows facile measurement of the activation of reporter genes based on interactions between two chimeric or "hybrid" proteins of interest. After a decade of service as a leading platform for individual investigators to use in exploring the interaction properties of interesting target proteins, the Y2H system has increasingly been applied in high-throughput applications intended to map genome-scale protein-protein interactions for model organisms and humans. Although some significant technical limitations apply, Y2H has made a great contribution to our general understanding of the topology of cellular signaling networks.

p107 acts as a tumor suppressor in pRb-deficient epidermis

The specific deletion of Rb gene in epidermis leads to altered proliferation and differentiation, but not to the development of spontaneous tumors. Our previous data have demonstrated the existence of a functional compensation of Rb loss by Rbl1 (p107) in as the phenotypic differences with respect to controls are intensified. However, the possible evolution of this aggravated phenotype, in particular in relationship with tumorigenesis, has not been evaluated due to the premature death of the double deficient mice. We have now investigated whether p107 can also act as a tumor suppressor in pRb-deficient epidermis using different experimental approaches. We found spontaneous tumor development in doubly-deficient skin grafts. Moreover, Rb-deficient keratinocytes are susceptible to Ha-ras-induced transformation, and this susceptibility is enhanced by p107 loss. Further functional analyses, including microarray gene expression profiling, indicated that the loss of p107, in the absence of pRb, produces the reduction of p53-dependent pro-apoptotic signals. Overall, our data demonstrate that p107 behaves as a tumor suppressor in epidermis in the absence of pRb and suggest novel tumor-suppressive roles for p107 in the context of functional p53 and activated Ras.

Predicting cancer involvement of genes from heterogeneous data

Background

Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.

Results

We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature.

Conclusion

Our approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.

The MoVIN server for the analysis of protein interaction networks

BACKGROUND

Protein-protein interactions are at the basis of most cellular processes and crucial for many bio-technological applications. During the last few years the development of high-throughput technologies has produced several large-scale protein-protein interaction data sets for various organisms. It is important to develop tools for dissecting their content and analyse the information they embed by data-integration and computational methods.

RESULTS

Interactions can be mediated by the presence of specific features, such as motifs, surface patches and domains. The co-occurrence of these features on proteins interacting with the same protein can indicate mutually exclusive interactions and, therefore, can be used for inferring the involvement of the proteins in common biological processes. We present here a publicly available server that allows the user to investigate protein interaction data in light of other biological information, such as their sequences, presence of specific domains, process and component ontologies. The server can be effectively used to construct a high-confidence set of mutually exclusive interactions by identifying similar features in groups of proteins sharing a common interaction partner. As an example, we describe here the identification of common motifs, function, cellular localization and domains in different datasets of yeast interactions.

CONCLUSIONS

The server can be used to analyse user-supplied datasets, it contains pre-processed data for four yeast Protein Protein interaction datasets and the results of their statistical analysis. These show that the presence of common motifs in proteins interacting with the same partner is a valuable source of information, it can be used to investigate the properties of the interacting proteins and provides information that can be effectively integrated with other sources. As more experimental interaction data become available, this tool will become more and more useful to gain a more detailed picture of the interactome.

Topological comparison of methods for predicting transcriptional cooperativity in yeast

Background

The cooperative interaction between transcription factors has a decisive role in the control of the fate of the eukaryotic cell. Computational approaches for characterizing cooperative transcription factors in yeast, however, are based on different rationales and provide a low overlap between their results. Because the wealth of information contained in protein interaction networks and regulatory networks has proven highly effective in elucidating functional relationships between proteins, we compared different sets of cooperative transcription factor pairs (predicted by four different computational methods) within the frame of those networks.

Results

Our results show that the overlap between the sets of cooperative transcription factors predicted by the different methods is low yet significant. Cooperative transcription factors predicted by all methods are closer and more clustered in the protein interaction network than expected by chance. On the other hand, members of a cooperative transcription factor pair neither seemed to regulate each other nor shared similar regulatory inputs, although they do regulate similar groups of target genes.

Conclusion

Despite the different definitions of transcriptional cooperativity and the different computational approaches used to characterize cooperativity between transcription factors, the analysis of their roles in the framework of the protein interaction network and the regulatory network indicates a common denominator for the predictions under study. The knowledge of the shared topological properties of cooperative transcription factor pairs in both networks can be useful not only for designing better prediction methods but also for better understanding the complexities of transcriptional control in eukaryotes.

Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics

Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation.

Macrophages play a vital role in host defense against infection by recognizing pathogens through pattern recognition receptors, such as the Toll-like receptors (TLRs), and mounting an immune response. Stimulation of TLRs initiates a complex transcriptional program in which induced transcription factor genes dynamically regulate downstream genes. Microarray-based transcriptional profiling has proved useful for mapping such transcriptional programs in simpler model organisms; however, mammalian systems present difficulties such as post-translational regulation of transcription factors, combinatorial gene regulation, and a paucity of available gene-knockout expression data. Additional evidence sources, such as DNA sequence-based identification of transcription factor binding sites, are needed. In this work, we computationally inferred a transcriptional network for TLR-stimulated murine macrophages. Our approach combined sequence scanning with time-course expression data in a probabilistic framework. Expression data were analyzed using the time-lagged correlation. A novel, unbiased method was developed to assess the significance of the time-lagged correlation. The inferred network of associations between transcription factor genes and co-expressed gene clusters was validated with targeted ChIP-on-chip experiments, and yielded insights into the macrophage activation program, including a potential novel regulator. Our general approach could be used to analyze other complex mammalian systems for which time-course expression data are available.

Linking entries in protein interaction database to structured text: the FEBS Letters experiment

The corpus of the scientific literature has reached such size that a lot of useful data, dispersed throughout millions different articles, are now hard to recover. For instance, many articles in the biological domain describe relationships between entities (gene, proteins, small molecules, etc.) yet this crucial information cannot be efficiently used because of the difficulties in retrieving it automatically from unstructured text. Databases are striving to capture this valuable information and to organize it in a structured format ready for automatic analysis. However, the current database model, based on manual curation, is not sustainable because the limited support is not compatible with complete and accurate coverage of published information. Several proposals have been put forward to increase the efficiency and accuracy of the curation process. Here we present an experiment, designed by the editorial board of FEBS Letters, aimed at integrating each manuscript with a structured summary precisely reporting, with database identifiers and predefined controlled vocabularies, the protein interactions reported in the manuscript. The authors play an important role in this process as they are requested to provide structured information to be appended, in the form of human-readable paragraphs, at the end of traditional summaries. It is envisaged that the structured text will become an integral part of Medline abstracts. In 6 months time the experience gained with this experiment will form the basis for a community discussion to propose a widely accepted strategy for information storage and retrieval.

The protein-binding potential of C2H2 zinc finger domains

There are over 10,000 C2H2-type zinc finger (ZF) domains distributed among more than 1,000 ZF proteins in the human genome. These domains are frequently observed to be involved in sequence-specific DNA binding, and uncharacterized domains are typically assumed to facilitate DNA interactions. However, some ZFs also facilitate binding to proteins or RNA. Over 100 Cys2-His2 (C2H2) ZF-protein interactions have been described. We initially attempted a bioinformatics analysis to identify sequence features that would predict a DNA- or protein-binding function. These efforts were complicated by several issues, including uncertainties about the full functional capabilities of the ZFs. We therefore applied an unbiased approach to directly examine the potential for ZFs to facilitate DNA or protein interactions. The human OLF-1/EBF associated zinc finger (OAZ) protein was used as a model. The human O/E-1-associated zinc finger protein (hOAZ) contains 30 ZFs in 6 clusters, some of which have been previously indicated in DNA or protein interactions. DNA binding was assessed using a target site selection (CAST) assay, and protein binding was assessed using a yeast two-hybrid assay. We observed that clusters known to bind DNA could facilitate specific protein interactions, but clusters known to bind protein did not facilitate specific DNA interactions. Our primary conclusion is that DNA binding is a more restricted function of ZFs, and that their potential for mediating protein interactions is likely greater. These results suggest that the role of C2H2 ZF domains in protein interactions has probably been underestimated. The implication of these findings for the prediction of ZF function is discussed.

Adaptations to climate in candidate genes for common metabolic disorders

Evolutionary pressures due to variation in climate play an important role in shaping phenotypic variation among and within species and have been shown to influence variation in phenotypes such as body shape and size among humans. Genes involved in energy metabolism are likely to be central to heat and cold tolerance. To test the hypothesis that climate shaped variation in metabolism genes in humans, we used a bioinformatics approach based on network theory to select 82 candidate genes for common metabolic disorders. We genotyped 873 tag SNPs in these genes in 54 worldwide populations (including the 52 in the Human Genome Diversity Project panel) and found correlations with climate variables using rank correlation analysis and a newly developed method termed Bayesian geographic analysis. In addition, we genotyped 210 carefully matched control SNPs to provide an empirical null distribution for spatial patterns of allele frequency due to population history alone. For nearly all climate variables, we found an excess of genic SNPs in the tail of the distributions of the test statistics compared to the control SNPs, implying that metabolic genes as a group show signals of spatially varying selection. Among our strongest signals were several SNPs (e.g., LEPR R109K, FABP2 A54T) that had previously been associated with phenotypes directly related to cold tolerance. Since variation in climate may be correlated with other aspects of environmental variation, it is possible that some of the signals that we detected reflect selective pressures other than climate. Nevertheless, our results are consistent with the idea that climate has been an important selective pressure acting on candidate genes for common metabolic disorders.

A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans

The fundamental aim of genetics is to understand how an organism's phenotype is determined by its genotype, and implicit in this is predicting how changes in DNA sequence alter phenotypes. A single network covering all the genes of an organism might guide such predictions down to the level of individual cells and tissues. To validate this approach, we computationally generated a network covering most C. elegans genes and tested its predictive capacity. Connectivity within this network predicts essentiality, identifying this relationship as an evolutionarily conserved biological principle. Critically, the network makes tissue-specific predictions-we accurately identify genes for most systematically assayed loss-of-function phenotypes, which span diverse cellular and developmental processes. Using the network, we identify 16 genes whose inactivation suppresses defects in the retinoblastoma tumor suppressor pathway, and we successfully predict that the dystrophin complex modulates EGF signaling. We conclude that an analogous network for human genes might be similarly predictive and thus facilitate identification of disease genes and rational therapeutic targets.

Chapter 1. Target selection in structural genomics projects to increase knowledge of protein structure and function space

Structural genomics aims to solve the three-dimensional structures of proteins at a rapid rate and in a cost-effective manner, with the hope of significantly impacting on the life sciences, biotechnology, and drug discovery in the long-term. Structural genomics initiatives started in Japan in 1997 with the advent of the Protein Folds Project. Since then many new initiatives have begun worldwide, with diverse aims motivating the selection of proteins for structure determination. In this chapter, we consider the biological goals of high-throughput structural biology, while focusing on the Protein Structure Initiative in the United States. This is the most productive of the structural genomics initiatives, having solved 3,363 new structures between September 2000 and October 2008.

Quantitative systems-level determinants of human genes targeted by successful drugs

What makes a successful drug target? A target molecule with an appropriate (druggable) tertiary structure is a necessary but not the sufficient condition for success. Here we analyzed specific properties of human genes and proteins targeted by 919 FDA-approved drugs and identified several quantitative measures that distinguish them from other genes and proteins at a highly significant level. Compared to an average gene and its encoded protein(s), successful drug targets are more highly connected (but far from being the most highly connected), have higher betweenness values, lower entropies of tissue expression, and lower ratios of nonsynonymous to synonymous single-nucleotide polymorphisms. Furthermore, we have identified human tissues that are significantly over- or undertargeted relative to the full spectrum of genes that are active in each tissue. Our study provides quantitative guidelines that could aid in the computational screening of new drug targets in human cells.

Public databases and software for the pathway analysis of cancer genomes

The study of pathway disruption is key to understanding cancer biology. Advances in high throughput technologies have led to the rapid accumulation of genomic data. The explosion in available data has generated opportunities for investigation of concerted changes that disrupt biological functions, this in turns created a need for computational tools for pathway analysis. In this review, we discuss approaches to the analysis of genomic data and describe the publicly available resources for studying biological pathways.

Structure-templated predictions of novel protein interactions from sequence information

The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.

Many functions performed within a living cell are mediated by specific interactions between proteins. Precise geometric and chemical matches between segments of the protein structures facilitate those interactions. Such binding surfaces are often evolutionarily conserved elements of protein structures known as conserved domains that recognize specific binding elements on the interacting proteins. Binding domains and their corresponding interacting profiles constitute basic interacting modules that are replicated in multiple protein pairs, where they mediate similar interactions. Although many conserved domains are identified, only a handful have known, well-characterized binding elements. This paper describes a computational method that aims to elucidate the binding specificity of many domains. The utility of the derived binding specificity is demonstrated by predicting new interactions between yeast proteins. The predictions are based solely on sequence information by identifying the conserved domains and their corresponding binding sequences. A number of the predicted interactions were confirmed experimentally, demonstrating the feasibility of this approach.

Genes and (common) pathways underlying drug addiction

Drug addiction is a serious worldwide problem with strong genetic and environmental influences. Different technologies have revealed a variety of genes and pathways underlying addiction; however, each individual technology can be biased and incomplete. We integrated 2,343 items of evidence from peer-reviewed publications between 1976 and 2006 linking genes and chromosome regions to addiction by single-gene strategies, microrray, proteomics, or genetic studies. We identified 1,500 human addiction-related genes and developed KARG (http://karg.cbi.pku.edu.cn), the first molecular database for addiction-related genes with extensive annotations and a friendly Web interface. We then performed a meta-analysis of 396 genes that were supported by two or more independent items of evidence to identify 18 molecular pathways that were statistically significantly enriched, covering both upstream signaling events and downstream effects. Five molecular pathways significantly enriched for all four different types of addictive drugs were identified as common pathways which may underlie shared rewarding and addictive actions, including two new ones, GnRH signaling pathway and gap junction. We connected the common pathways into a hypothetical common molecular network for addiction. We observed that fast and slow positive feedback loops were interlinked through CAMKII, which may provide clues to explain some of the irreversible features of addiction.

Modeling protein network evolution under genome duplication and domain shuffling

BACKGROUND

Successive whole genome duplications have recently been firmly established in all major eukaryote kingdoms. Such exponential evolutionary processes must have largely contributed to shape the topology of protein-protein interaction (PPI) networks by outweighing, in particular, all time-linear network growths modeled so far.

RESULTS

We propose and solve a mathematical model of PPI network evolution under successive genome duplications. This demonstrates, from first principles, that evolutionary conservation and scale-free topology are intrinsically linked properties of PPI networks and emerge from i) prevailing exponential network dynamics under duplication and ii) asymmetric divergence of gene duplicates. While required, we argue that this asymmetric divergence arises, in fact, spontaneously at the level of protein-binding sites. This supports a refined model of PPI network evolution in terms of protein domains under exponential and asymmetric duplication/divergence dynamics, with multidomain proteins underlying the combinatorial formation of protein complexes. Genome duplication then provides a powerful source of PPI network innovation by promoting local rearrangements of multidomain proteins on a genome wide scale. Yet, we show that the overall conservation and topology of PPI networks are robust to extensive domain shuffling of multidomain proteins as well as to finer details of protein interaction and evolution. Finally, large scale features of direct and indirect PPI networks of S. cerevisiae are well reproduced numerically with only two adjusted parameters of clear biological significance (i.e. network effective growth rate and average number of protein-binding domains per protein).

CONCLUSION

This study demonstrates the statistical consequences of genome duplication and domain shuffling on the conservation and topology of PPI networks over a broad evolutionary scale across eukaryote kingdoms. In particular, scale-free topologies of PPI networks, which are found to be robust to extensive shuffling of protein domains, appear to be a simple consequence of the conservation of protein-binding domains under asymmetric duplication/divergence dynamics in the course of evolution.

Profiling studies in ovarian cancer: a review

Ovarian cancer is a heterogeneous disease with respect to histopathology, molecular biology, and clinical outcome. In advanced stages, surgery and chemotherapy result in an approximately 25% overall 5-year survival rate, pointing to a strong need to identify subgroups of patients that may benefit from targeted innovative molecular therapy. This review summarizes: (a) microarray research identifying gene-expression profiles in ovarian cancer; (b) the methodological flaws in the available microarray studies; and (c) applications of pathway analysis to define new molecular subgroups. Microarray technology now permits the analysis of expression levels of thousands of genes. So far seven studies have aimed to identify a genetic profile that can predict survival/clinical outcome and/or response to platinum-based therapy. To date, the clinical evidence of prognostic microarray studies has only reached the level of small retrospective studies, and there are other issues that may explain the nonreproducibility among the reported prognostic profiles, such as overfitting, technical platform differences, and accuracy of measurements. We consider pathway analysis a promising new strategy. The accumulation of small differential expressions within a meaningful molecular regulatory network might lead to a critical threshold level, resulting in ovarian cancer. Microarray technologies have already provided valuable expression data for classifying ovarian cancer and the first clues about which molecular changes in ovarian cancer could be exploited in new treatment strategies. Further improvements in technology as well as in study design, combined with pathway analysis, will allow us to detect even more subtle tumor expression differences among subgroups of ovarian cancer patients. Disclosure of potential conflicts of interest is found at the end of this article.

Network-based classification of breast cancer metastasis

Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.

Broadening the horizon--level 2.5 of the HUPO-PSI format for molecular interactions

Background

Molecular interaction Information is a key resource in modern biomedical research. Publicly available data have previously been provided in a broad array of diverse formats, making access to this very difficult. The publication and wide implementation of the Human Proteome Organisation Proteomics Standards Initiative Molecular Interactions (HUPO PSI-MI) format in 2004 was a major step towards the establishment of a single, unified format by which molecular interactions should be presented, but focused purely on protein-protein interactions.

Results

The HUPO-PSI has further developed the PSI-MI XML schema to enable the description of interactions between a wider range of molecular types, for example nucleic acids, chemical entities, and molecular complexes. Extensive details about each supported molecular interaction can now be captured, including the biological role of each molecule within that interaction, detailed description of interacting domains, and the kinetic parameters of the interaction. The format is supported by data management and analysis tools and has been adopted by major interaction data providers. Additionally, a simpler, tab-delimited format MITAB2.5 has been developed for the benefit of users who require only minimal information in an easy to access configuration.

Conclusion

The PSI-MI XML2.5 and MITAB2.5 formats have been jointly developed by interaction data producers and providers from both the academic and commercial sector, and are already widely implemented and well supported by an active development community. PSI-MI XML2.5 enables the description of highly detailed molecular interaction data and facilitates data exchange between databases and users without loss of information. MITAB2.5 is a simpler format appropriate for fast Perl parsing or loading into Microsoft Excel.