HDNetDB: A Molecular Interaction Database for Network-Oriented Investigations into Huntington's Disease.

Huntington's disease (HD) is a progressive and fatal neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin gene. Although HD is monogenic, its molecular manifestation appears highly complex and involves multiple cellular processes. The recent application of high throughput platforms such as microarrays and mass-spectrometry has indicated multiple pathogenic routes. The massive data generated by these techniques together with the complexity of the pathogenesis, however, pose considerable challenges to researchers. Network-based methods can provide valuable tools to consolidate newly generated data with existing knowledge, and to decipher the interwoven molecular mechanisms underlying HD. To facilitate research on HD in a network-oriented manner, we have developed HDNetDB, a database that integrates molecular interactions with many HD-relevant datasets. It allows users to obtain, visualize and prioritize molecular interaction networks using HD-relevant gene expression, phenotypic and other types of data obtained from human samples or model organisms. We illustrated several HDNetDB functionalities through a case study and identified proteins that constitute potential cross-talk between HD and the unfolded protein response (UPR). HDNetDB is publicly accessible at http://hdnetdb.sysbiolab.eu .

Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity.

Assemblies of huntingtin (HTT) fragments with expanded polyglutamine (polyQ) tracts are a pathological hallmark of Huntington's disease (HD). The molecular mechanisms by which these structures are formed and cause neuronal dysfunction and toxicity are poorly understood. Here, we utilized available gene expression data sets of selected brain regions of HD patients and controls for systematic interaction network filtering in order to predict disease-relevant, brain region-specific HTT interaction partners. Starting from a large protein-protein interaction (PPI) data set, a step-by-step computational filtering strategy facilitated the generation of a focused PPI network that directly or indirectly connects 13 proteins potentially dysregulated in HD with the disease protein HTT. This network enabled the discovery of the neuron-specific protein CRMP1 that targets aggregation-prone, N-terminal HTT fragments and suppresses their spontaneous self-assembly into proteotoxic structures in various models of HD. Experimental validation indicates that our network filtering procedure provides a simple but powerful strategy to identify disease-relevant proteins that influence misfolding and aggregation of polyQ disease proteins.

UniHI 7: an enhanced database for retrieval and interactive analysis of human molecular interaction networks.

Unified Human Interactome (UniHI) (http://www.unihi.org) is a database for retrieval, analysis and visualization of human molecular interaction networks. Its primary aim is to provide a comprehensive and easy-to-use platform for network-based investigations to a wide community of researchers in biology and medicine. Here, we describe a major update (version 7) of the database previously featured in NAR Database Issue. UniHI 7 currently includes almost 350,000 molecular interactions between genes, proteins and drugs, as well as numerous other types of data such as gene expression and functional annotation. Multiple options for interactive filtering and highlighting of proteins can be employed to obtain more reliable and specific network structures. Expression and other genomic data can be uploaded by the user to examine local network structures. Additional built-in tools enable ready identification of known drug targets, as well as of biological processes, phenotypes and pathways enriched with network proteins. A distinctive feature of UniHI 7 is its user-friendly interface designed to be utilized in an intuitive manner, enabling researchers less acquainted with network analysis to perform state-of-the-art network-based investigations.

The integration and annotation of the human interactome in the UniHI Database.

In recent years, remarkable progress has been made toward the systematic charting of human protein interactions. The utilization of the generated interaction data remained however challenging for biomedical researchers due to lack of integration of currently available resources. To facilitate the direct access and analysis of the human interactome, we have developed the Unified Human Interactome (UniHI) database. It provides researchers with a user-friendly Web-interface and integrates interaction data from 12 major resources in its latest version, establishing one of the largest catalogs for human PPIs worldwide. At present, UniHI houses over 250,000 distinct interactions between 22,300 unique proteins and is publically available at http://www.unihi.org.

Inferring modules from human protein interactome classes.

BACKGROUND: The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features. RESULTS: We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated) data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules. CONCLUSIONS: Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.

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

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

Comparison of human protein-protein interaction maps.

MOTIVATION: Large-scale mappings of protein-protein interactions have started to give us new views of the complex molecular mechanisms inside a cell. After initial projects to systematically map protein interactions in model organisms such as yeast, worm and fly, researchers have begun to focus on the mapping of the human interactome. To tackle this enormous challenge, different approaches have been proposed and pursued. While several large-scale human protein interaction maps have recently been published, their quality remains to be critically assessed. RESULTS: We present here a first comparative analysis of eight currently available large-scale maps with a total of over 10,000 unique proteins and 57,000 interactions included. They are based either on literature search, orthology or by yeast-two-hybrid assays. Comparison reveals only a small, but statistically significant overlap. More importantly, our analysis gives clear indications that all interaction maps imply considerable selection and detection biases. These results have to be taken into account for future assembly of the human interactome. AVAILABILITY: An integrated human interaction network called Unified Human Interactome (UniHI) is made publicly accessible at http://www.mdc-berlin.de/unihi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UniHI: an entry gate to the human protein interactome.

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

Graph-theoretical comparison reveals structural divergence of human protein interaction networks.

Protein interactions constitute the backbone of the cellular machinery in living systems. Their biological importance has led to systematic assemblies of large-scale protein-protein interaction maps for various organisms. Recently, the focus of such interactome projects has shifted towards the elucidation of the human interaction network. Several strategies have been employed to gain comprehensive maps of protein interactions occurring in the human body. For their efficient analysis, graph theory has become a favourite tool. It can identify characteristic features of interaction networks which can give us important insights into the general structure of the underlying molecular networks. Although such graph-theoretical analyses have delivered us a variety of interesting results, their general validity remains to be demonstrated. We therefore examined whether independently assembled human interaction networks show common structural features. Remarkably, while some general graph-theoretical features were found, we detected a strong dependency of network structures on the method used to generate the network. Our study strongly indicates that graph-theoretical analysis can be severely compromised by the observed structural divergence and reassessment of earlier results might be warranted.

Systematic functional assessment of human protein-protein interaction maps.

Protein-protein interaction maps can contribute substantially to the discovery of protein cooperation patterns in the cell. Recently, several large-scale human protein-protein interaction maps have been generated using experimental or computational approaches. Evaluation of these maps is likely to provide a better understanding of human biology. However, careful analysis is needed, as the comparison of interaction maps of lower eukaryotes showed a surprising divergence between different maps. Here, we present a first systematic functional assessment of eight currently available large-scale human protein-protein interaction maps. The analysis shows that these maps include a large number of common proteins, but only a small number of common interactions. We detected several types of biases that need to be considered in the future utilization of these maps.