Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets

Histone deacetylases—an important class of cellular regulators with a variety of functions

The elucidation of mechanisms of chromatin remodeling, particular transcriptional activation, and repression by histone acetylation and deacetylation has shed light on the role of histone deacetylases (HDAC) as a new kind of therapeutic target for human cancer treatment. HDACs, in general, act as components of large corepressor complexes that prevent the transcription of several tumor suppression genes. In addition, they appear to be also involved in the deacetylation of nonhistone proteins. This paper reviews the most recent insights into the diverse biological roles of HDACs as well as the evolution of this important protein family.

Network-based prediction of protein function

Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

Predicting protein-protein interactions based only on sequences information

Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.

How complete are current yeast and human protein-interaction networks?

How can protein-interaction networks can be made more complete?

We estimate the full yeast protein-protein interaction network to contain 37,800-75,500 interactions and the human network 154,000-369,000, but owing to a high false-positive rate, current maps are roughly only 50% and 10% complete, respectively. Paradoxically, releasing raw, unfiltered assay data might help separate true from false interactions.

Integrative analysis of a cancer somatic mutome

BACKGROUND

The consecutive acquisition of genetic alterations characterizes neoplastic processes. As a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. The recent identification of the collection of somatically mutated genes in breast tumors (breast cancer somatic "mutome") allows the comprehensive study of its function and organization in complex networks.

RESULTS

We analyzed functional genomic data (loss of heterozygosity, copy number variation and gene expression in breast tumors) and protein binary interactions from public repositories to identify potential novel components of neoplastic processes, the functional relationships between them, and to examine their coordinated function in breast cancer pathogenesis. This analysis identified candidate tumor suppressors and oncogenes, and new genes whose expression level predicts survival rate in breast cancer patients. Mutome network modeling using different types of pathological and healthy functional relationships unveils functional modules significantly enriched in genes or proteins (genes/proteins) with related biological process Gene Ontology terms and containing known breast cancer-related genes/proteins.

CONCLUSION

This study presents a comprehensive analysis of the breast somatic mutome, highlighting those genes with a higher probability of playing a determinant role in tumorigenesis and better defining molecular interactions related to the neoplastic process.

Disorder and sequence repeats in hub proteins and their implications for network evolution

Protein interaction networks display approximate scale-free topology, in which hub proteins that interact with a large number of other proteins determine the overall organization of the network. In this study, we aim to determine whether hubs are distinguishable from other networked proteins by specific sequence features. Proteins of different connectednesses were compared in the interaction networks of Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Homo sapienswith respect to the distribution of predicted structural disorder, sequence repeats, low complexity regions, and chain length. Highly connected proteins ("hub proteins") contained significantly more of, and greater proportion of, these sequence features and tended to be longer overall as compared to less connected proteins. These sequence features provide two different functional means for realizing multiple interactions: (1) extended interaction surface and (2) flexibility and adaptability, providing a mechanism for the same region to bind distinct partners. Our view contradicts the prevailing view that scaling in protein interactomes arose from gene duplication and preferential attachment of equivalent proteins. We propose an alternative evolutionary network specialization process, in which certain components of the protein interactome improved their fitness for binding by becoming longer or accruing regions of disorder and/or internal repeats and have therefore become specialized in network organization.

Disrupted in Schizophrenia 1 Interactome: evidence for the close connectivity of risk genes and a potential synaptic basis for schizophrenia

Disrupted in Schizophrenia 1 (DISC1) is a schizophrenia risk gene associated with cognitive deficits in both schizophrenics and the normal ageing population. In this study, we have generated a network of protein-protein interactions (PPIs) around DISC1. This has been achieved by utilising iterative yeast-two hybrid (Y2H) screens, combined with detailed pathway and functional analysis. This so-called 'DISC1 interactome' contains many novel PPIs and provides a molecular framework to explore the function of DISC1. The network implicates DISC1 in processes of cytoskeletal stability and organisation, intracellular transport and cell-cycle/division. In particular, DISC1 looks to have a PPI profile consistent with that of an essential synaptic protein, which fits well with the underlying molecular pathology observed at the synaptic level and the cognitive deficits seen behaviourally in schizophrenics. Utilising a similar approach with dysbindin (DTNBP1), a second schizophrenia risk gene, we show that dysbindin and DISC1 share common PPIs suggesting they may affect common biological processes and that the function of schizophrenia risk genes may converge.

How to comprehensively analyse proteins and how this influences nutritional research

Proteomics, the comprehensive analysis of a protein complement in a cell, tissue or biological fluid at a given time, is a key platform within the "omic" technologies that also encompass genomics (gene analysis), transcriptomics (gene expression analysis) and metabolomics (metabolite profiling). This review summarises protein pre-separation, identification, quantification and modification/interaction analysis and puts them into perspective for nutritional R and D. Mass spectrometry has progressed with regard to mass accuracy, resolution and protein identification performance. Separation, depletion and enrichment techniques can increasingly cope with complexity and dynamic range of proteomic samples. Hence, proteomic studies currently provide a broader, albeit still incomplete, coverage of a given proteome. Proteomics adapted and applied to nutrition and health should demonstrate ingredient efficacy, deliver biomarkers for health and disease disposition, help in differentiating dietary responders from non-responders, and discover bioactive food components.

Longevity network: Construction and implications

The vast majority of studies on longevity have focused on individual genes/proteins, without adequately addressing the possible role of interactions between them. This study is the first attempt towards constructing a "longevity network" via analysis of human protein-protein interactions (PPIs). For this purpose, we (i) compiled a complete list of established longevity genes from different species, including those that most probably affect the longevity in humans, (ii) defined the human orthologs of the longevity genes, and (iii) determined whether the encoded proteins could be organized as a network. The longevity gene-encoded proteins together with their interacting proteins form a continuous network, which fits the criteria for a scale-free network with an extremely high contribution of hubs to the network connectivity. Most of them have never been annotated before in connection with longevity. Remarkably, almost all of the hubs of the "longevity network" were reported to be involved in at least one age-related disease (ARD), with many being involved in several ARDs. This may be one of the ways by which the proteins with multiple interactions affect the longevity. The hubs offer the potential of being primary targets for longevity-promoting interventions.

Specificity and evolvability in eukaryotic protein interaction networks

Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10(-5) interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10(3) interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.

An evaluation of human protein-protein interaction data in the public domain

Background

Protein-protein interaction (PPI) databases have become a major resource for investigating biological networks and pathways in cells. A number of publicly available repositories for human PPIs are currently available. Each of these databases has their own unique features with a large variation in the type and depth of their annotations.

Results

We analyzed the major publicly available primary databases that contain literature curated PPI information for human proteins. This included BIND, DIP, HPRD, IntAct, MINT, MIPS, PDZBase and Reactome databases. The number of binary non-redundant human PPIs ranged from 101 in PDZBase and 346 in MIPS to 11,367 in MINT and 36,617 in HPRD. The number of genes annotated with at least one interactor was 9,427 in HPRD, 4,975 in MINT, 4,614 in IntAct, 3,887 in BIND and <1,000 in the remaining databases. The number of literature citations for the PPIs included in the databases was 43,634 in HPRD, 11,480 in MINT, 10,331 in IntAct, 8,020 in BIND and <2,100 in the remaining databases.

Conclusion

Given the importance of PPIs, we suggest that submission of PPIs to repositories be made mandatory by scientific journals at the time of manuscript submission as this will minimize annotation errors, promote standardization and help keep the information up to date. We hope that our analysis will help guide biomedical scientists in selecting the most appropriate database for their needs especially in light of the dramatic differences in their content.

CellCircuits: a database of protein network models

CellCircuits () is an open-access database of molecular network models, designed to bridge the gap between databases of individual pairwise molecular interactions and databases of validated pathways. CellCircuits captures the output from an increasing number of approaches that screen molecular interaction networks to identify functional subnetworks, based on their correspondence with expression or phenotypic data, their internal structure or their conservation across species. This initial release catalogs 2019 computationally derived models drawn from 11 journal articles and spanning five organisms (yeast, worm, fly, Plasmodium falciparum and human). Models are available either as images or in machine-readable formats and can be queried by the names of proteins they contain or by their enriched biological functions. We envision CellCircuits as a clearinghouse in which theorists may distribute or revise models in need of validation and experimentalists may search for models or specific hypotheses relevant to their interests. We demonstrate how such a repository of network models is a novel systems biology resource by performing several meta-analyses not currently possible with existing databases.

The modular nature of genetic diseases

Evidence from many sources suggests that similar phenotypes are begotten by functionally related genes. This is most obvious in the case of genetically heterogeneous diseases such as Fanconi anemia, Bardet-Biedl or Usher syndrome, where the various genes work together in a single biological module. Such modules can be a multiprotein complex, a pathway, or a single cellular or subcellular organelle. This observation suggests a number of hypotheses about the human phenome that are now beginning to be explored. First, there is now good evidence from bioinformatic analyses that human genetic diseases can be clustered on the basis of their phenotypic similarities and that such a clustering represents true biological relationships of the genes involved. Second, one may use such phenotypic similarity to predict and then test for the contribution of apparently unrelated genes to the same functional module. This concept is now being systematically tested for several diseases. Most recently, a systematic yeast two-hybrid screen of all known genes for inherited ataxias indicated that they all form part of a single extended protein-protein interaction network. Third, one can use bioinformatics to make predictions about new genes for diseases that form part of the same phenotype cluster. This is done by starting from the known disease genes and then searching for genes that share one or more functional attributes such as gene expression pattern, coevolution, or gene ontology. Ultimately, one may expect that a modular view of disease genes should help the rapid identification of additional disease genes for multifactorial diseases once the first few contributing genes (or environmental factors) have been reliably identified.

Analysis of protein sequence and interaction data for candidate disease gene prediction

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

Transcriptional coordination of the metabolic network in Arabidopsis

Patterns of coexpression can reveal networks of functionally related genes and provide deeper understanding of processes requiring multiple gene products. We performed an analysis of coexpression networks for 1,330 genes from the AraCyc database of metabolic pathways in Arabidopsis (Arabidopsis thaliana). We found that genes associated with the same metabolic pathway are, on average, more highly coexpressed than genes from different pathways. Positively coexpressed genes within the same pathway tend to cluster close together in the pathway structure, while negatively correlated genes typically occupy more distant positions. The distribution of coexpression links per gene is highly skewed, with a small but significant number of genes having numerous coexpression partners but most having fewer than 10. Genes with multiple connections (hubs) tend to be single-copy genes, while genes with multiple paralogs are coexpressed with fewer genes, on average, than single-copy genes, suggesting that the network expands through gene duplication, followed by weakening of coexpression links involving duplicate nodes. Using a network-analysis algorithm based on coexpression with multiple pathway members (pathway-level coexpression), we identified and prioritized novel candidate pathway members, regulators, and cross pathway transcriptional control points for over 140 metabolic pathways. To facilitate exploration and analysis of the results, we provide a Web site (http://www.transvar.org/at_coexpress/analysis/web) listing analyzed pathways with links to regression and pathway-level coexpression results. These methods and results will aid in the prioritization of candidates for genetic analysis of metabolism in plants and contribute to the improvement of functional annotation of the Arabidopsis genome.

Discovering disease-genes by topological features in human protein-protein interaction network

MOTIVATION

Mining the hereditary disease-genes from human genome is one of the most important tasks in bioinformatics research. A variety of sequence features and functional similarities between known human hereditary disease-genes and those not known to be involved in disease have been systematically examined and efficient classifiers have been constructed based on the identified common patterns. The availability of human genome-wide protein-protein interactions (PPIs) provides us with new opportunity for discovering hereditary disease-genes by topological features in PPIs network.

RESULTS

This analysis reveals that the hereditary disease-genes ascertained from OMIM in the literature-curated (LC) PPIs network are characterized by a larger degree, tendency to interact with other disease-genes, more common neighbors and quick communication to each other whereas those properties could not be detected from the network identified from high-throughput yeast two-hybrid mapping approach (EXP) and predicted interactions (PDT) PPIs network. KNN classifier based on those features was created and on average gained overall prediction accuracy of 0.76 in cross-validation test. Then the classifier was applied to 5262 genes on human genome and predicted 178 novel disease-genes. Some of the predictions have been validated by biological experiments.

Schizophrenia susceptibility genes converge on interlinked pathways related to glutamatergic transmission and long-term potentiation, oxidative stress and oligodendrocyte viability

Over 130 genes have been associated with schizophrenia in genetic studies. None of these has reached a sufficient level of confidence to be accepted as a universal susceptibility gene and problems of replicability suggest that many may be false positives. Nevertheless, these genes can be grouped into distinct families related to glutamate transmission (in particular related to NMDA receptor function), the control of synaptic plasticity, dopaminergic transmission, oxidative stress, glutathione and quinone metabolism and oligodendrocyte viability. These families mirror the processes disrupted in the schizophrenic brain and certain gene families can be linked together to form a clearly defined signalling cascade involved in the phenomenon of NMDA receptor-dependent long-term potentiation and synaptic plasticity, that may be interconnected with oligodendrocyte and oxidative stress-related pathways. Many of the protein products of these genes interact with each other, forming complex integrated networks. Certain high-interest genes (for example DISC1, NRG1, COMT) may exert multiple effects on different areas of these pathways, while others exert more specific effects on certain branches. The convergence of a large number of genes on a definable signaling network raises the possibility of numerous interactions between gene candidates, and suggests that a targeted multigenic pathway approach would be useful in gene association studies.

Yeast two-hybrid contributions to interactome mapping

Interactome mapping, the systematic identification of protein interactions within an organism, promises to facilitate systems-level studies of biological processes. Using in vitro technologies that measure specific protein interactions, static maps are being generated that include many of the protein networks that occur in vivo. Most of the binary protein interaction data currently available was generated by large-scale yeast two-hybrid screens. Recent efforts to map interactions in model organisms and in humans illustrate the promise and some of the limitations of the two-hybrid approach. Although these maps are incomplete and include false positives, they are proving useful as a framework around which to elaborate and model the in vivo interactome.

Protein-protein interactions in the mammalian brain

Recent genome-wide high-throughput (HTS) analyses of protein-protein interactions (PPIs) provide molecular-based information to uncover functions of cells and tissues, such as those of the mammalian brain. However, the HTS PPI data contain much false-negatives and false-positives, which should be primarily addressed in experiments. Integrating PPI data sets with other genome-wide data, such as expression profiles and phenotype data sets, provides novel biological insights. Such integration analysis is valuable for addressing the complexity of the mammalian brain. Discovery of novel interactions followed by a detailed analysis is a successful approach to uncover the function of proteins. For example, extensive PPI screens for parkin, a hereditary Parkinson's disease gene, elucidated the function of parkin as an E3 ubiquitin ligase, with localization and activity regulated by contact with its interaction partners, uncovering at least a part of the molecular pathogenesis of Parkinson's disease.

Adrm1, a putative cell adhesion regulating protein, is a novel proteasome-associated factor

We have identified Adrm1 as a novel component of the regulatory ATPase complex of the 26 S proteasome: Adrm1 was precipitated with an antibody to proteasomes and vice versa. Adrm1 co-migrated with proteasomes on gel-filtration chromatography and non-denaturing polyacrylamide gel electrophoresis. Adrm1 has been described as an interferon-gamma-inducible, heavily glycosylated membrane protein of 110 kDa. However, we found Adrm1 in mouse tissues only as a 42 kDa peptide, corresponding to the mass of the non-glycosylated peptide chain, and it could not be induced in HeLa cells with interferon. Adrm1 was present almost exclusively in soluble 26 S proteasomes, albeit a small fraction was membrane-associated, like proteasomes. Adrm1 was found in cells in amounts equimolar with S6a, a 26 S proteasome subunit. HeLa cells contain no pool of free Adrm1 but recombinant Adrm1 could bind to pre-existing 26 S proteasomes in cell extracts. Adrm1 may be distantly related to the yeast proteasome subunit Rpn13, mutants of which are reported to display no obvious phenotype. Accordingly, knock-down of Adrm1 in HeLa cells had no effect on the amount of proteasomes, or on degradation of bulk cell protein, or accumulation of polyubiquitinylated proteins. This indicates that Adrm1 has a specialised role in proteasome function.

Predicting disease genes using protein-protein interactions

BACKGROUND

The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes.

OBJECTIVE

To investigate whether protein-protein interactions can predict genes for genetically heterogeneous diseases.

METHODS

72,940 protein-protein interactions between 10,894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes.

RESULTS

Almost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci.

CONCLUSIONS

Exploiting protein-protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions.