Identification of gene annotations and interactions and protein-protein interaction associated disorders through data integration

Signal flow direction is one of the most important features of the protein-protein interactions in signaling networks. However, almost all the outcomes of current high-throughout techniques for protein-protein interactions mapping are usually supposed to be non-directional. Based on the pairwise interaction domains, here we defined a novel parameter protein interaction directional score and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08%, and error ratio 16.91%. As an application, we established an integrated human directional protein interaction network, including 2,237 proteins and 5,530 interactions, and inferred a large amount of novel signaling pathways. Directional protein interaction network was strongly supported by the known signaling pathways literature (with the 87.5% accuracy) and further analyses on the biological annotation, subcellular localization, and network topology property. Thus, this study provided an effective method to define the upstream/downstream relations of interacting protein pairs and a powerful tool to unravel the unknown signaling pathways.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Protein-protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI ( http://evoppi.i3s.up.pt ) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.

Infection by the human blood fluke, Schistosoma mansoni involves a variety of cross-species protein- protein interactions. The pathogen expresses a diverse arsenal of proteins that facilitate the breach of physical and biochemical barriers present in skin evasion of the immune system, and digestion of human plasma proteins including albumin and hemoglobin, allowing schistosomes to reside in the host for years. However, only a small number of specific interactions between S. mansoni and human proteins have been identified. We present and apply a protocol that generates testable predictions of S. mansoni-human protein interactions. In this study, we have preliminary predictions of novel interactions between schistosome and human proteins relevant to infection and the ability of the parasite to evade the immune system. We applied a computational whole-genome comparative approach to predict potential S. mansoni-human protein interactions based on similarity to known protein complexes. We first predict S. mansoni -human protein interactions based on similarity to known protein complexes. Putative interactions were then scored and assessed using several contextual filters, including the use of annotation automatically derived from literature using a simple natural language processing methodology. Next, in vitro experiments were carried out between schistosome and host proteins to validate several prospective predictions. Our method predicted 7 out of the 10 previously known cross-species interactions involved in pathogenesis between S. mansoni and its human host. Interestingly, two novel putative interactions involving Schistosoma proteins, the cercarial elastase SmCE, and the adult tegument surface protein Sm29, were also predicted and experimentally characterized. Preliminary data suggest that elafin, a host endogenous serine protease inhibitor, may be a novel substrate for SmCE. Additionally, CD59, an inhibitor of the membrane attack complex, could interact with Sm29. Furthermore, the application framework provides an integrated methodology for investigation of host-pathogen interactions and an extensive source of orthogonal data for experimental analysis. We have made the predictions available for community perusal.

BackgroundThe invasion of red blood cells (RBCs) by malarial parasites is an essential step in the life cycle of Plasmodium falciparum. Human-parasite surface protein interactions play a critical role in this process. Although several interactions between human and parasite proteins have been discovered, the mechanism related to invasion remains poorly understood because numerous human-parasite protein interactions have not yet been identified. High-throughput screening experiments are not feasible for malarial parasites due to difficulty in expressing the parasite proteins. Here, we performed computational prediction of the PPIs involved in malaria parasite invasion to elucidate the mechanism by which invasion occurs.ResultsIn this study, an expectation maximization algorithm was used to estimate the probabilities of domain-domain interactions (DDIs). Estimates of DDI probabilities were then used to infer PPI probabilities. We found that our prediction performance was better than that based on the information of D. melanogaster alone when information related to the six species was used. Prediction performance was assessed using protein interaction data from S. cerevisiae, indicating that the predicted results were reliable. We then used the estimates of DDI probabilities to infer interactions between 490 parasite and 3,787 human membrane proteins. A small-scale dataset was used to illustrate the usability of our method in predicting interactions between human and parasite proteins. The positive predictive value (PPV) was lower than that observed in S. cerevisiae. We integrated gene expression data to improve prediction accuracy and to reduce false positives. We identified 80 membrane proteins highly expressed in the schizont stage by fast Fourier transform method. Approximately 221 erythrocyte membrane proteins were identified using published mass spectral datasets. A network consisting of 205 interactions was predicted. Results of network analysis suggest that SNARE proteins of parasites and APP of humans may function in the invasion of RBCs by parasites.ConclusionsWe predicted a small-scale PPI network that may be involved in parasite invasion of RBCs by integrating DDI information and expression profiles. Experimental studies should be conducted to validate the predicted interactions. The predicted PPIs help elucidate the mechanism of parasite invasion and provide directions for future experimental investigations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0393-z) contains supplementary material, which is available to authorized users.

Normal infants from 32 to 54 weeks post-conceptional age show a clear pathway of development in the exocrine pancreas, involving differentiation of acinar tissue, reflected by increased acinar to connective tissue volumes. In contrast, an abnormal pattern of development has been found in cystic fibrosis. Early signs of a deficiency in exocrine tissue at 32-38 weeks post-conceptional age suggest that there is a lack of normal maturation of pancreatic exocrine tissue that occurs in utero, with a degenerative process supervening after birth. The volumes of the acinar and duct lumen is markedly increased, up to 10 fold normal volume in cystic fibrosis subjects. However, the lumenal changes are not a function of age. Accumulation of secretory material in the duct is a characteristic feature in cystic fibrosis and may cause dilation of the duct and acinar lumen and degradation of acini. Alternatively, the greater volume and lack of direct relationship to exocrine acinar volume may reflect a persistence of the fetal pattern of pancreatic development in cystic fibrosis. The diagnosis of cystic fibrosis within the first few months of life is difficult when based on conventional or subjective histologic criteria. By quantitative microscopy of the pancreas, an objective approach is available that clearly distinguishes cystic fibrosis from control subjects. In this retrospective survey, 93% of cystic fibrosis infants were discriminated from normals; only 2 of 30 cases (70%) were not clearly differentiated from controls. As a genetic disorder, the manifestation of cystic fibrosis would be expected to result in pancreatic dysfunction in utero. Other predisposing factors, however, may be involved in the pancreatic lesions such as obstruction of the pancreatic duct, failure in the synthesis or secretion of pancreatic enzymes or abnormal mucus production in the intestine. Further ultrastructural and functional investigations will be important to understand the underlying defect in cystic fibrosis.

Protein-protein interaction (PPI) prediction method has provided an opportunity for elucidating potential biological processes and disease mechanisms. We integrated eight features involving proteomic, genomic, phenotype and functional annotation datasets by a mixed model consisting of full connected Bayesian (FCB) model and naive Bayesian model to predict human PPIs, resulting in 40 447 PPIs which contain 2740 common PPIs with the human protein reference database (HPRD) by a likelihood ratio cutoff of 512. Then we applied them to exploring underlying pathway crosstalk where pathways were derived from the pathway interaction database. Two pathway crosstalk networks (PCNs) were constructed based on PPI sets. The PPI sets were derived from two different sources. One source was strictly the HPRD database while the other source was a combination of HPRD and PPIs predicted by our mixed Bayesian method. We demonstrated that PCNs based on the mixed PPI set showed much more underlying pathway interactions than the HPRD PPI set. Furthermore, we mapped cancer-causing mutated somatic genes to PPIs between significant pathway crosstalk pairs. We extracted highly connected clusters from over-represented subnetworks of PCNs, which were enriched for mutated gene interactions that acted as crosstalk links. Most of the pathways in top ranking clusters were shown to play important roles in cancer. The clusters themselves showed coherent function categories pertaining to cancer development.

BackgroundAs numerous experimental factors drive the acquisition, identification, and interpretation of protein-protein interactions (PPIs), aggregated assemblies of human PPI data invariably contain experiment-dependent noise. Ascertaining the reliability of PPIs collected from these diverse studies and scoring them to infer high-confidence networks is a non-trivial task. Moreover, a large number of PPIs share the same number of reported occurrences, making it impossible to distinguish the reliability of these PPIs and rank-order them. For example, for the data analyzed here, we found that the majority (>83%) of currently available human PPIs have been reported only once.ResultsIn this work, we proposed an unsupervised statistical approach to score a set of diverse, experimentally identified PPIs from nine primary databases to create subsets of high-confidence human PPI networks. We evaluated this ranking method by comparing it with other methods and assessing their ability to retrieve protein associations from a number of diverse and independent reference sets. These reference sets contain known biological data that are either directly or indirectly linked to interactions between proteins. We quantified the average effect of using ranked protein interaction data to retrieve this information and showed that, when compared to randomly ranked interaction data sets, the proposed method created a larger enrichment (~134%) than either ranking based on the hypergeometric test (~109%) or occurrence ranking (~46%).ConclusionsFrom our evaluations, it was clear that ranked interactions were always of value because higher-ranked PPIs had a higher likelihood of retrieving high-confidence experimental data. Reducing the noise inherent in aggregated experimental PPIs via our ranking scheme further increased the accuracy and enrichment of PPIs derived from a number of biologically relevant data sets. These results suggest that using our high-confidence protein interactions at different levels of confidence will help clarify the topological and biological properties associated with human protein networks.

Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and in silico approaches allow the screening of protein interactors in a large-scale manner. Therefore, the bulk of information on protein interactions deposited in databases and peer-reviewed published literature is constantly growing. Multiple databases interfaced from user-friendly web tools recently emerged to facilitate the task of protein interaction data retrieval and data integration. Nevertheless, as we evidence in this report, despite the current efforts towards data integration, the quality of the information on protein interactions retrieved by in silico approaches is frequently incomplete and may even list false interactions. Here we point to some obstacles precluding confident data integration, with special emphasis on protein interactions, which include gene acronym redundancies and protein synonyms. Three human proteins (choline kinase, PPIase and uromodulin) and three different web-based data search engines focused on protein interaction data retrieval (PSICQUIC, DASMI and BIPS) were used to explain the potential occurrence of undesired errors that should be considered by researchers in the field. We demonstrate that, despite the recent initiatives towards data standardization, manual curation of protein interaction networks based on literature searches are still required to remove potential false positives. A three-step workflow consisting of: (i) data retrieval from multiple databases, (ii) peer-reviewed literature searches, and (iii) data curation and integration, is proposed as the best strategy to gather updated information on protein interactions. Finally, this strategy was applied to compile bona fide information on human DREAM protein interactome, which constitutes liable training datasets that can be used to improve computational predictions.

Using data from model organisms, the authors have generated a large-scale human protein-protein interaction map. The map can be used to predict the function of human proteins.

Protein complexes are a cornerstone of many biological processes. Protein-protein interaction (PPI) data enable a number of computational methods for predicting protein complexes. However, the insufficiency of the PPI data significantly lowers the accuracy of computational methods. In the current work, the authors develop a novel method named clustering based on multiple biological information (CMBI) to discover protein complexes via the integration of multiple biological resources including gene expression profiles, essential protein information and PPI data. First, CMBI defines the functional similarity of each pair of interacting proteins based on the edge-clustering coefficient and the Pearson correlation coefficient. Second, CMBI selects essential proteins as seeds to build the protein complexes. A redundancy-filtering procedure is performed to eliminate redundant complexes. In addition to the essential proteins, CMBI also uses other proteins as seeds to expand protein complexes. To check the performance of CMBI, the authors compare the complexes discovered by CMBI with the ones found by other techniques by matching the predicted complexes against the reference complexes. The authors use subsequently GO::TermFinder to analyse the complexes predicted by various methods. Finally, the effect of parameters T and R is investigated. The results from GO functional enrichment and matching analyses show that CMBI performs significantly better than the state-of-the-art methods.

BACKGROUNDStudies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells (HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use “omics” methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIMTo establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODSWe used microarrays, bioinformatics, protein-protein interaction (PPI) network, and sub-modules to investigate taurine-induced changes in gene expression in human HSCs (LX-2). Subsequently, all of the differentially expressed genes (DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTSA total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1 (ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase (MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway, estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21, TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSIONTaurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.

Cystic fibrosis (CF) is a genetic disorder with involvement of many organ systems. For some of the affected organs, the dysfunction is a direct consequence of cystic fibrosis transmembrane conductance regulator (CFTR) deficiency, while in others the pathophysiology is complex and may be influenced by treatments and social factors. Recent decades have seen improved wellbeing and survival of people with CF, even more so with the introduction of CFTR modulator treatments. For many manifestations of CF, the focus has shifted from treatment of complications to screening and monitoring for early detection of complications, and prevention of treatment-related complications. Examples are early detection of Pseudomonas aeruginosa airway infection and CF-related diabetes, and more-recently adopted strategies such as screening for colorectal cancer and anxiety and depression. In this article we review well-documented and emerging CF manifestations, risk factors for complications, and strategies recommended by international society guidelines and care recommendations for prevention, screening and monitoring. We review existing guideline-based recommendations with a glimpse at emerging modalities for screening and monitoring which may be adopted in the future. Finally, we review the changes due to treatment with CFTR modulators and their possible implications for future screening and monitoring strategies. As people with CF enjoy better quality of life and improved survival, screening for CF-related morbidities will be evolving constantly.

BackgroundMost proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as yeast-two-hybrid) have enabled systematic screening of pairwise protein-protein interactions en masse. Thus computational approaches for detecting protein complexes from protein interaction data are useful complements to the limited experimental methods. They can be used together with the experimental methods for mapping the interactions of proteins to understand how different proteins are organized into higher-level substructures to perform various cellular functions.ResultsGiven the abundance of pairwise protein interaction data from high-throughput genome-wide experimental screenings, a protein interaction network can be constructed from protein interaction data by considering individual proteins as the nodes, and the existence of a physical interaction between a pair of proteins as a link. This binary protein interaction graph can then be used for detecting protein complexes using graph clustering techniques. In this paper, we review and evaluate the state-of-the-art techniques for computational detection of protein complexes, and discuss some promising research directions in this field.ConclusionsExperimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes. In addition, the computational approaches have also improved in performance over the years. Further improvements could be achieved if the quality of the underlying protein interaction data can be considered adequately to minimize the undesirable effects from the irrelevant and noisy sources, and the various biological evidences can be better incorporated into the detection process to maximize the exploitation of the increasing wealth of biological knowledge available.

BackgroundEssential proteins are distinctly important for an organism’s survival and development and crucial to disease analysis and drug design as well. Large-scale protein-protein interaction (PPI) data sets exist in Saccharomyces cerevisiae, which provides us with a valuable opportunity to predict identify essential proteins from PPI networks. Many network topology-based computational methods have been designed to detect essential proteins. However, these methods are limited by the completeness of available PPI data. To break out of these restraints, some computational methods have been proposed by integrating PPI networks and multi-source biological data. Despite the progress in the research of multiple data fusion, it is still challenging to improve the prediction accuracy of the computational methods.ResultsIn this paper, we design a novel iterative model for essential proteins prediction, named Randomly Walking in the Heterogeneous Network (RWHN). In RWHN, a weighted protein-protein interaction network and a domain-domain association network are constructed according to the original PPI network and the known protein-domain association network, firstly. And then, we establish a new heterogeneous matrix by combining the two constructed networks with the protein-domain association network. Based on the heterogeneous matrix, a transition probability matrix is established by normalized operation. Finally, an improved PageRank algorithm is adopted on the heterogeneous network for essential proteins prediction. In order to eliminate the influence of the false negative, information on orthologous proteins and the subcellular localization information of proteins are integrated to initialize the score vector of proteins. In RWHN, the topology, conservative and functional features of essential proteins are all taken into account in the prediction process. The experimental results show that RWHN obviously exceeds in predicting essential proteins ten other competing methods.ConclusionsWe demonstrated that integrating multi-source data into a heterogeneous network can preserve the complex relationship among multiple biological data and improve the prediction accuracy of essential proteins. RWHN, our proposed method, is effective for the prediction of essential proteins.