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.     

Global topological features of cancer proteins in the human interactome

MOTIVATION: The study of interactomes, or networks of protein-protein interactions, is increasingly providing valuable information on biological systems. Here we report a study of cancer proteins in an extensive human protein-protein interaction network constructed by computational methods. RESULTS: We show that human proteins translated from known cancer genes exhibit a network topology that is different from that of proteins not documented as being mutated in cancer. In particular, cancer proteins show an increase in the number of proteins they interact with. They also appear to participate in central hubs rather than peripheral ones, mirroring their greater centrality and participation in networks that form the backbone of the proteome. Moreover, we show that cancer proteins contain a high ratio of highly promiscuous structural domains, i.e., domains with a high propensity for mediating protein interactions. These observations indicate an underlying evolutionary distinction between the two groups of proteins, reflecting the central roles of proteins, whose mutations lead to cancer. CONTACT: paul.bates@cancer.org.uk SUPPLEMENTARY INFORMATION: The interactome data are available though the PIP (Potential Interactions of Proteins) web server at http://bmm.cancerresearchuk.org/servers/pip. Further additional material is available at http://bmm.cancerresearchuk.org/servers/pip/bioinformatics/     

Topological structure analysis of the protein-protein interaction network in budding yeast

Interaction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevance within large-scale data sets is important to present-day biology. Here, a spectral method derived from graph theory was introduced to uncover hidden topological structures (i.e. quasi-cliques and quasi-bipartites) of complicated protein-protein interaction networks. Our analyses suggest that these hidden topological structures consist of biologically relevant functional groups. This result motivates a new method to predict the function of uncharacterized proteins based on the classification of known proteins within topological structures. Using this spectral analysis method, 48 quasi-cliques and six quasi-bipartites were isolated from a network involving 11,855 interactions among 2617 proteins in budding yeast, and 76 uncharacterized proteins were assigned functions.     

Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction

Background

Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer''s disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system.

Results

We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility.

Conclusions

The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0432-9) contains supplementary material, which is available to authorized users.     

A human autophagy interaction network

The original "Guidelines for the use and interpretation of assays for monitoring autophagy in higher eukaryotes" has been well received and used by many researchers and authors. I consider these to be very important guidelines that require a consensus among the researchers in the field, because they are used by authors to defend against inappropriate reviewers' comments, and by reviewers to point out to editors the flaws in research papers. Accordingly, I decided it was time to revise and update the guidelines. After all, the field has expanded substantially, as has the range of model systems being used to analyze autophagy. As a result, the list of authors has similarly increased. In addition, this version of the guidelines is not limited to higher eukaryotes nor to macroautophagy. Here, I explain the approach used to invite authors to participate in the revised guidelines, and briefly demonstrate one aspect of their utility.     

A human autophagy interaction network

The original “Guidelines for the use and interpretation of assays for monitoring autophagy in higher eukaryotes” has been well received and used by many researchers and authors. I consider these to be very important guidelines that require a consensus among the researchers in the field, because they are used by authors to defend against inappropriate reviewers’ comments, and by reviewers to point out to editors the flaws in research papers. Accordingly, I decided it was time to revise and update the guidelines. After all, the field has expanded substantially, as has the range of model systems being used to analyze autophagy. As a result, the list of authors has similarly increased. In addition, this version of the guidelines is not limited to higher eukaryotes nor to macroautophagy. Here, I explain the approach used to invite authors to participate in the revised guidelines, and briefly demonstrate one aspect of their utility.     

Functional clustering of yeast proteins from the protein-protein interaction network

Background  

The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins.     

根据蛋白质互作网络预测前列腺癌相关蛋白质的细致功能

对于功能部分已知的前列腺癌相关蛋白质,提出了一种基于Gent Ontology的功能特异的子网构建方法来细化其功能注释。结果显示该方法能够以很高的精确率为前列腺癌相关蛋白质预测更为精细的功能。预测的相关蛋白质的功能对于指导实验研究前列腺癌的分子机制具有重要的价值。     

Probabilistic model of the human protein-protein interaction network

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.     

The protein interaction network of extracellular vesicles derived from human colorectal cancer cells

Various mammalian cells including tumor cells secrete extracellular vesicles (EVs), otherwise known as exosomes and microvesicles. EVs are nanosized bilayered proteolipids and play multiple roles in intercellular communication. Although many vesicular proteins have been identified, their functional interrelationships and the mechanisms of EV biogenesis remain unknown. By interrogating proteomic data using systems approaches, we have created a protein interaction network of human colorectal cancer cell-derived EVs which comprises 1491 interactions between 957 vesicular proteins. We discovered that EVs have well-connected clusters with several hub proteins similar to other subcellular networks. We also experimentally validated that direct protein interactions between cellular proteins may be involved in protein sorting during EV formation. Moreover, physically and functionally interconnected protein complexes form functional modules involved in EV biogenesis and functions. Specifically, we discovered that SRC signaling plays a major role in EV biogenesis, and confirmed that inhibition of SRC kinase decreased the intracellular biogenesis and cell surface release of EVs. Our study provides global insights into the cargo-sorting, biogenesis, and pathophysiological roles of these complex extracellular organelles.     

Tissue specificity and the human protein interaction network

A protein interaction network describes a set of physical associations that can occur between proteins. However, within any particular cell or tissue only a subset of proteins is expressed and so only a subset of interactions can occur. Integrating interaction and expression data, we analyze here this interplay between protein expression and physical interactions in humans. Proteins only expressed in restricted cell types, like recently evolved proteins, make few physical interactions. Most tissue‐specific proteins do, however, bind to universally expressed proteins, and so can function by recruiting or modifying core cellular processes. Conversely, most ‘housekeeping’ proteins that are expressed in all cells also make highly tissue‐specific protein interactions. These results suggest a model for the evolution of tissue‐specific biology, and show that most, and possibly all, ‘housekeeping’ proteins actually have important tissue‐specific molecular interactions.     

A human functional protein interaction network and its application to cancer data analysis

Background  

One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system.     

Virtual identification of essential proteins within the protein interaction network of yeast

Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organizational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the nonessential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins, and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.     

Topological chirality of iron-sulfur proteins

An examination of x-ray structures of single-cluster [4Fe-4S] proteins in the Protein Data Bank has revealed that all redox proteins and the glutamine 5-phosphoribosyl-1-pyrophosphate amidotransferase from Bacillus subtilis have a topological configuration arbitrarily designated as D, whereas the DNA repair enzyme endonuclease III from Escherichia coli has the opposite topological configuration, L. This is the first example in which both senses of topological chirality have been observed in a class of proteins. © 1997 John Wiley & Sons, Inc. Biopoly 42: 411–414, 1997