The protein–protein interaction network of the human Sirtuin family

Protein–protein interaction networks are useful for studying human diseases and to look for possible health care through a holistic approach. Networks are playing an increasing and important role in the understanding of physiological processes such as homeostasis, signaling, spatial and temporal organizations, and pathological conditions. In this article we show the complex system of interactions determined by human Sirtuins (Sirt) largely involved in many metabolic processes as well as in different diseases. The Sirtuin family consists of seven homologous Sirt-s having structurally similar cores but different terminal segments, being rather variable in length and/or intrinsically disordered. Many studies have determined their cellular location as well as biological functions although molecular mechanisms through which they act are actually little known therefore, the aim of this work was to define, explore and understand the Sirtuin-related human interactome. As a first step, we have integrated the experimentally determined protein–protein interactions of the Sirtuin-family as well as their first and second neighbors to a Sirtuin-related sub-interactome. Our data showed that the second-neighbor network of Sirtuins encompasses 25% of the entire human interactome, and exhibits a scale-free degree distribution and interconnectedness among top degree nodes. Moreover, the Sirtuin sub interactome showed a modular structure around the core comprising mixed functions. Finally, we extracted from the Sirtuin sub-interactome subnets related to cancer, aging and post-translational modifications for information on key nodes and topological space of the subnets in the Sirt family network.     

Identification of proliferative diabetic retinopathy-associated genes on the protein–protein interaction network by using heat diffusion algorithm

Diabetic retinopathy is a common complication of diabetes mellitus that causes pathogenic damage to the retina. Particularly, the proliferative diabetic retinopathy (PDR) state can cause abnormal angiogenesis in the retina tissues and trigger the retina destruction in advanced stage. In the clinic, the symptoms during the initiation and progression of PDR are relatively unrecognizable. Therefore, various studies have focused on the pathogenesis of PDR. According to published literature, genetic contributions play an irreplaceable role in the initiation and progression of PDR. Although many computational methods, such as shortest path- and random walk with restart-based methods, have been applied in screening the potential pathogenic factors of PDR, advanced computational methods, which may provide essential supplements for previous ones, are still widely needed. In this study, a novel computational method was presented to infer novel PDR-associated genes. Different from previous methods, the method used in this work employed a different network algorithm, that is, the Laplacian heat diffusion algorithm. This algorithm was applied on the protein–protein interaction network reported in the STRING database. Three screening tests were performed to filter the most likely inferred genes. A total of 26 genes were accessed using the proposed method. Compared with the two previous predictions, most of the identified genes were novel, and only one gene was shared. Several inferred genes, such as CSF3, COL18A1, CXCR2, CCR1, FGF23, CXCL11, and IL13, were related to the pathogenesis of PDR.     

Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

Investigating the effect of increasing robot group sizes on the human psychophysiological state in the context of human–swarm interaction

We study the psychophysiological state of humans when exposed to robot groups of varying sizes. In our experiments, 24 participants are exposed sequentially to groups of robots made up of 1, 3 and 24 robots. We measure both objective physiological metrics (skin conductance level and heart rate), and subjective self-reported metrics (from a psychological questionnaire). These measures allow us to analyse the psychophysiological state (stress, anxiety, happiness) of our participants. Our results show that the number of robots to which a human is exposed has a significant impact on the psychophysiological state of the human and that higher numbers of robots provoke a stronger response.     

Advances in the study of protein–DNA interaction

Protein-DNA interaction plays an important role in many biological processes. The classical methods and the novel technologies advanced have been developed for the interaction of protein-DNA. Recent developments of these methods and research achievements have been reviewed in this paper.     

Identification of genes related to proliferative diabetic retinopathy through RWR algorithm based on protein–protein interaction network

Proliferative diabetic retinopathy (PDR) is one of the most common complications of diabetes and can lead to blindness. Proteomic studies have provided insight into the pathogenesis of PDR and a series of PDR-related genes has been identified but are far from fully characterized because the experimental methods are expensive and time consuming. In our previous study, we successfully identified 35 candidate PDR-related genes through the shortest-path algorithm. In the current study, we developed a computational method using the random walk with restart (RWR) algorithm and the protein–protein interaction (PPI) network to identify potential PDR-related genes. After some possible genes were obtained by the RWR algorithm, a three-stage filtration strategy, which includes the permutation test, interaction test and enrichment test, was applied to exclude potential false positives caused by the structure of PPI network, the poor interaction strength, and the limited similarity on gene ontology (GO) terms and biological pathways. As a result, 36 candidate genes were discovered by the method which was different from the 35 genes reported in our previous study. A literature review showed that 21 of these 36 genes are supported by previous experiments. These findings suggest the robustness and complementary effects of both our efforts using different computational methods, thus providing an alternative method to study PDR pathogenesis.     

ModuleSearch: finding functional modules in a protein–protein interaction network

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein–protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein–protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.     

Construction and analysis of the protein–protein interaction network for the olfactory system of the silkworm Bombyx mori

Olfaction plays an essential role in feeding and information exchange in insects. Previous studies on the olfaction of silkworms have provided a wealth of information about genes and proteins, yet, most studies have only focused on a single gene or protein related to the insect's olfaction. The aim of the current study is to determine key proteins in the olfactory system of the silkworm, and further understand protein–protein interactions (PPIs) in the olfactory system of Lepidoptera. To achieve this goal, we integrated Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses. Furthermore, we selected 585 olfactory-related proteins and constructed a (PPI) network for the olfactory system of the silkworm. Network analysis led to the identification of several key proteins, including GSTz1, LOC733095, BGIBMGA002169-TA, BGIBMGA010939-TA, GSTs2, GSTd2, Or-2, and BGIBMGA013255-TA. A comprehensive evaluation of the proteins showed that glutathione S-transferases (GSTs) had the highest ranking. GSTs also had the highest enrichment levels in GO and KEGG. In conclusion, our analysis showed that key nodes in the biological network had a significant impact on the network, and the key proteins identified via network analysis could serve as new research targets to determine their functions in olfaction.     

An insight into major signaling pathways and protein–protein interaction networks involved in the pathogenesis of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is associated with the increase of glucose in the blood rather than being absorbed by the cells. A better understanding of the signaling pathways is necessary to understand the pathophysiology of GDM. This study provides details about a series of signaling pathways and protein–protein interactions involved in the pathogenesis of GDM and their evaluations in GDM development. Protein–protein interactions were found between proteins of several signaling pathways that suggest interlink between these signaling pathways. Protein–protein interactions were generated with high confidence interaction scores based on textmining, cooccurrence, coexpression, neighborhood, gene fusion, experiments, and databases. The dysregulation of signaling pathways may also contribute to the increased risk of complications associated with GDM in the mother and child. Further, studies on signaling pathways involved in the pathogenesis of GDM would help in the development of an effective intervention to prevent GDM along with the identification of key targets for effective therapies in the future.     

Combination use of protein–protein interaction network topological features improves the predictive scores of deleterious non-synonymous single-nucleotide polymorphisms

Single-nucleotide polymorphisms (SNPs) are the most frequent form of genetic variations. Non-synonymous SNPs (nsSNPs) occurring in coding region result in single amino acid substitutions that associate with human hereditary diseases. Plenty of approaches were designed for distinguishing deleterious from neutral nsSNPs based on sequence level information. Novel in this work, combinations of protein–protein interaction (PPI) network topological features were introduced in predicting disease-related nsSNPs. Based on a dataset that was compiled from Swiss-Prot, a random forest model was constructed with an average accuracy value of 80.43 % and an MCC value of 0.60 in a rigorous tenfold crossvalidation test. For an independent dataset, our model achieved an accuracy of 88.05 % and an MCC of 0.67. Compared with previous studies, our approach presented superior prediction ability. Results showed that the incorporated PPI network topological features outperform conventional features. Our further analysis indicated that disease-related proteins are topologically different from other proteins. This study suggested that nsSNPs may share some topological information of proteins and the change of topological attributes could provide clues in illustrating functional shift due to nsSNPs.     

On the functional and structural characterization of hubs in protein–protein interaction networks

A number of interesting issues have been addressed on biological networks about their global and local properties. The connection between the topological properties of proteins in Protein–Protein Interaction (PPI) networks and their biological relevance has been investigated focusing on hubs, i.e. proteins with a large number of interacting partners. We will survey the literature trying to answer the following questions: Do hub proteins have special biological properties? Do they tend to be more essential than non-hub proteins? Are they more evolutionarily conserved? Do they play a central role in modular organization of the protein interaction network? Are there structural properties that characterize hub proteins?     

Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein–protein interaction network

Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. “Cancer driver” theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein–protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.     

Is the concentration of C-reactive protein in bacteraemia associated with age?

Background  

C-reactive protein (CRP) is an indicator of inflammation, and is often used in the diagnosis of bacterial infections. It is poorly known whether CRP in bacterial infection is age-dependent.     

Identification of mitogen-activated protein kinase kinase gene family and MKK–MAPK interaction network in maize

Plant mitogen-activated protein kinases (MAPK) are involved in important processes, including stress signaling and development. MAPK kinases (MAPKK, MKK) have been investigated in several plant species including Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, and Brachypodium distachyon. In the present study, nine putative maize MKK genes have been identified. Analysis of the conserved protein motifs, exon–intron junctions and intron phase has revealed high levels of conservation within the phylogenetic groups. Next, we defined four new ZmMKK–ZmMPK interactions using yeast two-hybrid. Finally, we examined the biological functions of the ZmMKK4 gene. Overexpression of ZmMKK4 in Arabidopsis conferred tolerance to oxidative stress by increased germination rate and early seedling growth compared with WT plants. Taken together, we provide a comprehensive bioinformatics analysis of the MKK gene family in maize genome and our data provide an important foundation for further functional study of MAPK and MKK families in maize.     

Structural and functional analysis of the tandem β-zipper interaction of a Streptococcal protein with human fibronectin

Bacterial fibronectin-binding proteins (FnBPs) contain a large intrinsically disordered region (IDR) that mediates adhesion of bacteria to host tissues, and invasion of host cells, through binding to fibronectin (Fn). These FnBP IDRs consist of Fn-binding repeats (FnBRs) that form a highly extended tandem β-zipper interaction on binding to the N-terminal domain of Fn. Several FnBR residues are highly conserved across bacterial species, and here we investigate their contribution to the interaction. Mutation of these residues to alanine in SfbI-5 (a disordered FnBR from the human pathogen Streptococcus pyogenes) reduced binding, but for each residue the change in free energy of binding was <2 kcal/mol. The structure of an SfbI-5 peptide in complex with the second and third F1 modules from Fn confirms that the conserved FnBR residues play equivalent functional roles across bacterial species. Thus, in SfbI-5, the binding energy for the tandem β-zipper interaction with Fn is distributed across the interface rather than concentrated in a small number of "hot spot" residues that are frequently observed in the interactions of folded proteins. We propose that this might be a common feature of the interactions of IDRs and is likely to pose a challenge for the development of small molecule inhibitors of FnBP-mediated adhesion to and invasion of host cells.