Proteinarium: Multi-sample protein-protein interaction analysis and visualization tool

We posit the likely architecture of complex diseases is that subgroups of patients share variants in genes in specific networks which are sufficient to give rise to a shared phenotype. We developed Proteinarium, a multi-sample protein-protein interaction (PPI) tool, to identify clusters of patients with shared gene networks. Proteinarium converts user defined seed genes to protein symbols and maps them onto the STRING interactome. A PPI network is built for each sample using Dijkstra's algorithm. Pairwise similarity scores are calculated to compare the networks and cluster the samples. A layered graph of PPI networks for the samples in any cluster can be visualized. To test this newly developed analysis pipeline, we reanalyzed publicly available data sets, from which modest outcomes had previously been achieved. We found significant clusters of patients with unique genes which enhanced the findings in the original study.     

In silico discovery and experimental validation of new protein-protein interactions

We introduce a framework for predicting novel protein-protein interactions (PPIs), based on Fisher's method for combining probabilities of predictions that are based on different data sources, such as the biomedical literature, protein domain and mRNA expression information. Our method compares favorably to our previous method based on text-mining alone and other methods such as STRING. We evaluated our algorithms through the prediction of experimentally found protein interactions underlying Muscular Dystrophy, Huntington's Disease and Polycystic Kidney Disease, which had not yet been recorded in protein-protein interaction databases. We found a 1.74-fold increase in the mean average prediction precision for dysferlin and a 3.09-fold for huntingtin when compared to STRING. The top 10 of predicted interaction partners of huntingtin were analysed in depth. Five were identified previously, and the other five were new potential interaction partners. The full matrix of human protein pairs and their prediction scores are available for download. Our framework can be extended to predict other types of relationships such as proteins in a complex, pathway or related disease mechanisms.     

蛋白质相互作用数据库

随着“蛋白质组学”的蓬勃发展和人类对生物大分子功能机制的知识积累,涌现出海量的蛋白质相互作用数据。随之,研究者开发了300多个蛋白质相互作用数据库,用于存储、展示和数据的重利用。蛋白质相互作用数据库是系统生物学、分子生物学和临床药物研究的宝贵资源。本文将数据库分为3类：（1）综合蛋白质相互作用数据库;（2）特定物种的蛋白质相互作用数据库;（3）生物学通路数据库。重点介绍常用的蛋白质相互作用数据库,包括BioGRID、STRING、IntAct、MINT、DIP、IMEx、HPRD、Reactome和KEGG等。     

A Computational Study Identifies HIV Progression-Related Genes Using mRMR and Shortest Path Tracing

Since statistical relationships between HIV load and CD4+ T cell loss have been demonstrated to be weak, searching for host factors contributing to the pathogenesis of HIV infection becomes a key point for both understanding the disease pathology and developing treatments. We applied Maximum Relevance Minimum Redundancy (mRMR) algorithm to a set of microarray data generated from the CD4+ T cells of viremic non-progressors (VNPs) and rapid progressors (RPs) to identify host factors associated with the different responses to HIV infection. Using mRMR algorithm, 147 gene had been identified. Furthermore, we constructed a weighted molecular interaction network with the existing protein-protein interaction data from STRING database and identified 1331 genes on the shortest-paths among the genes identified with mRMR. Functional analysis shows that the functions relating to apoptosis play important roles during the pathogenesis of HIV infection. These results bring new insights of understanding HIV progression.     

Integrative Analysis of Periostin in Primary and Advanced Prostate Cancer

Periostin (POSTN) is an extracellular matrix protein associated with tumor progression and shorter survival in prostate cancer (PCa). Here, we performed an integrative analysis of POSTN’s role in patients with PCa. Clinical and POSTN data from large-scale datasets were analyzed. POSTN cutoffs were identified with X-Tile, and STRING was used for protein-protein interaction analysis. In a cohort of 48 patients with metastatic castration-resistant prostate cancer (mCRPC), we used the AdnaTest platform to isolate circulating tumor cells and extract POSTN mRNA. Plasma samples were also tested for POSTN protein expression by dot blot assay. Data from large-scale datasets did not reveal any association between POSTN genetic alterations and outcome. In primary tumors, we found a significant correlation between POSTN mRNA overexpression, worse baseline prognostic features, and shorter disease-free survival. POSTN was overexpressed in mCRPC and correlated with aggressive features. In our cohort of mCRPC patients, we found a positive correlation between POSTN plasma levels and androgen-receptor variant 7 positivity and an association with shorter overall survival. Our integrative analysis shows that POSTN is associated with poor clinical features and worse outcome in patients with PCa. Further studies are warranted to uncover the function of POSTN in PCa progression and to validate the prognostic significance of POSTN in mCRPC.     

Accounting for Redundancy when Integrating Gene Interaction Databases

During the last years gene interaction networks are increasingly being used for the assessment and interpretation of biological measurements. Knowledge of the interaction partners of an unknown protein allows scientists to understand the complex relationships between genetic products, helps to reveal unknown biological functions and pathways, and get a more detailed picture of an organism''s complexity. Being able to measure all protein interactions under all relevant conditions is virtually impossible. Hence, computational methods integrating different datasets for predicting gene interactions are needed. However, when integrating different sources one has to account for the fact that some parts of the information may be redundant, which may lead to an overestimation of the true likelihood of an interaction. Our method integrates information derived from three different databases (Bioverse, HiMAP and STRING) for predicting human gene interactions. A Bayesian approach was implemented in order to integrate the different data sources on a common quantitative scale. An important assumption of the Bayesian integration is independence of the input data (features). Our study shows that the conditional dependency cannot be ignored when combining gene interaction databases that rely on partially overlapping input data. In addition, we show how the correlation structure between the databases can be detected and we propose a linear model to correct for this bias. Benchmarking the results against two independent reference data sets shows that the integrated model outperforms the individual datasets. Our method provides an intuitive strategy for weighting the different features while accounting for their conditional dependencies.     

A comparison of computational methods for identifying virulence factors

Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them.     

STRING: a database of predicted functional associations between proteins

Functional links between proteins can often be inferred from genomic associations between the genes that encode them: groups of genes that are required for the same function tend to show similar species coverage, are often located in close proximity on the genome (in prokaryotes), and tend to be involved in gene-fusion events. The database STRING is a precomputed global resource for the exploration and analysis of these associations. Since the three types of evidence differ conceptually, and the number of predicted interactions is very large, it is essential to be able to assess and compare the significance of individual predictions. Thus, STRING contains a unique scoring-framework based on benchmarks of the different types of associations against a common reference set, integrated in a single confidence score per prediction. The graphical representation of the network of inferred, weighted protein interactions provides a high-level view of functional linkage, facilitating the analysis of modularity in biological processes. STRING is updated continuously, and currently contains 261 033 orthologs in 89 fully sequenced genomes. The database predicts functional interactions at an expected level of accuracy of at least 80% for more than half of the genes; it is online at http://www.bork.embl-heidelberg.de/STRING/.     

STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene

The repeated occurrence of genes in each other’s neighbourhood on genomes has been shown to indicate a functional association between the proteins they encode. Here we introduce STRING (search tool for recurring instances of neighbouring genes), a tool to retrieve and display the genes a query gene repeatedly occurs with in clusters on the genome. The tool performs iterative searches and visualises the results in their genomic context. By finding the genomically associated genes for a query, it delineates a set of potentially functionally associated genes. The usefulness of STRING is illustrated with an example that suggests a functional context for an RNA methylase with unknown specificity. STRING is available at http://www.bork.embl-heidelberg.de/STRING     

宫颈癌差异表达基因筛选及功能分析

目的:筛选参与宫颈癌发生、发展的关键基因,为临床诊疗提供新的靶点。方法:在NCBI-GEO数据库中筛选多组宫颈癌基因表达检测数据集,利用GEO2R分析工具筛选各组数据集的差异表达基因;应用R分析筛选不同数据集之间共有的差异表达基因;利用DAVID在线分析对差异表达基因进行功能聚类和通路分析;利用STRING分析差异表达基因编码蛋白之间的相互作用关系。结果:共选择6组表达数据集,筛选得到59个差异表达基因(宫颈癌组织vs正常组织),表达差异至少达2倍,其中包含50个表达上调基因及9个表达下调基因。这些差异表达基因参与细胞周期、DNA复制、细胞分裂等生物进程。蛋白互作分析表明,这些差异表达基因多数存在相互作用。结论:利用生物信息学方法对不同来源的基因检测数据进行整合分析,有助于更准确的筛选对宫颈癌发生、发展过程具有重要作用的关键基因,本文筛选的宫颈癌差异基因为进一步研究宫颈癌发生、发展的分子机制及临床诊疗提供思路。     

Immune-related chemotactic factors were found in acute coronary syndromes by bioinformatics

DNA microarray data for thrombus-related leukocyte from patients with acute coronary syndrome (ACS) was analyzed to acquire key genes associated with ACS. Microarray data set GSE19339, including four ACS patients’ samples and four normal samples, were downloaded from Gene Expression Omnibus database. Raw data was pre-processed and differentially expressed genes (DEGs) were identified by Affy packages of R. The interaction network was established with STRING. DrugBank was retrieved to obtain relevant small molecules. A total of 487 differentially expressed genes were identified as DEGs between normal and disease samples. Among which, ten up-regulated genes belonging to chemokine family (CCL2, CCR1, CXCL3, CXCL2, CCL8, CXCL11, CCL7, IL10, CCL22 and CCL20) were related to inflammatory response. In addition, two inhibitors of CCL2 (L-Mimosine) were retrieved from the DrugBank database. Considering the roles of inflammatory response in the progression of ACS and the functions of the ten up-regulated genes, we speculated that these genes might be related to ACS. Moreover, the inhibitors could provide guidelines for future drug design acting on these genes.     

Molecular modelling of urease accessory interaction proteins of Helicobacter Pylori J 99 and predicting an interruption in interaction by Vigna radiata Defensins

Helicobacter pylori is the major causative agent of Gastric carcinoma. Significance of the urease accessory interaction proteins are emphasized in colonization of human gastric mucosa and efficient infection of H. pylori. Here an attempt is made to explore the structure and properties of urease accessory interaction proteins from Helicobacter pylori J 99. The proteins chosen for the study are ureH, ureI, nikR, groL and flgS based on the interaction map available from STRING database. The above mentioned proteins do not have a comprehensive three dimensional structure. Hence the models were generated using PSI-BLAST (Position Specific Iterative-Blast) and MODELLER 9V8. Physicochemical characterization encompasses pI, EC, AI, II and GRAVY. Secondary structure was predicted using PSI-PRED. Functional characterization was done by SOSUI and DISULFIND Servers and refinement of structure was done using Ramachandran plot analysis. RMS-Z values were calculated using Q-MEAN Server and CHIMERA was used for molecular simulation studies. Plant defensins from Vigna radiata are successfully docked to the modeled structures and thus interaction could be possibly prevented. These results will pave way for further selective inhibition of H. pylori colonization and in vivo survival by employing plant defensins from Vigna radiata (VrD1 & VrD2). The work will prove that plant defensins provides anticancer relief too.     

Analysis and prediction of protein stability based on interaction network,gene ontology,and KEGG pathway enrichment scores

Metabolic stability of proteins plays a vital role in various dedicated cellular processes. Traditional methods of measuring the metabolic stability are time-consuming and expensive. Therefore, we developed a more efficient computational approach to understand the protein dynamic action mechanisms in biological process networks. In this study, we collected 341 short-lived proteins and 824 non-short-lived proteins from U2OS; 342 short-lived proteins and 821 non-short-lived proteins from HEK293T; 424 short-lived proteins and 1153 non-short-lived proteins from HCT116; and 384 short-lived proteins and 992 non-short-lived proteins from RPE1. The proteins were encoded by GO and KEGG enrichment scores based on the genes and their neighbors in STRING, resulting in 20,681 GO term features and 297 KEGG pathway features. We also incorporated the protein interaction information from STRING into the features and obtained 19,247 node features. Boruta and mRMR methods were used for feature filtering, and IFS method was used to obtain the best feature subsets and create the models with the highest performance. The present study identified 42 features that did not appear in previous studies and classified them into eight groups according to their functional annotation. By reviewing the literature, we found that the following three functional groups were critical in determining the stability of proteins: synaptic transmission, post-translational modifications, and cell fate determination. These findings may serve as a valuable reference for developing drugs that target protein stability.     

Identification of retinoblastoma related genes with shortest path in a protein-protein interaction network

This paper presents a new method for identifying retinoblastoma related genes by integrating gene expression profile and shortest path in a functional linkage graph. With the existing protein-protein interaction data from STRING, a weighted functional linkage graph is constructed. 119 consistently differentially expressed genes between retinoblastoma and normal retina were obtained from the overlap of two gene expression studies of retinoblastoma. Then the shortest paths between each pair of these 119 genes were determined with Dijkstra's algorithm. Finally, all the genes present on the shortest paths were extracted and ranked according to their betweenness and the 119 shortest genes with a betweenness greater than 100 and with a p-value less than 0.05 were selected for further analysis. We also identified 53 retinoblastoma related miRNAs from published miRNA array data and most of the 238 (119 consistently differentially expressed genes and 119 shortest path genes) retinoblastoma genes were shown to be target genes of these 53 miRNAs. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network included more cancer genes than did the genes identified from the gene expression profiles alone. In addition, these genes also had greater functional similarity to the reported cancer genes than did the genes identified from the gene expression profiles alone. This study shows promising results and proves the efficiency of the proposed methods.     

Novel Functions of CD147 in the Mitochondria Exacerbates Melanoma Metastasis

Melanoma is an aggressive form of skin cancer characterized by rapid invasion and metastasis. CD147 is known to be functioning in cell invasion. In this study, we showed that CD147 was translocated from the cell membrane to the mitochondria in advanced melanoma. Melanoma patients with CD147 localized to the mitochondria confer a worse prognosis. The mitochondrial CD147 levels are correlated with the invasion potential of various melanoma cell lines as well as mitochondrial energy metabolism. Depletion of CD147 decreased the activity of mitochondrial complex V. STRING analysis for protein-protein interaction networks (PPIN) in CD147-depleted melanoma cells showed that mitochondrial proteins HSP60 and ATP5B, a subunit of mitochondrial complex V, were node proteins. HSP60 upregulation was correlated with a worse prognosis of melanoma patients. Co-immunoprecipitation (Co-IP) assay indicates that CD147 interacts with HSP60. These data suggested that mitochondrial CD147 may prompt HSP60 to activate ATP5B, thereby promoting the mitochondrial aerobic oxidation and the invasive abilities of melanoma cells. Correlation analysis of the data acquired from patients was helpful to draw a 5-year survival curve for patients who screened positive and negative for mitochondrial CD147. This study unravels the function of CD147 in tumor invasion and highlights it as a potential tumor therapeutic target.     

Brent Geese (<Emphasis Type="Italic">Branta bernicla</Emphasis>) Breeding Associations with Pomarine Skuas (<Emphasis Type="Italic">Stercorarius pomarinus</Emphasis>) on the Mainland Tundra

This article is based on data that were collected in the years 2000?2007, 2012, and 2014 in the vicinities of Medusa Bay (73°21′ N, 80°32′ E) and in 2002 at the mouth of the Uboynaya River (73°37′ N, 82°10′ E), in the northwestern part of the Taimyr Peninsula. In years when the abundance of lemmings is high, brent geese may breed not only near nests of snowy owls and rough-legged buzzards, but also sparsely in the mainland tundra, often without any protection. Some such nests are successfully incubated until hatching. A considerable part of these dispersed nests appears to be associated with a nest or territory of pomarine skuas that are able to scare away the main tundra predator, the arctic fox, to a distance of about 500 m from their nests. Brent geese that breed within this distance to theses nests gain additional protection against arctic foxes. However, brent geese do not display a tendency to place their nests closer to pomarine skua nests. The mean distance from geese nests to pomarine skua nests or centers of their territories comprised 2/3 of the mean distance between nests of pomarine skuas and turned out to be quite stable over the years and in two different tundra areas.