Computational Identification of Protein-Protein Interactions in Rice Based on the Predicted Rice Interactome Network

Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/ prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box do- main-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.     

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data

Coronary artery disease(CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism(SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium(WTCCC) SNP datasets of CAD and control samples were used to assess the jointeffect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction(PPI)knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease.These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.     

Deciphering Ascorbic Acid Regulatory Pathways in Ripening Tomato Fruit Using a Weighted Gene Correlation Network Analysis Approach

Genotype is generally determined by the co-expression of diverse genes and multiple regulatory pathways in plants. Gene co-expression analysis combining with physiological trait data provides very important information about the gene function and regulatory mechanism. L-Ascorbic acid （AsA）, which is an essential nutrient component for human health and plant metabolism, plays key roles in diverse biological processes such as cell cycle, cell expansion, stress resistance, hormone synthesis, and signaling. Here, we applied a weighted gene correlation network analysis approach based on gene expression values and AsA content data in ripening tomato （Solanum lycopersicum L.） fruit with different AsA content levels, which leads to identification of AsA relevant modules and vital genes in AsA regulatory pathways. Twenty- four modules were compartmentalized according to gene expression profiling. Among these modules, one negatively related module containing genes involved in redox processes and one positively related module enriched with genes involved in AsA biosynthetic and recycling pathways were further analyzed. The present work herein indicates that redox pathways as well as hormone-signal pathways are closely correlated with AsA accumulation in ripening tomato fruit, and allowed us to prioritize candidate genes for follow-up studies to dissect this interplay at the biochemical and molecular level.     

The Chloroplast Kinase Network： New Insights from Large-Scale Phosphoproteome Profiling

Protein phosphorylation is one of the most important posttranslational modifications in eukaryotic cells and affects almost all basic cellular processes. The chloroplast as plant-specific cell organelle with important metabolic functions is integrated into the cellular signaling and phosphorylation network. Recent large-scale chloroplast phosphoproteome analyses in Arabidopsis have provided new information about phosphorylation targets and expanded the list of chloroplast metabolic and regulatory functions that are potentially controlled by protein phosphorylation. Phosphorylated peptides identified from chloroplast proteins provide new insights into phosphorylation motifs, protein kinase activities, and substrate utilization. Phosphorylation sites in protein kinases can reveal chloroplast phosphorylation cascades that may network different functions by integrating signaling chains. Our review provides a meta-analysis of currently available chloroplast phosphoproteome information and discusses biological insights from large-scale chloroplast phosphoprotein profiling as well as technological constraints of kinase network analysis.     

Global identification of CobB interactors by an Escherichia coli proteome microarray

Protein acetylation is one of the most abundant post translational modifications and plays critical roles in many important biological processes. Based on the recent advances in mass spectrometry technology, in bacteria, such as Escherichia coli, tremendous acetylated proteins and acetylation sites have been identified. However, only one protein deacetylase, i.e. CobB, has been identified in E. coli so far. How CobB is regulated is still elusive. One right strategy to study the regulation of CobB is to globally identify its interacting proteins. In this study, we used a proteome microarray containing 4000 affinitypurified E. coli proteins to globally identify CobB interactors, and finally identified 183 binding proteins of high stringency. Bioinformatics analysis showed that these interacting pro teins play a variety of roles in a wide range of cellular func tions and are highly enriched in carboxylic acid metabolic process and hexose catabolic process, and also enriched in transferase and hydrolase. We further used biolayer inter ferometry to analyze the interaction and quantify the kinetic parameters of putative CobB interactors, and clearly showed that Cobb could strongly interact with TopA and AccC. The novel CobB interactors that we identified could serve as a start point for further functional analysis.     

Cell Wall,Cytoskeleton, and Cell Expansion in Higher Plants

To accommodate two seemingly contradictory biological roles in plant physiology, providing both the rigid structural support of plant cells and the adjustable elasticity needed for cell expansion, the composition of the plant cell wall has evolved to become an intricate network of cellulosic, hemicellulosic, and pectic polysaccharides and protein. Due to its complexity, many aspects of the cell wall influence plant cell expansion, and many new and insightful observations and technologies are forthcoming. The biosynthesis of cell wall polymers and the roles of the variety of proteins involved in polysaccharide synthesis continue to be characterized. The interactions within the cell wall polymer network and the modification of these interactions provide insight into how the plant cell wall provides its dual function. The complex cell wall architecture is controlled and organized in part by the dynamic intracellular cytoskeleton and by diverse trafficking pathways of the cell wall polymers and cell wall-related machinery. Meanwhile, the cell wall is continually influenced by hormonal and integrity sensing stimuli that are perceived by the cell. These many processes cooperate to construct, maintain, and manipulate the intricate plant cell wall--an essential structure for the sustaining of the plant stature, growth, and life.     

Development of a soluble PTD-HPV18E7 fusion protein and its functional characterization in eukaryotic cells

Though accumulated evidence has demonstrated the transformation capacity of human papillomavirus （HPV） type 18 protein E7, the underlying mechanism is still arguable. Developing a protein transduction domain （PTD）-linked E7 molecule is a suitable strategy for assessing the biological functions of the protein. In the present study, HPV18 E7 protein fused to an N-terminal PTD was expressed in the form of glutathione S-transferase fusion protein in Escherichia coli with pGEX-4T- 3 vector. After glutathione-Sepharose 4B bead affinity purification, immunoblot identification and thrombin cleavage, the PTD-18E7 protein showed structural and functional activity in that it potently transduced the ceils and localized into their nuclei. The PTD-18E7 protein transduced the NIH3T3 ceils in 30 min and remained stable for at least 24 h. In addition, the PTD-18E7 protein interacted with retinoblastoma protein （pRB） and caused pRB degradation in the transduced NIH3T3 cells. In contrast to the pRB level, p27 protein level was elevated in the transduced NIH3T3 cells. The PTD-18E7 protein gives us a new tool to study the biological functions of the HPV E7 protein.     

Bioinformatics analyses for signal transduction networks

Research in signaling networks contributes to a deeper understanding of organism living activities. With the development of experimental methods in the signal transduction field, more and more mechanisms of signaling pathways have been discovered. This paper introduces such popular bioin-formatics analysis methods for signaling networks as the common mechanism of signaling pathways and database resource on the Internet, summerizes the methods of analyzing the structural properties of networks, including structural Motif finding and automated pathways generation, and discusses the modeling and simulation of signaling networks in detail, as well as the research situation and tendency in this area. Now the investigation of signal transduction is developing from small-scale experiments to large-scale network analysis, and dynamic simulation of networks is closer to the real system. With the investigation going deeper than ever, the bioinformatics analysis of signal transduction would have immense space for development and application.     

An Integrative Meta-analysis of MicroRNAs in Hepatocellular Carcinoma

We aimed to shed new light on the roles of microRNAs （miRNAs） in liver cancer using an integrative in silico bioinformatics analysis. A new protocol for target prediction and functional analysis is presented and applied to the 26 highly differentially deregulated miRNAs in hepatocellular carcinoma. This framework comprises： （1） the overlap of prediction results by four out of five target prediction tools, including TargetScan, PicTar, miRanda, DIANA-microT and miRDB （combining machine-learning, alignment, interaction energy and statistical tests in order to minimize false positives）, （2） evidence from previous microarray analysis on the expression of these targets, （3） gene ontology （GO） and pathway enrichment analysis of the miRNA targets and their pathways and （4） linking these results to oncogenesis and cancer hallmarks. This yielded new insights into the roles of miRNAs in cancer hallmarks. Here we presented several key targets and hundreds of new targets that are significantly enriched in many new cancer-related hallmarks. In addition, we also revealed some known and new oncogenic pathways for liver cancer. These included the famous MAPK, TGFβ and cell cycle pathways. New insights were also provided into Wnt signaling, prostate cancer, axon guidance and oocyte meiosis pathways. These signaling and developmental pathways crosstalk to regulate stem cell transformation and implicate a role of miRNAs in hepatic stem cell deregulation and cancer development. By analyzing their complete interactome, we proposed new categorization for some of these miRNAs as either tumor-suppressors or oncomiRs with dual roles. Therefore some of these miRNAs may be addressed as therapeutic targets or used as therapeutic agents. Such dual roles thus expand the view of miRNAs as active maintainers of cellular homeostasis.     

BiNoM: a Cytoscape plugin for manipulating and analyzing biological networks

BiNoM (Biological Network Manager) is a new bioinformatics software that significantly facilitates the usage and the analysis of biological networks in standard systems biology formats (SBML, SBGN, BioPAX). BiNoM implements a full-featured BioPAX editor and a method of 'interfaces' for accessing BioPAX content. BiNoM is able to work with huge BioPAX files such as whole pathway databases. In addition, BiNoM allows the analysis of networks created with CellDesigner software and their conversion into BioPAX format. BiNoM comes as a library and as a Cytoscape plugin which adds a rich set of operations to Cytoscape such as path and cycle analysis, clustering sub-networks, decomposition of network into modules, clipboard operations and others. AVAILABILITY: Last version of BiNoM distributed under the LGPL licence together with documentation, source code and API are available at http://bioinfo.curie.fr/projects/binom     

Gastric cancer biomarkers; A systems biology approach

Gastric cancer is one of the most fatal cancers in the world. Many efforts in recent years have attempted to find effective proteins in gastric cancer. By using a comprehensive list of proteins involved in gastric cancer, scientists were able to retrieve interaction information. The study of protein-protein interaction networks through systems biology based analysis provides appropriate strategies to discover candidate proteins and key biological pathways.In this study, we investigated dominant functional themes and centrality parameters including betweenness as well as the degree of each topological clusters and expressionally active sub-networks in the resulted network. The results of functional analysis on gene sets showed that neurotrophin signaling pathway, cell cycle and nucleotide excision possess the strongest enrichment signals. According to the computed centrality parameters, HNF4A, TAF1 and TP53 manifested as the most significant nodes in the interaction network of the engaged proteins in gastric cancer. This study also demonstrates pathways and proteins that are applicable as diagnostic markers and therapeutic targets for future attempts to overcome gastric cancer.     

Comparative analysis of differential proteome-wide protein-protein interaction network of Methanobrevibacter ruminantium M1

A proteome-wide protein-protein interaction (PPI) network of Methanobrevibacter ruminantium M1 (MRU), a predominant rumen methanogen, was constructed from its metabolic genes using a gene neighborhood algorithm and then compared with closely related rumen methanogens Using proteome-wide PPI approach, we constructed network encompassed 2194 edges and 637 nodes interacting with 634 genes. Network quality and robustness of functional modules were assessed with gene ontology terms. A structure-function-metabolism mapping for each protein has been carried out with efforts to extract experimental PPI concomitant information from the literature. The results of our study revealed that some topological properties of its network were robust for sharing homologous protein interactions across heterotrophic and hydrogenotrophic methanogens. MRU proteome has shown to establish many PPI sub-networks for associated metabolic subsystems required to survive in the rumen environment. MRU genome found to share interacting proteins from its PPI network involved in specific metabolic subsystems distinct to heterotrophic and hydrogenotrophic methanogens. Across these proteomes, the interacting proteins from differential PPI networks were shared in common for the biosynthesis of amino acids, nucleosides, and nucleotides and energy metabolism in which more fractions of protein pairs shared with Methanosarcina acetivorans. Our comparative study expedites our knowledge to understand a complex proteome network associated with typical metabolic subsystems of MRU and to improve its genome-scale reconstruction in the future.     

GIANT: A Cytoscape Plugin for Modular Networks

Network analysis provides deep insight into real complex systems. Revealing the link between topological and functional role of network elements can be crucial to understand the mechanisms underlying the system. Here we propose a Cytoscape plugin (GIANT) to perform network clustering and characterize nodes at the light of a modified Guimerà-Amaral cartography. This approach results into a vivid picture of the a topological/functional relationship at both local and global level. The plugin has been already approved and uploaded on the Cytoscape APP store.     

Computational identification of phospho-tyrosine sub-networks related to acanthocyte generation in neuroacanthocytosis

Acanthocytes, abnormal thorny red blood cells (RBC), are one of the biological hallmarks of neuroacanthocytosis syndromes (NA), a group of rare hereditary neurodegenerative disorders. Since RBCs are easily accessible, the study of acanthocytes in NA may provide insights into potential mechanisms of neurodegeneration. Previous studies have shown that changes in RBC membrane protein phosphorylation state affect RBC membrane mechanical stability and morphology. Here, we coupled tyrosine-phosphoproteomic analysis to topological network analysis. We aimed to predict signaling sub-networks possibly involved in the generation of acanthocytes in patients affected by the two core NA disorders, namely McLeod syndrome (MLS, XK-related, Xk protein) and chorea-acanthocytosis (ChAc, VPS13A-related, chorein protein). The experimentally determined phosphoproteomic data-sets allowed us to relate the subsequent network analysis to the pathogenetic background. To reduce the network complexity, we combined several algorithms of topological network analysis including cluster determination by shortest path analysis, protein categorization based on centrality indexes, along with annotation-based node filtering. We first identified XK- and VPS13A-related protein-protein interaction networks by identifying all the interactomic shortest paths linking Xk and chorein to the corresponding set of proteins whose tyrosine phosphorylation was altered in patients. These networks include the most likely paths of functional influence of Xk and chorein on phosphorylated proteins. We further refined the analysis by extracting restricted sets of highly interacting signaling proteins representing a common molecular background bridging the generation of acanthocytes in MLS and ChAc. The final analysis pointed to a novel, very restricted, signaling module of 14 highly interconnected kinases, whose alteration is possibly involved in generation of acanthocytes in MLS and ChAc.     

基于Cytoscape的蛋白质网络可视化聚类分析插件

蛋白质网络聚类是识别功能模块的重要手段,不仅有利于理解生物系统的组织结构,对预测蛋白质功能也具有重要的意义.聚类结果的可视化分析是实现蛋白质网络聚类的有效途径.本论文基于开源的Cytoscape平台,设计并实现了一个蛋白质网络聚类分析及可视化插件CytoCluster.该插件集成了MCODE,FAG-EC,HC-PIN,OH-PIN,IPCA,EAGLE等六种典型的聚类算法;实现了聚类结果的可视化,将分析所得的clusters以缩略图列表的形式直观地显示出来,对于单个cluster,可显示在原网络中的位置,并能生成相应的子图单独显示;可对聚类结果进行导出,记录了算法名称、参数、聚类结果等信息.该插件具有良好的扩展性,提供了统一的算法接口,可不断添加新的聚类算法.     

Identification of potential host proteins for influenza A virus based on topological and biological characteristics by proteome-wide network approach

A proteome-wide network approach was performed to characterize significant patterns of influenza A virus (IAV)-human interactions, and to further identify potentially valuable targets for prophylactic and therapeutic interventions. Topological analysis demonstrated a strong tendency for IAV to interplay with highly connected and central proteins located in sparsely connected sub-networks. Additionally, functional analysis based on biological process revealed a number of functional groups overrepresented for IAV interactions, in which regulation of cell death and apoptosis, and phosphorus metabolic process is the most highly enriched. In order to investigate whether these topological and biological features are significant enough to distinguish IAV targets from human proteome, a discrimination model was constructed based on these features using support vector machine coupled with genetic algorithm. The average result of overall prediction accuracy is 71.04% by leave-one-out across validation test. The optimized classifier was then applied to 9706 human proteins. As a result, 1418 novel genes were identified from human interactome, some of which were experimentally validated by others' works to be important for IAV infection. The findings presented in this study might be important in discovering new drug targets for therapeutic treatments as well as revealing topological features and functional properties specific for viral infection.     

Structural analysis of metabolic networks based on flux centrality

Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell’s biochemistry.We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network.     

Breaking down Complex Saproxylic Communities: Understanding Sub-Networks Structure and Implications to Network Robustness

Saproxylic insect communities inhabiting tree hollow microhabitats correspond with large food webs which simultaneously are constituted by multiple types of plant-animal and animal-animal interactions, according to the use of trophic resources (wood- and insect-dependent sub-networks), or to trophic habits or interaction types (xylophagous, saprophagous, xylomycetophagous, predators and commensals). We quantitatively assessed which properties of specialised networks were present in a complex networks involving different interacting types such as saproxylic community, and how they can be organised in trophic food webs. The architecture, interacting patterns and food web composition were evaluated along sub-networks, analysing their implications to network robustness from random and directed extinction simulations. A structure of large and cohesive modules with weakly connected nodes was observed throughout saproxylic sub-networks, composing the main food webs constituting this community. Insect-dependent sub-networks were more modular than wood-dependent sub-networks. Wood-dependent sub-networks presented higher species degree, connectance, links, linkage density, interaction strength, and were less specialised and more aggregated than insect-dependent sub-networks. These attributes defined high network robustness in wood-dependent sub-networks. Finally, our results emphasise the relevance of modularity, differences among interacting types and interrelations among them in modelling the structure of saproxylic communities and in determining their stability.