Expression Pattern, Interaction Network, and Functional Analysis of the Arabidopsis Botrytis Susceptible1 Interactor

E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that mediates the ubiquitination of BOS1 (Botrytis susceptible1), a transcription factor involved in stress and pathogen responses. Although BOI is an E3 ligase, there are reports to show that BOI interacts with target proteins such as DELLAs or CONSTANS to repress gibberellin responses and flowering without the degradation of the target proteins. In this article, we utilize diversified methods to comprehensively analyze the expression pattern, interaction network and function of BOI gene. Firstly, 1800 bp upstream region of BOI gene from Arabidopsis thaliana (Arabidopsis) genome was isolated, and fused GUS reporter gene. The resulting expression cassette was introduced into wild-type Arabidopsis through Agrobacterium-mediated transformation. The result demonstrated that BOI gene was expressed predominantly in leaves, siliques, young roots, and flowering tissues, indicating that BOI gene may be involved in multiple processes in plant growth and development in Arabidopsis. Besides, eight candidate interacting proteins were obtained from the Arabidopsis cDNA library via yeast two-hybrid technology, including EXO70E2 (AT5G61010), WRKY7 (AT4G24240), WRKY11 (AT4G31550), WRKY17 (AT2G24570), UBP20 (AT4G17895), L5 (AT1G12290), SAUR9 (AT4G36110) and TCP21 (AT5G08330). Functional analysis of these candidate interacting proteins manifested that they related to multiple pathways, including biological and abiotic stress, programmed cell death, protein degradation, material metabolism and transcriptional regulation. In addition, the results of the transient assay proclaimed that BOI protein affects the protein stability of EXO70E2 and L5 through its E3 ubiquitin ligase activity. Our results provide novel clues for a better understanding of molecular mechanisms underlying BOI-mediated regulations.

Construction of gene/protein interaction networks and enrichment pathway analysis for paroxysmal nocturnal hemoglobinuria and aplastic anemia

Background:To develop a protein-protein interaction network of Paroxysmal nocturnal hemoglobinuria(PNH)and Aplastic anemia(AA)based on genetic genes and to predict pathways underlying the molecular complexes in the network.Methods:In this research,the PNH and AA-related genes were screened through Online Mendelian Inheritance in Man(OMIM).The plugins and Cytoscape were used to search literature and build a protein-protein interaction network.Results:The protein-protein interaction network contains two molecular complexes that are five higher than the correlation integral values.The target genes of this study were obtained:CD59,STAT3,TERC,TNF,AKT1,C5AR1,EPO,IL6,IL10 and so on.We also found that many factors regulate biological behaviors:neutrophils,macrophages,vascular endothelial growth factor,immunoglobulin,interleukin,cytokine receptor,interleukin-6 receptor,tumor necrosis factor,and so on.This research provides a bioinformatics foundation for further explaining the mechanism of common development of both.Conclusion:This indicates that the PNH and AA is a complex process regulated by many cellular pathways and multiple genes.

Protein interaction network related to Helicobacter pylori infection response

AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.

Spatial Interaction Network Analysis of Crude Oil Trade Relations between Countries along the Belt and Road

Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.

Transcriptome Profile Based on Protein-Protein Interaction Networks Provides a Set of Core Genes for Understanding the Metabolic Mechanisms of the Egg-Protecting Behavior in Amphioctopus fangsiao

Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms associated with behaviors of Am-phioctopus fangsiao are still unclear.Moreover,as a factor affecting the normal growth of A.fangsiao,egg protection has rarely been considered in previous behavioral studies.In this research,we analyzed the transcriptome profile of gene expression in A.fangsiao egg-unprotected larvae and egg-protected larvae,and identified 818 differentially expressed genes(DEGs).We used GO and KEGG enrichment analyses to search for metabolism-related DEGs.Protein-protein interaction networks were constructed to examine the interactions between metabolism-related genes.Twenty hub genes with multiple protein-protein interaction relationships or that were involved in multiple KEGG signaling pathways were obtained and verified by quantitative RT-PCR.We first studied the effects of egg protection on the metabolism of A.fangsiao larvae by means of protein-protein interaction networks,and the results provide va-luable gene resources for understanding the metabolism of invertebrate larvae.The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.

Analysis of mechanism on Indigo Naturalis in treating chronic myelocytic leukemia based on two-dimentional model of protein-protein interaction network-moleculardocking technique

To explore the molecular mechanism of Ind-igo Naturalis in intervening chronic myelocytic leukemia （CML） under the guidance of protein-protein interaction network, the molecular docking technique and in vitro cell experiment were chosen. CML-related genes were obtained from the online mendelian inheritance in man database （OMIM）, then String 10. 0 was used for text mining and constructing the CML protein-protein interaction network. The interaction data were input in Cytoscape 3. 4. 0 software. Plug-in CentiScaPe 2. 1 was used for implement topology analysis. Small active substances of Indigo Naturalis were obtained from a third-party database, which were optimized by Chemoffice 8. 0 and Sybyl 8. 1, then small molecular ligand library was obtained. The molecular docking was carried out by Surflex-Dock module, the key target was received after scoring. Protein-protein interaction network of CML was constructed, which was consisted of 425 nodes （ proteins） and 2 799 sides （ interactions）. The key gene J.AK2 was got. CML is a polygenic disease and JAK2 is likely to be a key node.

Yeast protein-protein interaction network model based on biological experimental data

Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.

Seasonal variation and host sex affect bat–bat fly interaction networks in the Amazonian savannahs

Bats are the second-most diverse group of mammals in the world,and bat flies are their main parasites.However,significant knowledge gaps remain regarding these antagonistic interactions,especially since diverse factors such as seasonality and host sex can affect their network structures.Here,we explore the influence of such factors by comparing species richness and composition of bat flies on host bats,as well as specialization and modularity of bat–bat fly interaction networks between seasons and adult host sexes.We captured bats and collected their ectoparasitic flies at 10 sampling sites in the savannahs of AmapáState,northeastern region of the Brazilian Amazon.Despite female bats being more parasitized and recording greater bat fly species richness in the wet season,neither relationship was statistically significant.The pooled network could be divided into 15 compartments with 54 links,and all subnetworks comprised>12 compartments.The total number of links ranged from 27 to 48(for the dry and wet seasons,respectively),and female and male subnetworks had 44 and 41 links,respectively.Connectance values were very low for the pooled network and for all subnetworks.Our results revealed higher bat fly species richness and abundance in the wet season,whereas specialization and modularity were higher in the dry season.Moreover,the subnetwork for female bats displayed higher specialization and modularity than the male subnetwork.Therefore,both seasonality and host sex contribute in different ways to bat–bat fly network structure.Future studies should consider these factors when evaluating bat–bat fly interaction networks.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Effect of Apis mellifera on community composition of local pollinator bees and their pollination network in Qinling Mountains and surrounding areas

The Qinling Mountains, known for their rich vegetation and diverse pollinating insects, have seen a significant decline in bee species richness and abundance over recent decades, largely due to the introduction and spread of Apis mellifera. This decline has caused cascading effects on the region's community structure and ecosystem stability. To improve the protection of native bees in the natural and agricultural landscape of the Qinling Mountains and its surrounding areas, we investigated 33 sampling sites within three habitats: forest, forest-agriculture ecotones, and farmland. Using a generalized linear mixing model, t-test, and other data analysis methods, we explored the impact of Apis mellifera on local pollinator bee richness, abundance, and the pollination network in different habitats in these regional areas. The results show that(1)Apis mellifera significantly negatively affects the abundance and richness of wild pollinator bees,while Apis cerana abundance is also affected by beekeeping conditions.(2)There are significant negative effects of Apis mellifera on the community structure of pollinator bees in the Qinling Mountains and its surrounding areas: the Shannon-Wiener diversity index, Pielou evenness index, and Margalef richness index of bee communities at sites with Apis mellifera influence were significantly lower than those at sites without Apis mellifera influence.(3)The underlying driver of this effect is the monopolization of flowering resources by Apis mellifera. This species tends to visit flowering plants with large nectar sources, which constitute a significant portion of the local plant community. By maintaining a dominant role in the bee-plant pollination network, Apis mellifera competitively displaces native pollinator bees, reducing their access to floral resources. This ultimately leads to a reduction in local bee-plant interactions, decreasing the complexity and stability of the pollination network. These findings highlight the need for targeted conservation efforts to protect native pollinator species and maintain the ecological balance in the Qinling Mountains.

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks

Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies.However,it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments.With the advances of the high-throughput techniques,a large number of protein-protein interactions have been produced.Therefore,to address this issue,several methods based on protein interaction network have been proposed.In this paper,we propose a shortest path-based algorithm,named SPranker,to prioritize disease-causing genes in protein interaction networks.Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes,we further propose an improved algorithm SPGOranker by integrating the semantic similarity of gene ontology(GO)annotations.SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account.The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches,ICN,VS and RWR.The experimental results show that SPranker and SPGOranker outperform ICN,VS,and RWR for the prioritization of orphan disease-causing genes.Importantly,for the case study of severe combined immunodeficiency,SPranker and SPGOranker predict several novel causal genes.

Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

Curcumin, the medically active component from Curcuma Tonga (Turmeric), is widely used to treat inflammatory diseases. Protein interaction network (PIN) analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein protein interactions (PPIs) were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO) enrichment analysis based on molecular complex detection (MCODE). A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation. (C) 2015 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.

Protein interaction networks:centrality,modularity,dynamics,and applications

In the post-genomic era,proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies.Especially the rapid accumulation of protein-protein interactions(PPIs)provides a foundation for constructing protein interaction networks(PINs),which can furnish a new perspective for understanding cellular organizations,processes,and functions at network level.In this paper,we present a comprehensive survey on three main characteristics of PINs:centrality,modularity,and dynamics.1)Different centrality measures,which are used to calculate the importance of proteins,are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information;2)Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced;3)The dynamics of proteins,PPIs and sub-networks are discussed,respectively.Finally,the main applications of PINs in the complex diseases are reviewed,and the challenges and future research directions are also discussed.

Exploring Mechanisms of Panax notoginseng Saponins in Treating Coronary Heart Disease by Integrating Gene Interaction Network and Functional Enrichment Analysis

Objective： To investigate the mechanisms of Panax notoginseng saponins（PNS） in treating coronary heart disease（CHD） by integrating gene interaction network and functional enrichment analysis. Methods： Text mining was used to get CHD and PNS associated genes. Gene–gene interaction networks of CHD and PNS were built by the Gene MANIA Cytoscape plugin. Advanced Network Merge Cytoscape plugin was used to analyze the two networks. Their functions were analyzed by gene functional enrichment analysis via DAVID Bioinformatics. Joint subnetwork of CHD network and PNS network was identified by network analysis. Results： The 11 genes of the joint subnetwork were the direct targets of PNS in CHD network and enriched in cytokine-cytokine receptor interaction pathway. PNS could affect other 85 genes by the gene–gene interaction of joint subnetwork and these genes were enriched in other 7 pathways. The direct mechanisms of PNS in treating CHD by targeting cytokines to relieve the inflammation and the indirect mechanisms of PNS in treating CHD by affecting other 7 pathways through the interaction of joint subnetwork of PNS and CHD network. The genes in the 7 pathways could be potential targets for the immunologic adjuvant, anticoagulant, hypolipidemic, anti-platelet and anti-hypertrophic activities of PNS. Conclusion： The key mechanisms of PNS in treating CHD could be anticoagulant and hypolipidemic which are indicated by analyzing biological functions of hubs in the merged network.

Proteomic dissection of biological pathways/processes through profiling protein-protein interaction networks

Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.

MINE:A Method of Multi-Interaction Heterogeneous Information Network Embedding

Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.

Investigating the Impact of Ocean Acidification on Anti-Stress Mechanisms in Sepia esculenta Larvae Based on Transcriptome Profiling

With the rapid development of oil,energy,power and other industries,CO_(2) emissions rise sharply,which will cause a large amount of CO_(2) in the air be absorbed by the ocean and lead to ocean acidification.The growth and development of organisms can be seriously affected by acidified seawater.Sepia esculenta is a mollusk with high nutritional and economic value and is widely cultured in offshore waters of China.Larvae are the early life forms of the organism and are more vulnerable to changes in the external environment.Too low pH will lead to some adverse reactions in larvae,which will affect metabolism,immune response and other life activities.In this study,we sequenced the transcriptome of S.esculenta subjected to acidified seawater stress and identified 1072differentially expressed genes(DEGs).The detected atypical expression of DEGs substantiates cellular malformation and translocation in S.esculenta under low pH stimulation.Simultaneously,this also substantiates the notable impact of ocean acidification on mollusks.These DEGs were used for functional enrichment analysis of GO and KEGG,and the top twenty items of the biological process classification in GO terms and 11 KEGG signaling pathways were significantly enriched.Finally,the constructed proteinprotein interaction network(PPI)was used to analyze protein-protein interactions,and 12 key DEGs and 3 hub genes were identified.The reliability of 12 genes was verified by quantitative RT-PCR.A comprehensive analysis of the KEGG signaling pathway and PPI revealed that ocean acidification leads to abnormalities in lipid metabolism in S.esculenta larvae,which can lead to cancer development and metastasis,accompanied by some degree of inflammation.The results of the study will help to further investigate the physiological processes of S.esculenta when stimulated by ocean acidification,and provide a reference to cope with the captive breeding of S.esculenta affected by acidification.

符号互动:品牌数字传播的社会认同建构

网络化个体主义是推动品牌社会认同加速离散的重要力量,由此造成品牌社会认同的危机,然而,网络个体的重新聚集也成为品牌构建新社会认同的契机。因此,探索全新的品牌数字传播对社会认同的建构理路,对于理解广告品牌研究的理论与实践具有重要意义。研究基于符号互动理论,对“后浪现象”这一数字传播典型案例进行探讨,从个体、群际和社会三重互动情境着手分析,提炼出品牌数字传播构建社会认同的两个关键环节:“情感化的符号生产”与“仪式化的公众文本创作”。在个体间情感认同形成层面,符号的生产促成情感心理空间的连接和扩大,其是形成情感认同的中间纽带;在群际间层面,人们通过互动仪式链参与公众文本创作,促进群际间的意义生产,突破圈层的意义边界,助力群际间认同。

RNA sequencing of exosomes secreted by fibroblast and Schwann cells elucidates mechanisms underlying peripheral nerve regeneration

Exosomes exhibit complex biological functions and mediate a variety of biological processes,such as promoting axonal regeneration and functional recove ry after injury.Long non-coding RNAs(IncRNAs)have been reported to play a crucial role in axonal regeneration.Howeve r,the role of the IncRNA-microRNAmessenger RNA(mRNA)-competitive endogenous RNA(ceRNA)network in exosome-mediated axonal regeneration remains unclear.In this study,we performed RNA transcriptome sequencing analysis to assess mRNA expression patterns in exosomes produced by cultured fibroblasts(FC-EXOs)and Schwann cells(SCEXOs).Diffe rential gene expression analysis,Gene Ontology analysis,Kyoto Encyclopedia of Genes and Genomes analysis,and protein-protein intera ction network analysis were used to explo re the functions and related pathways of RNAs isolated from FC-EXOs and SC-EXOs.We found that the ribosome-related central gene Rps5 was enriched in FC-EXOs and SC-EXOs,which suggests that it may promote axonal regeneration.In addition,using the miRWalk and Starbase prediction databases,we constructed a regulatory network of ceRNAs targeting Rps5,including 27 microRNAs and five IncRNAs.The ceRNA regulatory network,which included Ftx and Miat,revealed that exsosome-derived Rps5 inhibits scar formation and promotes axonal regeneration and functional recovery after nerve injury.Our findings suggest that exosomes derived from fibro blast and Schwann cells could be used to treat injuries of peripheral nervous system.

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.