Herpesviral protein networks and their interaction with the human proteome

The comprehensive yeast two-hybrid analysis of intraviral protein interactions in two members of the herpesvirus family, Kaposi sarcoma-associated herpesvirus (KSHV) and varicella-zoster virus (VZV), revealed 123 and 173 interactions, respectively. Viral protein interaction networks resemble single, highly coupled modules, whereas cellular networks are organized in separate functional submodules. Predicted and experimentally verified interactions between KSHV and human proteins were used to connect the viral interactome into a prototypical human interactome and to simulate infection. The analysis of the combined system showed that the viral network adopts cellular network features and that protein networks of herpesviruses and possibly other intracellular pathogens have distinguishing topologies.     

An in vivo map of the yeast protein interactome

Protein interactions regulate the systems-level behavior of cells; thus, deciphering the structure and dynamics of protein interaction networks in their cellular context is a central goal in biology. We have performed a genome-wide in vivo screen for protein-protein interactions in Saccharomyces cerevisiae by means of a protein-fragment complementation assay (PCA). We identified 2770 interactions among 1124 endogenously expressed proteins. Comparison with previous studies confirmed known interactions, but most were not known, revealing a previously unexplored subspace of the yeast protein interactome. The PCA detected structural and topological relationships between proteins, providing an 8-nanometer-resolution map of dynamically interacting complexes in vivo and extended networks that provide insights into fundamental cellular processes, including cell polarization and autophagy, pathways that are evolutionarily conserved and central to both development and human health.     

High-quality binary protein interaction map of the yeast interactome network

Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.     

The cellular interactome for glycoprotein 5 of the Chinese highly pathogenic porcine reproductive and respiratory syndrome virus

The glycoprotein 5(GP5) of porcine reproductive and respiratory syndrome virus(PRRSV) is a multi-functional protein that plays important roles in virus assembly,entry and viral anti-host responses.In the present study,we investigated the cellular binding partners of GP5 by using lentivirus transduction coupled with immunoprecipitation and mass spectrometry.There were about 40 cellular proteins identified with high Confidence Icons by MS/MS.Ingenuity Pathway Analysis(IPA)indicated that these proteins could be assigned to different functional classes and networks.Furthermore,we validated some of the interactions by co-immunoprecipitation(Co-IP) and confocal microscopy,including those with mitofilin,a mitochondrial inner membrane protein that might be involved in PRRSV or GP5-induced apoptosis,and calnexin,a protein chaperone that might facilitate the folding and maturation of GP5.The interactome data contribute to understand the role and molecular mechanisms of GP5 in PRRSV pathogenesis.     

Use of logic relationships to decipher protein network organization

A major focus of genome research is to decipher the networks of molecular interactions that underlie cellular function. We describe a computational approach for identifying detailed relationships between proteins on the basis of genomic data. Logic analysis of phylogenetic profiles identifies triplets of proteins whose presence or absence obey certain logic relationships. For example, protein C may be present in a genome only if proteins A and B are both present. The method reveals many previously unidentified higher order relationships. These relationships illustrate the complexities that arise in cellular networks because of branching and alternate pathways, and they also facilitate assignment of cellular functions to uncharacterized proteins.     

Rhizosphere immunity: targeting the underground for sustainable plant health management

Managing plant health is a great challenge for modern food production and is further complicated by the lack of common ground between the many disciplines involved in disease control. Here we present the concept of rhizosphere immunity, in which plant health is considered as an ecosystem level property emerging from networks of interactions between plants, microbiota and the surrounding soil matrix. These interactions can potentially extend the innate plant immune system to a point where the rhizosphere immunity can fulfil all four core functions of a full immune system: pathogen prevention, recognition, response and homeostasis. We suggest that considering plant health from a meta-organism perspective will help in developing multidisciplinary pathogen management strategies that focus on steering the whole plant-microbe-soil networks instead of individual components. This might be achieved by bringing together the latest discoveries in phytopathology, microbiome research, soil science and agronomy to pave the way toward more sustainable and productive agriculture.     

Specialization and rarity predict nonrandom loss of interactions from mutualist networks

The loss of interactions from mutualistic networks could foreshadow both plant and animal species extinctions. Yet, the characteristics of interactions that predispose them to disruption are largely unknown. We analyzed 12 pollination webs from isolated hills ("sierras"), in Argentina, ranging from tens to thousands of hectares. We found evidence of nonrandom loss of interactions with decreasing sierra size. Low interaction frequency and high specialization between interacting partners contributed additively to increase the vulnerability of interactions to disruption. Interactions between generalists in the largest sierras were ubiquitous across sierras, but many of them lost their central structural role in the smallest sierras. Thus, particular configurations of interaction networks, along with unique ecological relations and evolutionary pathways, could be lost forever after habitat reduction.     

High-throughput mapping of a dynamic signaling network in mammalian cells

Signaling pathways transmit information through protein interaction networks that are dynamically regulated by complex extracellular cues. We developed LUMIER (for luminescence-based mammalian interactome mapping), an automated high-throughput technology, to map protein-protein interaction networks systematically in mammalian cells and applied it to the transforming growth factor-beta (TGFbeta) pathway. Analysis using self-organizing maps and k-means clustering identified links of the TGFbeta pathway to the p21-activated kinase (PAK) network, to the polarity complex, and to Occludin, a structural component of tight junctions. We show that Occludin regulates TGFbeta type I receptor localization for efficient TGFbeta-dependent dissolution of tight junctions during epithelial-to-mesenchymal transitions.     

The robustness and restoration of a network of ecological networks

Understanding species' interactions and the robustness of interaction networks to species loss is essential to understand the effects of species' declines and extinctions. In most studies, different types of networks (such as food webs, parasitoid webs, seed dispersal networks, and pollination networks) have been studied separately. We sampled such multiple networks simultaneously in an agroecosystem. We show that the networks varied in their robustness; networks including pollinators appeared to be particularly fragile. We show that, overall, networks did not strongly covary in their robustness, which suggests that ecological restoration (for example, through agri-environment schemes) benefitting one functional group will not inevitably benefit others. Some individual plant species were disproportionately well linked to many other species. This type of information can be used in restoration management, because it identifies the plant taxa that can potentially lead to disproportionate gains in biodiversity.     

Asymmetric coevolutionary networks facilitate biodiversity maintenance

The mutualistic interactions between plants and their pollinators or seed dispersers have played a major role in the maintenance of Earth's biodiversity. To investigate how coevolutionary interactions are shaped within species-rich communities, we characterized the architecture of an array of quantitative, mutualistic networks spanning a broad geographic range. These coevolutionary networks are highly asymmetric, so that if a plant species depends strongly on an animal species, the animal depends weakly on the plant. By using a simple dynamical model, we showed that asymmetries inherent in coevolutionary networks may enhance long-term coexistence and facilitate biodiversity maintenance.     

Genome-wide prediction of C. elegans genetic interactions

To obtain a global view of functional interactions among genes in a metazoan genome, we computationally integrated interactome data, gene expression data, phenotype data, and functional annotation data from three model organisms-Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster-and predicted genome-wide genetic interactions in C. elegans. The resulting genetic interaction network (consisting of 18,183 interactions) provides a framework for system-level understanding of gene functions. We experimentally tested the predicted interactions for two human disease-related genes and identified 14 new modifiers.     

真菌无毒基因克隆与抗性蛋白互作研究

无毒基因（avirulence genes, Avr）是病原物遗传因子,能诱发寄主植物产生抗病性。真菌Avr基因的克隆、编码产物结构和功能分析,以及与寄主抗性蛋白的互作机制等方面的研究对于深入了解植物的抗真菌病害分子机理具有重要意义。其编码AVR蛋白（又可以称为效应子,effector）通常富含半胱氨酸,效应子通过吸器或侵入丝被分泌到寄主细胞内促发抗性反应。在抗性反应过程中,效应子能直接或间接地被植物细胞相应的抗性蛋白识别,这种识别机制与效应子和抗性蛋白的区段相关。以几个真菌病害为例,综述了近年来有关无毒基因克隆、表达、以及与抗性蛋白的互作机制等方面的主要研究进展,以期为相关研究的深入提供参考。     

Novel understanding of Trichoderma interaction mechanisms

Trichoderma- based biofungicides are a reality in commercial agriculture, with more than 50 formulations registered worldwide as biopesticides or biofertilizers. Several research strategies have been applied to identify the main genes and compounds involved in the complex, three-way interactions between fungal antagonists, plants and microbial pathogens. Proteome and genome analyses have greatly enhanced our ability to conduct targeted and genome-based functional studies. We have obtained repr…     

Surface sites for engineering allosteric control in proteins

Statistical analyses of protein families reveal networks of coevolving amino acids that functionally link distantly positioned functional surfaces. Such linkages suggest a concept for engineering allosteric control into proteins: The intramolecular networks of two proteins could be joined across their surface sites such that the activity of one protein might control the activity of the other. We tested this idea by creating PAS-DHFR, a designed chimeric protein that connects a light-sensing signaling domain from a plant member of the Per/Arnt/Sim (PAS) family of proteins with Escherichia coli dihydrofolate reductase (DHFR). With no optimization, PAS-DHFR exhibited light-dependent catalytic activity that depended on the site of connection and on known signaling mechanisms in both proteins. PAS-DHFR serves as a proof of concept for engineering regulatory activities into proteins through interface design at conserved allosteric sites.     

Aromatic-aromatic interaction: a mechanism of protein structure stabilization

Analysis of neighboring aromatic groups in four biphenyl peptides or peptide analogs and 34 proteins reveals a specific aromatic-aromatic interaction. Aromatic pairs (less than 7 A between phenyl ring centroids) were analyzed for the frequency of pair type, their interaction geometry (separation and dihedral angle), their nonbonded interaction energy, the secondary structural locations of interacting residues, their environment, and their conservation in related molecules. The results indicate that on average about 60 percent of aromatic side chains in proteins are involved in aromatic pairs, 80 percent of which form networks of three or more interacting aromatic side chains. Phenyl ring centroids are separated by a preferential distance of between 4.5 and 7 A, and dihedral angles approaching 90 degrees are most common. Nonbonded potential energy calculations indicate that a typical aromatic-aromatic interaction has energy of between -1 and -2 kilocalories per mole. The free energy contribution of the interaction depends on the environment of the aromatic pair. Buried or partially buried pairs constitute 80 percent of the surveyed sample and contribute a free energy of between -0.6 and -1.3 kilocalories per mole to the stability of the protein's structure at physiologic temperature. Of the proteins surveyed, 80 percent of these energetically favorable interactions stabilize tertiary structure, and 20 percent stabilize quaternary structure. Conservation of the interaction in related molecules is particularly striking.     

蛋白质互作组学技术及其在植物 研究中的应用进展

蛋白质互作组学技术是一门鉴定和量化蛋白质与其他代谢物或蛋白质等分子相互作用的前沿技 术,已成为研究植物系统生物学和多组学研究的重要组成部分。近年来,基于质谱的组学技术迅速发展,也促 进蛋白质 - 代谢物相互作用（Protein-metabolite interaction, PMI）、蛋白质 - 蛋白质相互作用（ Protein-protein interaction, PPI）的发现和验证方法取得巨大进步 , 这些蛋白质互作组学技术在功能基因组和功能代谢组研究中 逐渐展示出巨大的应用潜力。系统总结了过去 10 年不同蛋白质互作组学技术（主要包括 PMI 和 PPI）的分析策略, 并详细分析了它们各自的优缺点和适用的相互作用类型,综述了蛋白质互作组学技术在植物研究领域的应用进 展,对植物蛋白质互作组学技术的应用策略和需要攻克的关键技术瓶颈进行了总结。蛋白质互作组学技术的不 断发展将进一步推动植物胞内信号转导及代谢调控通路的解析,而精准解析信号网络中关键相互作用将为植物 自身生长发育以及适应外界环境等机制研究提供重要的信息。