Potential Role of Pathogen Signaling in Multitrophic Plant-Microbe Interactions Involved in Disease Protection

Multitrophic interactions mediate the ability of fungal pathogens to cause plant disease and the ability of bacterial antagonists to suppress disease. Antibiotic production by antagonists, which contributes to disease suppression, is known to be modulated by abiotic and host plant environmental conditions. Here, we demonstrate that a pathogen metabolite functions as a negative signal for bacterial antibiotic biosynthesis, which can determine the relative importance of biological control mechanisms available to antagonists and which may also influence fungus-bacterium ecological interactions. We found that production of the polyketide antibiotic 2,4-diacetylphloroglucinol (DAPG) was the primary biocontrol mechanism of Pseudomonas fluorescens strain Q2-87 against Fusarium oxysporum f. sp. radicis-lycopersici on the tomato as determined with mutational analysis. In contrast, DAPG was not important for the less-disease-suppressive strain CHA0. This was explained by differential sensitivity of the bacteria to fusaric acid, a pathogen phyto- and mycotoxin that specifically blocked DAPG biosynthesis in strain CHA0 but not in strain Q2-87. In CHA0, hydrogen cyanide, a biocide not repressed by fusaric acid, played a more important role in disease suppression.     

Role of the PDZ domains in Escherichia coli DegP protein

PDZ domains are modular protein interaction domains that are present in metazoans and bacteria. These domains possess unique structural features that allow them to interact with the C-terminal residues of their ligands. The Escherichia coli essential periplasmic protein DegP contains two PDZ domains attached to the C-terminal end of the protease domain. In this study we examined the role of each PDZ domain in the protease and chaperone activities of this protein. Specifically, DegP mutants with either one or both PDZ domains deleted were generated and tested to determine their protease and chaperone activities, as well as their abilities to sequester unfolded substrates. We found that the PDZ domains in DegP have different roles; the PDZ1 domain is essential for protease activity and is responsible for recognizing and sequestering unfolded substrates through C-terminal tags, whereas the PDZ2 domain is mostly involved in maintaining the hexameric cage of DegP. Interestingly, neither of the PDZ domains was required for the chaperone activity of DegP. In addition, we found that the loops connecting the protease domain to PDZ1 and connecting PDZ1 to PDZ2 are also essential for the protease activity of the hexameric DegP protein. New insights into the roles of the PDZ domains in the structure and function of DegP are provided. These results imply that DegP recognizes substrate molecules targeted for degradation and substrate molecules targeted for refolding in different manners and suggest that the substrate recognition mechanisms may play a role in the protease-chaperone switch, dictating whether the substrate is degraded or refolded.     

Protein/Protein Interactions (PDZ) in Proximal Tubules

The Journal of Membrane Biology -     

Role of electrostatic interactions in PDZ domain ligand recognition

PDZ domains are protein-protein interaction modules that normally recognize short C-terminal peptides. The apparent requirement for a ligand with a free terminal carboxylate group has led to the proposal that electrostatic interactions with the terminus play a significant role in recognition. However, this model has been called into question by the more recent finding that PDZ domains can recognize some internal peptide motifs that occur within a specific secondary structure context. Although these motifs bind at the same interface, they lack a terminal charge. Here we have investigated the role of electrostatics in PDZ-mediated recognition in the mouse alpha1-syntrophin PDZ domain by examining the salt dependence of binding to both terminal and internal ligands and the effects of mutating a conserved basic residue previously proposed to play a role in electrostatic recognition. These studies indicate that direct electrostatic interactions with the peptide terminus do not play a significant energetic role in binding. Additional chemical modification studies of the peptide terminus support a model in which steric and hydrogen bonding complementarity play a primary role in recognition specificity. Peptides with a free carboxy terminus, or presented within a specific structural context, can satisfy these requirements.     

Helicobacter pylori Interactions with Host Serum and Extracellular Matrix Proteins: Potential Role in the Infectious Process

Helicobacter pylori, a gram-negative spiral-shaped bacterium, specifically colonizes the stomachs of humans. Once established in this harsh ecological niche, it remains there virtually for the entire life of the host. To date, numerous virulence factors responsible for gastric colonization, survival, and tissue damage have been described for this bacterium. Nevertheless, a critical feature of H. pylori is its ability to establish a long-lasting infection. In fact, although good humoral (against many bacterial proteins) and cellular responses are observed, most infected persons are unable to eradicate the infection. A large body of evidence has shown that the interaction between H. pylori and the host is very complex. In addition to the effect of virulence factors on colonization and persistence, binding of specialized bacterial proteins, known as receptins, to certain host molecules (ligands) could explain the success of H. pylori as a chronically persisting pathogen. Some of the reported interactions are of high affinity, as revealed by their calculated dissociation constant. This review examines the binding of host proteins (serum and extracellular matrix proteins) to H. pylori and considers the significance of these interactions in the infectious process. A more thorough understanding of the kinetics of these receptin interactions could provide a new approach to preventing deeper tissue invasion in H. pylori infections and could represent an alternative to antibiotic treatment.     

Protein-Protein Interactions: Principles,Techniques, and their Potential Role in New Drug Development

Abstract

A vast network of genes is inter-linked through protein-protein interactions and is critical component of almost every biological process under physiological conditions. Any disruption of the biologically essential network leads to pathological conditions resulting into related diseases. Therefore, proper understanding of biological functions warrants a comprehensive knowledge of protein-protein interactions and the molecular mechanisms that govern such processes. The importance of protein-protein interaction process is highlighted by the fact that a number of powerful techniques/methods have been developed to understand how such interactions take place under various physiological and pathological conditions. Many of the key protein-protein interactions are known to participate in disease-associated signaling pathways, and represent novel targets for therapeutic intervention. Thus, controlling protein-protein interactions offers a rich dividend for the discovery of new drug targets. Availability of various tools to study and the knowledge of human genome have put us in a unique position to understand highly complex biological network, and the mechanisms involved therein. In this review article, we have summarized protein-protein interaction networks, techniques/methods of their binding/kinetic parameters, and the role of these interactions in the development of potential tools for drug designing.     

A Potential Role for U2AF-SAP 155 Interactions in Recruiting U2 snRNP to the Branch Site

Base pairing between U2 snRNA and the branchpoint sequence (BPS) is essential for pre-mRNA splicing. Because the metazoan BPS is short and highly degenerate, this interaction alone is insufficient for specific binding of U2 snRNP. The splicing factor U2AF binds to the pyrimidine tract at the 3′ splice site in the earliest spliceosomal complex, E, and is essential for U2 snRNP binding in the spliceosomal complex A. We show that the U2 snRNP protein SAP 155 UV cross-links to pre-mRNA on both sides of the BPS in the A complex. SAP 155’s downstream cross-linking site is immediately adjacent to the U2AF binding site, and the two proteins interact directly in protein-protein interaction assays. Using UV cross-linking, together with functional analyses of pre-mRNAs containing duplicated BPSs, we show a direct correlation between BPS selection and UV cross-linking of SAP 155 on both sides of the BPS. Together, our data are consistent with a model in which U2AF binds to the pyrimidine tract in the E complex and then interacts with SAP 155 to recruit U2 snRNP to the BPS.     

Novel Protein Interactions with Endoglin and Activin Receptor-like Kinase 1: Potential Role in Vascular Networks

Endoglin and activin receptor-like kinase 1 are specialized transforming growth factor-beta (TGF-β) superfamily receptors, primarily expressed in endothelial cells. Mutations in the corresponding ENG or ACVRL1 genes lead to hereditary hemorrhagic telangiectasia (HHT1 and HHT2 respectively). To discover proteins interacting with endoglin, ACVRL1 and TGF-β receptor type 2 and involved in TGF-β signaling, we applied LUMIER, a high-throughput mammalian interactome mapping technology. Using stringent criteria, we identified 181 novel unique and shared interactions with ACVRL1, TGF-β receptor type 2, and endoglin, defining potential novel important vascular networks. In particular, the regulatory subunit B-beta of the protein phosphatase PP2A (PPP2R2B) interacted with all three receptors. Interestingly, the PPP2R2B gene lies in an interval in linkage disequilibrium with HHT3, for which the gene remains unidentified. We show that PPP2R2B protein interacts with the ACVRL1/TGFBR2/endoglin complex and recruits PP2A to nitric oxide synthase 3 (NOS3). Endoglin overexpression in endothelial cells inhibits the association of PPP2R2B with NOS3, whereas endoglin-deficient cells show enhanced PP2A-NOS3 interaction and lower levels of endogenous NOS3 Serine 1177 phosphorylation. Our data suggest that endoglin regulates NOS3 activation status by regulating PPP2R2B access to NOS3, and that PPP2R2B might be the HHT3 gene. Furthermore, endoglin and ACVRL1 contribute to several novel networks, including TGF-β dependent and independent ones, critical for vascular function and potentially defective in HHT.Transforming growth factor-β (TGF-β)¹ superfamily ligands, including TGF-βs, activins and bone morphogenic proteins (BMPs), regulate several pathways essential for vascular development and function (1). Responses to these ligands are controlled by type I and II serine kinase receptors, coreceptors and signaling SMAD intermediates. Endothelial cells express the coreceptor, endoglin, and the specialized type I receptor, ACVRL1 (activin receptor-like kinase 1 or ALK1); both molecules are critical for regulation of angiogenesis and vasomotor function by TGF-β superfamily ligands (2, 3).Mutations in ENG and ACVRL1 genes lead to hereditary hemorrhagic telangiectasia (HHT), types 1 and 2, respectively (4). HHT affects 1 in 5000–8000 people worldwide and is characterized by arteriovenous malformations (AVMs) in multiple organs, potentially leading to severe hemorrhages and strokes (4). Haploinsufficiency is the underlying cause of HHT, indicating that reduced levels of functional endoglin or ACVRL1 (ALK1) proteins predispose to endothelial dysfunction and AVMs (5). Although the mechanisms responsible for AVMs remain unclear, the elucidation of how members of the TGF-β superfamily and their molecular networks regulate vascular integrity is vital for future treatments of HHT.We have demonstrated that endoglin interacts with endothelial nitric oxide synthase (NOS3 or eNOS) and regulates its activation (2). NOS3 is a Ca⁺² and calmodulin-regulated enzyme that produces NO● in response to humoral and mechanical stimuli via dynamic interactions with various allosteric regulators such as heat shock protein 90 (HSP90). NOS3 is also regulated by dynamic changes in its phosphorylation status. For example, effects of the vascular endothelial growth factor (VEGF) on angiogenesis, vascular permeability and vasomotor tone are mediated in part through Akt-dependent phosphorylation of NOS3 Ser1177 and by increased NOS3-HSP90 association (6). Although phosphorylation of NOS3 Ser1177 is indicative of agonist-induced activation, it is preceded by dephosphorylation at Thr495. TGF-β1 and -β3 but not -β2 responses can sensitize NOS3 for activation by inducing dephosphorylation at Thr495, and therefore contribute to NOS3 activation and NO-dependent vasorelaxation (7). Endoglin regulates TGF-β1 and -β3 but not -β2 responses, and is required for their induction of NOS3 Thr495 dephosphorylation (7, 8).In the vascular endothelium of HHT patients and in Eng and Alk1 heterozygous mice, impaired association of NOS3 with HSP90 renders the enzyme uncoupled, causing production of superoxide (●O₂⁻) instead of NO● (2, 3, 9) and leading to endothelial damage. Interestingly, TGF-β1 and -β3 do not induce phosphorylation at NOS3 Ser1177, yet NOS3 activation in response to TGF-β1 is abolished in endoglin-deficient cells, impairing vasomotor function (3). ACVRL1 (or ALK1) also interacts with NOS3, and its reduced levels in endothelial cells similarly cause NOS3-derived oxidative stress (3, 9).In view of the crucial roles of endoglin and ACVRL1 in the development and maintenance of the normal vasculature and the definite contribution of their mutated state to HHT, we used the LUMIER high-throughput technology (10) to identify novel protein interactions and molecular networks for these predominantly endothelial receptors. We included TGFBR2 to further define TGF-β protein networks potentially important for vascular function, and attempt to distinguish the TGF-β networks from those associated with BMP9/BMP10 and mediated by ACVRL1 in a complex with BMPR2 and endoglin (11, 12).One of identified proteins interacting with all three receptors was protein phosphatase 2A (PP2A), implicated in multiple pathways. PP2A is a holoenzyme with one structural subunit (PPP2R1A or PPP2R1B) associated with one catalytic subunit (PPP2CA or PPP2CB) and one of 19 regulatory B subunits, the latter conferring specificity to the enzyme by recruiting interacting proteins (13, 14). Of interest, PP2A interacts with NOS3 to regulate Ser1177 phosphorylation and NO● production (15). However, the mechanisms governing recruitment of PP2A to NOS3 and the contribution of TGF-β/BMP receptor complexes are unknown. Recently, the human PPP2R2B gene coding for PPP2R2B protein (also known as PP2A-Bβ regulatory subunit) was mapped to chromosome 5q31-q32, in an interval in linkage disequilibrium with the HHT3 locus (16, 17). We now report that PPP2R2B interacts with the ACVRL1/TGFBR2/endoglin complex and that endoglin governs NOS3 phosphorylation and activation status by hindering PP2A access to NOS3 via the PPP2R2B subunit. Loss of endoglin leads to constitutive reduction in NOS3 phosphorylation and likely to changes in several networks with consequent endothelial dysfunction.     

Computational Identification of Potential Molecular Interactions in Arabidopsis

Knowledge of the protein interaction network is useful to assist molecular mechanism studies. Several major repositories have been established to collect and organize reported protein interactions. Many interactions have been reported in several model organisms, yet a very limited number of plant interactions can thus far be found in these major databases. Computational identification of potential plant interactions, therefore, is desired to facilitate relevant research. In this work, we constructed a support vector machine model to predict potential Arabidopsis (Arabidopsis thaliana) protein interactions based on a variety of indirect evidence. In a 100-iteration bootstrap evaluation, the confidence of our predicted interactions was estimated to be 48.67%, and these interactions were expected to cover 29.02% of the entire interactome. The sensitivity of our model was validated with an independent evaluation data set consisting of newly reported interactions that did not overlap with the examples used in model training and testing. Results showed that our model successfully recognized 28.91% of the new interactions, similar to its expected sensitivity (29.02%). Applying this model to all possible Arabidopsis protein pairs resulted in 224,206 potential interactions, which is the largest and most accurate set of predicted Arabidopsis interactions at present. In order to facilitate the use of our results, we present the Predicted Arabidopsis Interactome Resource, with detailed annotations and more specific per interaction confidence measurements. This database and related documents are freely accessible at http://www.cls.zju.edu.cn/pair/.The complex cellular functions of an organism rely on physical interactions between proteins. Deciphering the protein-protein interaction network to understand higher level phenotypes and their regulations is always a major focus of both experimental biologists and computational biologists. A number of high-throughput (HTP) assays have been developed to identify in vitro protein interactions from several model organisms (Uetz et al., 2000; Giot et al., 2003; Li et al., 2004). A number of initiatives, such as IntAct (Kerrien et al., 2006), Molecular INTeraction database (Chatr-aryamontri et al., 2007), the Database of Interacting Proteins (Salwinski et al., 2004), Biomolecular Interaction Network Database (BIND; Alfarano et al., 2005), and BioGRID (Stark et al., 2006), have been established to systematically collect and organize the interaction data reported by both proteome-scale HTP experiments and traditional low-throughput studies focusing on individual proteins or pathways.Arabidopsis (Arabidopsis thaliana) has long been studied as a model organism to investigate the physiology, biochemistry, growth, development, and metabolism of a flowering plant at the molecular level. The molecular mechanism studies of various phenotypes and their regulations in Arabidopsis may be facilitated by a comprehensive reference protein interaction network, based on which working hypotheses could be invented with more guidance and confidence. However, due to technological limitations, most experimentally reported protein interactions in available databases were from other organisms. A very limited number of plant interactions could be found in these databases. Therefore, an accurate prediction of the Arabidopsis interactome would be valuable to assist relevant research.Studies on the computational identification of potential interactions started along with the advent of HTP interaction-detection technologies, which often produced a large number of false positives (Deane et al., 2002). Indirect evidence of protein interaction (e.g. protein colocalization and relevance in function) were hence introduced to boost the confidence of HTP results (Jansen et al., 2003). Further investigations demonstrated that direct inference of protein interactions from such indirect evidence alone was possible (Scott and Barton, 2007). The accuracy and effectiveness of using indirect evidence to predict interactions have also been thoroughly assessed (Qi et al., 2006; Suthram et al., 2006). These works offered precious insights into how protein interactions may be predicted accurately on a proteomic scale. In other organisms such as Homo sapiens, the prediction of an entire interactome has already been proven applicable and useful (Rhodes et al., 2005).On the other side, several efforts have been made to collect and organize a comprehensive map of Arabidopsis molecular interactions. For instances, around 20,000 interactions were inferred by homology to known interactions in other organisms (Geisler-Lee et al., 2007). Another work predicted 23,396 interactions based on multiple indirect data and curated 4,666 interactions from the literature and enzyme complexes (Cui et al., 2008). The Arabidopsis reactome database was established describing the functions of 2,195 proteins with 8,269 reactions in 318 superpathways (Tsesmetzis et al., 2008). And a general interaction database, IntAct (Kerrien et al., 2006), had allocated a special unit actively curating all plant protein interactions from literature and submitted data sets, which now contains 2,649 Arabidopsis interactions. However, in yeast, approximately 18,000 protein-protein interactions had been estimated for approximately 6,000 genes (Yu et al., 2008). Assuming the same rate of interaction, approximately 200,000 protein interactions would be expected for approximately 20,000 Arabidopsis genes. Therefore, the current collection of Arabidopsis interactions is still significantly limited. Moreover, most previous prediction works did not provide rigorous confidence measurements for their predicted interactions, which further limited their scope of applications.Recent advances in statistical learning presented a powerful algorithm, support vector machine (SVM), which may be used to predict interactions based on multiple indirect data. Although the basis of SVM had been laid in the 1960s, the idea of SVM was only officially proposed in the 1990s by Vapnik (1998, 2000). Then, research on its theoretical and application aspects thrived. It has been applied in a wide range of problems, including text categorization (de Vel et al., 2001; Kim et al., 2001), image classification and object detection (Ben-Yacoub et al., 1999; Karlsen et al., 2000), flood stage forecasting (Liong and Sivapragasam, 2002), microarray gene expression data analysis (Brown et al., 2000), drug design (Zhao et al., 2006a, 2006b), protein solvent accessibility prediction (Yuan et al., 2002), and protein fold prediction (Ding and Dubchak, 2001; Hua and Sun, 2001). Many studies have demonstrated that SVM was consistently superior to other supervised learning methods (Brown et al., 2000; Burbidge et al., 2001; Cai et al., 2003).In this work, with careful preparation of example data and selection of indirect evidence, we constructed an SVM model to predict potential Arabidopsis interactions. False positives were tightly controlled. With the high-confidence model, we identified altogether 224,206 potential interactions, which were expected to be 48.67% accurate and to cover 29.02% of the entire Arabidopsis interactome. More specific confidence measurements were also assigned on a per interaction basis. To facilitate the use of our results, we present the Predicted Arabidopsis Interactome Resource (PAIR; http://www.cls.zju.edu.cn/pair/), featuring detailed annotations and a friendly user interface.     

Sequential Delivery of Synaptic GluA1- and GluA4-containing AMPA Receptors (AMPARs) by SAP97 Anchored Protein Complexes in Classical Conditioning

Multiple signaling pathways are involved in AMPAR trafficking to synapses during synaptic plasticity and learning. The mechanisms for how these pathways are coordinated in parallel but maintain their functional specificity involves subcellular compartmentalization of kinase function by scaffolding proteins, but how this is accomplished is not well understood. Here, we focused on characterizing the molecular machinery that functions in the sequential synaptic delivery of GluA1- and GluA4-containing AMPARs using an in vitro model of eyeblink classical conditioning. We show that conditioning induces the interaction of selective protein complexes with the key structural protein SAP97, which tightly regulates the synaptic delivery of GluA1 and GluA4 AMPAR subunits. The results demonstrate that in the early stages of conditioning the initial activation of PKA stimulates the formation of a SAP97-AKAP/PKA-GluA1 protein complex leading to synaptic delivery of GluA1-containing AMPARs through a SAP97-PSD95 interaction. This is followed shortly thereafter by generation of a SAP97-KSR1/PKC-GluA4 complex for GluA4 AMPAR subunit delivery again through a SAP97-PSD95 interaction. These data suggest that SAP97 forms the molecular backbone of a protein scaffold critical for delivery of AMPARs to the PSD during conditioning. Together, the findings reveal a cooperative interaction of multiple scaffolding proteins for appropriately timed delivery of subunit-specific AMPARs to synapses and support a sequential two-stage model of AMPAR synaptic delivery during classical conditioning.     

In Vitro Interactions of Extracellular Histones with LDL Suggest a Potential Pro-Atherogenic Role

Background

Nuclear histones have previously been shown to aggregate LDL in vitro, suggestive of a possible pro-atherogenic role. Recent studies indicate that histones are released during acute inflammation, and therefore might interact with circulating lipoproteins in vivo. In view of the associative link between inflammation and cardiovascular disease, the behaviour of histones was investigated using in vitro models of LDL retention and foam cell formation.

Methodology/Principal Findings

Heparin agarose beads were used as a model of a matrix rich in sulphated glycosaminoglycans, to which histones bind strongly. Histone-modified beads were observed to pull down more LDL from solution than untreated beads, indicating that histones can function as bridging molecules, enhancing LDL retention. Furthermore, addition of heparin inhibited histone-induced aggregation of LDL. To model foam cell formation, murine RAW 264.7 macrophages were incubated for 24 h in the presence of LDL, histones, LDL plus histones or vehicle control. Cells incubated with LDL in the presence of histones accumulated significantly more intracellular lipid than with LDL or histone alone.

Conclusions/Significance

These results are consistent with a potential pro-atherogenic role for extracellular histones, which should be investigated further.     

Investigating the Role of Gene-Gene Interactions in TB Susceptibility

Tuberculosis (TB) is the second leading cause of mortality from infectious disease worldwide. One of the factors involved in developing disease is the genetics of the host, yet the field of TB susceptibility genetics has not yielded the answers that were expected. A commonly posited explanation for the missing heritability of complex disease is gene-gene interactions, also referred to as epistasis. In this study we investigate the role of gene-gene interactions in genetic susceptibility to TB using a cohort recruited from a high TB incidence community from Cape Town, South Africa. Our discovery data set incorporates genotypes from a large a number of candidate gene studies as well as genome-wide data. After limiting our search space to pairs of putative TB susceptibility genes, as well as pairs of genes that have been curated in online databases as potential interactors, we use statistical modelling to identify pairs of interacting SNPs. We attempt to validate the top models identified in our discovery data set using an independent genome-wide TB case-control data set from The Gambia. A number of models were successfully validated, indicating that interplay between the NRG1 - NRG3, GRIK1 - GRIK3 and IL23R - ATG4C gene pairs may modify susceptibility to TB. Gene pairs involved in the NF-κB pathway were also identified in the discovery data set (SFTPD - NOD2, ISG15 - TLR8 and NLRC5 - IL12RB1), but could not be tested in the Gambian study group due to lack of overlapping data.     

Role of Site-Specific N-Glycans Expressed on GluA2 in the Regulation of Cell Surface Expression of AMPA-Type Glutamate Receptors

The AMPA-type glutamate receptor (AMPAR), which is a tetrameric complex composed of four subunits (GluA1-4) with several combinations, mediates the majority of rapid excitatory synaptic transmissions in the nervous system. Cell surface expression levels of AMPAR modulate synaptic plasticity, which is considered one of the molecular bases for learning and memory formation. To date, a unique trisaccharide (HSO₃-3GlcAβ1-3Galβ1-4GlcNAc), human natural killer-1 (HNK-1) carbohydrate, was found expressed specifically on N-linked glycans of GluA2 and regulated the cell surface expression of AMPAR and the spine maturation process. However, evidence that the HNK-1 epitope on N-glycans of GluA2 directly affects these phenomena is lacking. Moreover, it is thought that other N-glycans on GluA2 also have potential roles in the regulation of AMPAR functions. In the present study, using a series of mutants lacking potential N-glycosylation sites (N256, N370, N406, and N413) within GluA2, we demonstrated that the mutant lacking the N-glycan at N370 strongly suppressed the intracellular trafficking of GluA2 from the endoplasmic reticulum (ER) in HEK293 cells. Cell surface expression of GluA1, which is a major subunit of AMPAR in neurons, was also suppressed by co-expression of the GluA2 N370S mutant. The N370S mutant and wild-type GluA2 were co-immunoprecipitated with GluA1, suggesting that N370S was properly associated with GluA1. Moreover, we found that N413 was the main potential site of the HNK-1 epitope that promoted the interaction of GluA2 with N-cadherin, resulting in enhanced cell surface expression of GluA2. The HNK-1 epitope on N-glycan at the N413 of GluA2 was also involved in the cell surface expression of GluA1. Thus, our data suggested that site-specific N-glycans on GluA2 regulate the intracellular trafficking and cell surface expression of AMPAR.     

Electrical Interactions via the Extracellular Potential Near Cell Bodies

Ephaptic interactions between a neuron and axons or dendrites passing by its cell body can be, in principle, more significant than ephaptic interactions among axons in a fiber tract. Extracellular action potentials outside axons are small in amplitude and spatially spread out, while they are larger in amplitude and much more spatially confined near cell bodies. We estimated the extracellular potentials associated with an action potential in a cortical pyramidal cell using standard one-dimensional cable theory and volume conductor theory. Their spatial and temporal pattern reveal much about the location and timing of currents in the cell, especially in combination with a known morphology, and simple experiments could resolve questions about spike initiation. From the extracellular potential we compute the ephaptically induced polarization in a nearby passive cable. The magnitude of this induced voltage can be several mV, does not spread electrotonically, and depends only weakly on the passive properties of the cable. We discuss their possible functional relevance.     

Deciphering the Role of Ethylene in Plant–Herbivore Interactions

Most plants emit ethylene in response to herbivory by insects from many different feeding guilds. The elicitors of these ethylene emissions are thought to be microorganisms or oral secretion-specific compounds that are transferred when the attacking insect feeds. To find the receptors for these elicitors and describe the signaling cascades that are subsequently activated will be the challenge of future research. Past experiments on the function of herbivore-induced ethylene, which were biased toward the use of chemical treatments to manipulate ethylene, identified seven ethylene-dependent defense responses. In contrast, a genetic toolbox that consists of several mutants has rarely been used and to date, mutants have helped to identify only one additional ethylene-dependent defense response. Ethylene-dependent responses include the emission of specific volatile organic compounds as indirect defense, the accumulation of phenolic compounds, and proteinase inhibitor activity. Besides being ethylene regulated, these defenses depend strongly on the wound-hormone jasmonic acid (JA). That ethylene requires the concomitant induction of JA, or other signals, appears to be decisive. Rather than being the principal elicitor of defense responses, ethylene modulates the sensitivity to a second signal and its downstream responses. Given this modulator role, and the artifacts associated with the use of chemical treatments to manipulate ethylene production and perception, future advances in the study of ethylene’s function in plant–herbivore interactions will likely come from the use of signaling mutants or transgenic plants. It will be exciting to see if adaptive phenotypic plasticity is largely an ethylene-mediated response.     

KLF4在内毒素血症小鼠中的表达模式及其意义

目的探讨Kruppel样因子4（Kruppel-like factor 4,KLF4）在内毒素血症小鼠中的表达模式及意义。方法运用实时荧光PCR技术和Western blot技术,分别从mRNA水平和蛋白水平探讨内毒素血症小鼠肝脏和肺脏中KLF4的表达;运用生物信息学技术,对启动子区含有KLF4的结合位点的炎症介质基因进行了预测;运用RT-PCR技术,从mRNA水平探讨内毒素血症小鼠肝脏和肺脏中IL1β的表达模式。结果内毒素血症小鼠肝脏和肺脏中KLF4 mRNA的表达下凋,KLF4蛋白的表达先下凋后升高;IL-18、IL-15、IL-12、IL-18、IL-10等炎症介质基因的启动子区均含有KLF4的结合元件,这些炎症基因的表达可能直接受到KLF4的调控;内毒素血症小鼠肝脏和肺脏中IL-IB的表达模式与KLF4的表达模式呈相反趋势。结论内毒素血症小鼠肝脏和肺脏中KLF4表达下调,KLF4在炎症介质基因表达调控中可能具有重要作用。