Proteomic characterization of the multiple forms of the PLAs from the venom of the social wasp Polybia paulista

The phospholipases A(1) (PLA(1)s) from the venom of the social wasp Polybia paulista occur as a mixture of different molecular forms. To characterize the molecular origin of these structural differences, an experimental strategy was planned combining the isolation of the pool of PLAs from the wasp venom with proteomic approaches by using 2-D, MALDI-TOF-TOF MS and classical protocols of protein chemistry, which included N- and C-terminal sequencing. The existence of an intact form of PLA(1) and seven truncated forms was identified, apparently originating from controlled proteolysis of the intact protein; in addition to this, four of these truncated forms also presented carbohydrates attached to their molecules. Some of these forms are immunoreactive to specific-IgE, while others are not. These observations permit to raise the hypothesis that naturally occurring proteolysis of PLA(1), combined with protein glycosylation may create a series of different molecular forms of these proteins, with different levels of allergenicity. Two forms of PLA(2)s, apparently related to each other, were also identified; however, it was not possible to determine the molecular origin of the differences between both forms, except that one of them was glycosylated. None of these forms were immunoreactive to human specific IgE.

Profiling the Proteome of the Venom from the Social Wasp Polybia paulista: A Clue to Understand the Envenoming Mechanism

The study reported here is a classical bottom-up proteomic approach where proteins from wasp venom were extracted and separated by 2-DE; the individual protein spots were proteolytically digested and subsequently identified by using tandem mass spectrometry and database query with the protein search engine MASCOT. Eighty-four venom proteins belonging to 12 different molecular functions were identified. These proteins were classified into three groups; the first is constituted of typical venom proteins: antigens-5, hyaluronidases, phospholipases, heat shock proteins, metalloproteinases, metalloproteinase-desintegrin like proteins, serine proteinases, proteinase inhibitors, vascular endothelial growth factor-related protein, arginine kinases, Sol i-II and -II like proteins, alpha-glucosidase, and superoxide dismutases. The second contained proteins structurally related to the muscles that involves the venom reservoir. The third group, associated with the housekeeping of cells from venom glands, was composed of enzymes, membrane proteins of different types, and transcriptional factors. The composition of P. paulista venom permits us to hypothesize about a general envenoming mechanism based on five actions: (i) diffusion of venom through the tissues and to the blood, (ii) tissue, (iii) hemolysis, (iv) inflammation, and (v) allergy-played by antigen-5, PLA1, hyaluronidase, HSP 60, HSP 90, and arginine kinases.

A polydnavirus-encoded protein of an endoparasitoid wasp is an immune suppressor

The molecular mechanism by which polydnaviruses of endoparasitoid wasps disrupt cell-mediated encapsulation reactions of host insects is largely unknown. Here we show that a polydnavirus-encoded protein, produced from baculovirus and plasmid expression vectors, prevents cell surface exposure of lectin-binding sites and microparticle formation during immune stimulation of haemocytes. The inactivation of immune-related cellular processes by this protein was analysed using a specific lectin and annexin V and shown to be virtually identical to polydnavirus-mediated effects on haemocytes. Cytochalasin D application has similar effects on haemocytes, suggesting that the immune suppression by the polydnavirus protein is caused by the destabilization of actin filaments. Since the exposure of cell surface glycoproteins and the formation of microparticles are part of an immune response to foreign objects or microorganisms and a prerequisite for cell-mediated encapsulation of microorganisms and parasites, the virus-encoded protein may become an important tool for the inactivation of cellular immune reactions in insects and an essential component in understanding immune suppression in parasitized host insects.

Multiple endocrine neoplasia type 1 in Brazil: MEN1 founding mutation, clinical features, and bone mineral density profile

Objective: Only few large families with multiple endocrine neoplasia type 1 (MEN1) have been documented. Here, we aimed to investigate the clinical features of a seven-generation Brazilian pedigree. which included 715 at-risk family members. Design: Genealogical and geographic analysis was used to identify the MEN1 pedigree. Clinical and genetic approach was applied to characterize the phenotypic and genotypic features of the family members. Results: Our genetic data indicated that a founding mutation in the MEN1 gene has occurred in this extended Brazilian family. Fifty family members were diagnosed with MEN1. Very high frequencies of functioning and non-functioning MEN1-related tumors were documented and the prevalence of prolactinoma (29.6%) was similar to that previously described in prolactinoma-variant Burin (32%). In addition, bone mineral density analysis revealed severe osteoporosis (T,-2.87 +/- 0.32) of compact bone (distal radius) in hyperparathyroidism (HPT)/MEN1 patients. while marked bone mineral loss in the lumbar spine (T,-1.95 +/- 0.39). with most cancellous bone, and femoral neck (mixed composition: T,-1.48 +/- 0.27) were also present. Conclusions: In this study, we described clinically and genetically the fifth largest MEN1 family in the literature. Our data confirm previous findings suggesting that prevalence of MEN1-related tumors in large families may differ from reports combining cumulative data of small families. Furthermore. we were able to evaluate the bone status in HPT/MEN1 cases, a subject that has been incompletely approached in the literature. We discussed the bone loss pattern found in our MEN1 patients comparing with that of patients with sporadic primary HPT.

Protonectin (1-6): A novel chemotactic peptide from the venom of the social wasp Agelaia pallipes pallipes

Peptides constitute the largest group of Hymenoptera venom toxins; some of them interact with GPCR, being involved with the activation of different types of leukocytes, smooth muscle contraction and neurotoxicity. Most of these toxins vary from dodecapeptides to tetradecapeptides, amidated at their C-teminal amino acid residue. The venoms of social wasps can also contains some tetra-, penta-, hexa- and hepta-peptides, but just a few of them have been structurally and functionally characterized up to now. Protonectin (ILG-TILGLLKGL-NH(2)) is a polyfunctional peptide, presenting mast cell degranulation, release of lactate dehydrogenase (LDH) from mast cells, antibiosis against Gram-positive and Gram-negative bacteria and chemotaxis for polymorphonucleated leukocytes (PMNL), while Protonectin (1-6) (ILGTIL-NH(2)) only presents chemotaxis for PMNL However, the mixture of Protonectin (1-6) with Protonectin in the molar ratio of 1:1 seems to potentiate the biological activities dependent of the membrane perturbation caused by Protonectin, as observed in the increasing of the activities of mast cell degranulation, LDH releasing from mast cells, and antibiosis. Despite both peptides are able to induce PMNL chemotaxis, the mixture of them presents a reduced activity in comparison to the individual peptides. Apparently, when mixed both peptides seems to form a supra-molecular structure, which interact with the receptors responsible for PMNL chemotaxis, disturbing their individual docking with these receptors. In addition to this, a comparison of the sequences of both peptides suggests that the sequence ILGTIL is conserved, suggesting that it must constitute a linear motif for the structural recognition by the specific receptor which induces leukocytes migration. (C) 2010 Elsevier Ltd. All rights reserved.

Anticoagulant and fibrinogenolytic properties of the venom of Polybia occidentalis social wasp

Previous studies have shown that venoms of social wasps and bees exhibit strong anticoagulant activity. The present study describes the anticoagulant and fibrinogen-degrading pharmacological properties of the venom of Polybia occidentalis social wasp. The results demonstrated that this venom presented anticoagulant effect, inhibiting the coagulation at different steps of the clotting pathway (intrinsic, extrinsic and common pathway). The venom inhibited platelet aggregation and degraded plasma fibrinogen, possibly containing metal-dependent metalloproteases that specifically cleave the B beta-chain of fibrinogen. In conclusion, fibrinogenolytic and anticoagulant properties of this wasp venom find a potential application in drug development for the treatment of thrombotic disorders. For that, further studies should be carried out in order to identify and isolate the active compounds responsible for these effects. Blood Coagul Fibrinolysis 21: 653-659 (c) 2010 Wolters Kluwer Health | Lippincott Williams & Wilkins.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Cooperative scheduling system with emergent swarm based behavior

This paper presents a Swarm based Cooperation Mechanism for scheduling optimization. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to support decision making in agile manufacturing environments. Agents coordinate their actions automatically without human supervision considering a common objective – global scheduling solution taking advantages from collective behavior of species through implicit and explicit cooperation. The performance of the cooperation mechanism will be evaluated consider implicit cooperation at first stage through ACS, PSO and ABC algorithms and explicit through cooperation mechanism application.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

A long-term swarm intelligence hedging tool applied to electricity markets

This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Swarm intelligence for scheduling: a review

Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduces some of the theoretical foundations, the biological motivation and fundamental aspects of swarm intelligence based optimization techniques such Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bees Colony (ABC) algorithms for scheduling optimization.