Characterization of immunoglobulin G antibodies to Plasmodium falciparum sporozoite surface antigen MB2 in malaria exposed individuals

<p>Abstract</p> <p>Background</p> <p>MB2 protein is a sporozoite surface antigen on the human malaria parasite <it>Plasmodium falciparum</it>. MB2 was identified by screening a <it>P. falciparum </it>sporozoite cDNA expression library using immune sera from a protected donor immunized via the bites of <it>P. falciparum</it>-infected irradiated mosquitoes. It is not known whether natural exposure to <it>P. falciparum </it>also induces the anti-MB2 response and if this response differs from that in protected individuals immunized via the bites of <it>P. falciparum </it>infected irradiated mosquitoes. The anti-MB2 antibody response may be part of a robust protective response against the sporozoite.</p> <p>Methods</p> <p>Fragments of polypeptide regions of MB2 were constructed as recombinant fusions sandwiched between glutathione S-transferase and a hexa histidine tag for bacterial expression. The hexa histidine tag affinity purified proteins were used to immunize rabbits and the polyclonal sera evaluated in an <it>in vitro </it>inhibition of sporozoite invasion assay. The proteins were also used in immunoblots with sera from a limited number of donors immunized via the bites of <it>P. falciparum </it>infected irradiated mosquitoes and plasma and serum obtained from naturally exposed individuals in Kenya.</p> <p>Results</p> <p>Rabbit polyclonal antibodies targeting the non-repeat region of the basic domain of MB2 inhibited sporozoites entry into HepG2-A16 cells <it>in vitro</it>. Analysis of serum from five human volunteers that were immunized via the bites of <it>P. falciparum </it>infected irradiated mosquitoes that developed immunity and were completely protected against subsequent challenge with non-irradiated parasite also had detectable levels of antibody against MB2 basic domain. In contrast, in three volunteers not protected, anti-MB2 antibodies were below the level of detection. Sera from protected volunteers preferentially recognized a non-repeat region of the basic domain of MB2, whereas plasma from naturally-infected individuals also had antibodies that recognize regions of MB2 that contain a repeat motif in immunoblots. Sequence analysis of eleven field isolates and four laboratory strains showed that these antigenic regions of the basic domain of the <it>MB2 </it>gene are highly conserved in parasites obtained from different parts of the world. Moreover, anti-MB2 antibodies also were detected in the plasma of 83% of the individuals living in a malaria endemic area of Kenya (n = 41).</p> <p>Conclusion</p> <p>A preliminary analysis of the human humoral response against MB2 indicates that it may be an additional highly conserved target for immune intervention at the pre-erythrocytic stage of <it>P. falciparum </it>life cycle.</p

Why Functional Pre-Erythrocytic and Bloodstage Malaria Vaccines Fail: A Meta-Analysis of Fully Protective Immunizations and Novel Immunological Model

Background: Clinically protective malaria vaccines consistently fail to protect adults and children in endemic settings, and at best only partially protect infants. Methodology/Principal Findings: We identify and evaluate 1916 immunization studies between 1965-February 2010, and exclude partially or nonprotective results to find 177 completely protective immunization experiments. Detailed reexamination reveals an unexpectedly mundane basis for selective vaccine failure: live malaria parasites in the skin inhibit vaccine function. We next show published molecular and cellular data support a testable, novel model where parasite-host interactions in the skin induce malaria-specific regulatory T cells, and subvert early antigen-specific immunity to parasite-specific immunotolerance. This ensures infection and tolerance to reinfection. Exposure to Plasmodium-infected mosquito bites therefore systematically triggers immunosuppression of endemic vaccine-elicited responses. The extensive vaccine trial data solidly substantiate this model experimentally. Conclusions/Significance: We conclude skinstage-initiated immunosuppression, unassociated with bloodstage parasites, systematically blocks vaccine function in the field. Our model exposes novel molecular and procedural strategies to significantly and quickly increase protective efficacy in both pipeline and currently ineffective malaria vaccines, and forces fundamental reassessment of central precepts determining vaccine development. This has major implications fo

Impact of species and antibiotic therapy of enterococcal peritonitis on 30-day mortality in critical care - An analysis of the OUTCOMEREA database

Introduction: Enterococcus species are associated with an increased morbidity in intraabdominal infections (IAI). However, their impact on mortality remains uncertain. Moreover, the influence on outcome of the appropriate or inappropriate status of initial antimicrobial therapy (IAT) is subjected to debate, except in septic shock. The aim of our study was to evaluate whether an IAT that did not cover Enterococcus spp. was associated with 30-day mortality in ICU patients presenting with IAI growing with Enterococcus spp. Material and methods: Retrospective analysis of French database OutcomeRea from 1997 to 2016. We included all patients with IAI with a peritoneal sample growing with Enterococcus. Primary endpoint was 30-day mortality. Results: Of the 1017 patients with IAI, 76 (8%) patients were included. Thirty-day mortality in patients with inadequate IAT against Enterococcus was higher (7/18 (39%) vs 10/58 (17%), p = 0.05); however, the incidence of postoperative complications was similar. Presence of Enterococcus spp. other than E. faecalis alone was associated with a significantly higher mortality, even greater when IAT was inadequate. Main risk factors for having an Enterococcus other than E. faecalis alone were as follows: SAPS score on day 0, ICU-acquired IAI, and antimicrobial therapy within 3 months prior to IAI especially with third-generation cephalosporins. Univariate analysis found a higher hazard ratio of death with an Enterococcus other than E. faecalis alone that had an inadequate IAT (HR = 4.4 [1.3-15.3], p = 0.019) versus an adequate IAT (HR = 3.1 [1.0-10.0], p = 0.053). However, after adjusting for confounders (i.e., SAPS II and septic shock at IAI diagnosis, ICU-acquired peritonitis, and adequacy of IAT for other germs), the impact of the adequacy of IAT was no longer significant in multivariate analysis. Septic shock at diagnosis and ICU-acquired IAI were prognostic factors. Conclusion: An IAT which does not cover Enterococcus is associated with an increased 30-day mortality in ICU patients presenting with an IAI growing with Enterococcus, especially when it is not an E. faecalis alone. It seems reasonable to use an IAT active against Enterococcus in severe postoperative ICU-acquired IAI, especially when a third-generation cephalosporin has been used within 3 months. © 2019 The Author(s)

PR-VNE: Preventive Reliable Virtual Network Embedding Algorithm in Cloud's Network

International audienceIn this paper, we propose a new preventive reliable virtual network embedding algorithm denoted by PR-VNE within the Cloud's backbone network. The proposal does not allocate any backup resources and takes into consideration the ageing of the hardware backbone network. The main objective is to maximise the number of hosted virtual networks while minimising the rate of crashed virtual networks impacted by physical (i.e., routers or links) failures. The problem is a multi-objective non-linear optimisation and classified as NP-hard. To overcome its complexity, PR-VNE is based on the artificial bee colony metaheuristic. Moreover, it makes use of a multi commodity flow algorithm in order to maximise the load balancing of bandwidth usage within the physical network. Based on extensive simulations, the performance obtained is better than the related strategies found in literature in terms of reject and blackout rates of virtual networks

A Batch Approach for Survivable Virtual Network Embedding based on Monte-Carlo Tree Search

International audience© 2015 IEEE. In this paper, we address the survivable batch-embedding virtual network problem within Cloud's backbone. In fact, the batch mapping of virtual networks will enhance the cumulative Cloud provider's revenue thanks to the global view of the incoming requests during a predefined time slot. Hence, the differentiation between requests can be performed and the arrival order of requests is ignored. The embedding of one virtual network is NP-hard. Adding the batch processing of the requests will further increase the complexity of the problem. In order to skirt the exponential complexity, we formulate the problem as building and researching problems within a decision tree. To resolve it, we propose a novel reliable batch-embedding virtual network strategy denoted by BR-VNE. It is based on Monte-Carlo Tree Search optimization method in which the upper confidence bounds can be reached in polynomial time. Based on extensive simulations, the results obtained show that BR-VNE outperforms the related work in terms of i) acceptance rate of virtual network requests, ii) Cloud provider's revenue and iii) rate of requests impacted by physical failures within the Cloud's backbone

Fault‐Tolerant Prediction‐Based Scheme for Target Tracking in Wireless Sensor Networks

International audienceFault-tolerance is an important function in target tracking application using wireless sensor networks. We propose in this paper, an efficient fault-tolerant approach for target tracking that prevents the loss of the target. Instead of using a single prediction mechanism, our approach uses a multi-level incremental prediction technique that adjusts the prediction precision of the target movement. The responsible node of target detection uses multiple historical information pieces to calculate multi-level predictions which have different precision levels according to the number of information pieces used. Thanks to our parametric prediction model, our approach increases the prediction success rate and decreases the target loss frequency compared to basic approaches that use simple prediction models