Improved Innate and Adaptive Immunostimulation by Genetically Modified HIV-1 Protein Expressing NYVAC Vectors.

Attenuated poxviruses are safe and capable of expressing foreign antigens. Poxviruses are applied in veterinary vaccination and explored as candidate vaccines for humans. However, poxviruses express multiple genes encoding proteins that interfere with components of the innate and adaptive immune response. This manuscript describes two strategies aimed to improve the immunogenicity of the highly attenuated, host-range restricted poxvirus NYVAC: deletion of the viral gene encoding type-I interferon-binding protein and development of attenuated replication-competent NYVAC. We evaluated these newly generated NYVAC mutants, encoding HIV-1 env, gag, pol and nef, for their ability to stimulate HIV-specific CD8 T-cell responses in vitro from blood mononuclear cells of HIV-infected subjects. The new vectors were evaluated and compared to the parental NYVAC vector in dendritic cells (DCs), RNA expression arrays, HIV gag expression and cross-presentation assays in vitro. Deletion of type-I interferon-binding protein enhanced expression of interferon and interferon-induced genes in DCs, and increased maturation of infected DCs. Restoration of replication competence induced activation of pathways involving antigen processing and presentation. Also, replication-competent NYVAC showed increased Gag expression in infected cells, permitting enhanced cross-presentation to HIV-specific CD8 T cells and proliferation of HIV-specific memory CD8 T-cells in vitro. The recombinant NYVAC combining both modifications induced interferon-induced genes and genes involved in antigen processing and presentation, as well as increased Gag expression. This combined replication-competent NYVAC is a promising candidate for the next generation of HIV vaccines.

Potential evidence of parasite avoidance in an avian malarial vector

Epidemiological studies of malaria or other vector-transmitted diseases often consider vectors as passive actors in the complex life cycle of the parasites, assuming that vector populations are homogeneous and vertebrate hosts are equally susceptible to being infected during their lifetime. However, some studies based on both human and rodent malaria systems found that mosquito vectors preferentially selected infected vertebrate hosts. This subject has been scarcely investigated in avian malaria models and even less in wild animals using natural host-parasite associations. We investigated whether the malaria infection status of wild great tits, Parus major, played a role in host selection by the mosquito vector Culex pipiens. Pairs of infected and uninfected birds were tested in a dual-choice olfactometer to assess their attractiveness to the mosquitoes. Plasmodium-infected birds attracted significantly fewer mosquitoes than the uninfected ones, which suggest that avian malaria parasites alter hosts' odours involved in vector orientation. Reaction time of the mosquitoes, that is, the time taken to select a host, and activation of mosquitoes, defined as the proportion of individuals flying towards one of the hosts, were not affected by the bird's infection status. The importance of these behavioural responses for the vector is discussed in light of recent advances in related or similar model systems.

Single cell analysis reveals similar functional competence of dominant and nondominant CD8 T-cell clonotypes.

Immune protection from infectious diseases and cancer is mediated by individual T cells of different clonal origin. Their functions are tightly regulated but not yet fully characterized. Understanding the contribution of each T cell will improve the prediction of immune protection based on laboratory assessment of T-cell responses. Here we developed techniques for simultaneous molecular and functional assessment of single CD8 T cells directly ex vivo. We studied two groups of patients with melanoma after vaccination with two closely related tumor antigenic peptides. Vaccination induced T cells with strong memory and effector functions, as found in virtually all T cells of the first patient group, and fractions of T cells in the second group. Interestingly, high functionality was not restricted to dominant clonotypes. Rather, dominant and nondominant clonotypes acquired equal functional competence. In parallel, this was also found for EBV- and CMV-specific T cells. Thus, the nondominant clonotypes may contribute similarly to immunity as their dominant counterparts.

Exendin-(9-39) is an inverse agonist of the murine glucagon-like peptide-1 receptor: implications for basal intracellular cyclic adenosine 3',5'-monophosphate levels and beta-cell glucose competence.

The effect of exendin-(9-39), a described antagonist of the glucagon-like peptide-1 (GLP-1) receptor, was evaluated on the formation of cAMP- and glucose-stimulated insulin secretion (GSIS) by the conditionally immortalized murine betaTC-Tet cells. These cells have a basal intracellular cAMP level that can be increased by GLP-1 with an EC50 of approximately 1 nM and can be decreased dose dependently by exendin-(9-39). This latter effect was receptor dependent, as a beta-cell line not expressing the GLP-1 receptor was not affected by exendin-(9-39). It was also not due to the endogenous production of GLP-1, because this effect was observed in the absence of detectable preproglucagon messenger RNA levels and radioimmunoassayable GLP-1. Importantly, GSIS was shown to be sensitive to this basal level of cAMP, as perifusion of betaTC-Tet cells in the presence of exendin-(9-39) strongly reduced insulin secretion. This reduction of GSIS, however, was observed only with growth-arrested, not proliferating, betaTC-Tet cells; it was also seen with nontransformed mouse beta-cells perifused in similar conditions. These data therefore demonstrated that 1) exendin-(9-39) is an inverse agonist of the murine GLP-1 receptor; 2) the decreased basal cAMP levels induced by this peptide inhibit the secretory response of betaTC-Tet cells and mouse pancreatic islets to glucose; 3) as this effect was observed only with growth-arrested cells, this indicates that the mechanism by which cAMP leads to potentiation of insulin secretion is different in proliferating and growth-arrested cells; and 4) the presence of the GLP-1 receptor, even in the absence of bound peptide, is important for maintaining elevated intracellular cAMP levels and, therefore, the glucose competence of the beta-cells.

Lentiviral Vector Tropism In Degenerating Retinas

Introduction: In normal mice, lentiviral vector (LV) shows a great efficiency to infect the RPE cells, but transduces retinal neurons more efficiently during development. Here, we investigated the tropism of LV in the degenerating retina of mice, knowing that the retina structure changes during degeneration. We postulated that the viral transduction would be increased by the alteration of the interphotoreceptor matrix (IPM). We tested two different LV-pseudotypes using the VSVG and the Mokola envelopes. Methods: Subretinal injections were performed in wild-type (C57/Bl6) and rhodopsin knockout (Rho-/-) mice. We injected LV-VSVG-EFS-GFPII into 3.3-4.9 month old mice and LV-VSVG-Rho-GFP into 1-1.4 month old mice to target the photoreceptors (PR). LV-MOK-CMV-GFP was injected into 2.4-3.3 months old mice. We sacrificed the animals one week post injection, used immunohistochemistry to identify the transduced cells, and investigated the OLM integrity. Results: Using LV-VSVG-EFS-GFPII into 3.3-4.9 months mice, we observed significant retinal and RPE transduction in Rho-/- mice. However, the retinas showed transduction mainly at the injection's site. We mostly observed GFP+ cells having a Müller cell morphology. Using LV-MOK-CMV-GFP into 2.4-3.3 months mice, we evidenced the same pattern of viral infection, but with more Müller cells targeted by the virus. Using LV-VSVG-Rho-GFP into 1-1.4 month old mice, we don't note any difference between Rho-/- and wild-type mice for transduced cells. The IPM stained with ZO1 appears irregular into the 4.9 months old Rho-/- mice; for the youngest mice (Rho-/- and C57/Bl6), there is no modification of the IPM. Conclusion: The degeneration improves retinal cells transduction due to the alteration of the IPM in old Rho-/- mice. Müller cells seem (by morphological evidences) to be the principal cells expressing the transgene. The LV with Mokola envelope can transduce Müller cells in a degenerating retina with an intact IPM. In 1 month old mice, the degeneration doesn't enhance the transduction in rod PR probably because the IPM is not yet altered. The possibility to target photoreceptors at a later stage of the degeneration is under investigation.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Statistical discrimination of steroid profiles in doping control with support vector machines.

Due to their performance enhancing properties, use of anabolic steroids (e.g. testosterone, nandrolone, etc.) is banned in elite sports. Therefore, doping control laboratories accredited by the World Anti-Doping Agency (WADA) screen among others for these prohibited substances in urine. It is particularly challenging to detect misuse with naturally occurring anabolic steroids such as testosterone (T), which is a popular ergogenic agent in sports and society. To screen for misuse with these compounds, drug testing laboratories monitor the urinary concentrations of endogenous steroid metabolites and their ratios, which constitute the steroid profile and compare them with reference ranges to detect unnaturally high values. However, the interpretation of the steroid profile is difficult due to large inter-individual variances, various confounding factors and different endogenous steroids marketed that influence the steroid profile in various ways. A support vector machine (SVM) algorithm was developed to statistically evaluate urinary steroid profiles composed of an extended range of steroid profile metabolites. This model makes the interpretation of the analytical data in the quest for deviating steroid profiles feasible and shows its versatility towards different kinds of misused endogenous steroids. The SVM model outperforms the current biomarkers with respect to detection sensitivity and accuracy, particularly when it is coupled to individual data as stored in the Athlete Biological Passport.

Impact of Pre-Existing Immunity on the Selection of Rare Adenovirus Vector Candidates: Implications for HIV Vaccine Development

Background: Adenovirus serotype 5 (Ad5) phase IIb vaccine trial (STEP) was prematurely stopped due to a lack of efficacy and two-fold higher incidence of HIV infection among Ad5 seropositive vaccine recipients. We have recently demonstrated that Ad5 immune complexes (Ad5 ICs)-mediated activation of the dendritic cell (DC)-T cell axis was associated with the enhancement of HIV infection in vitro. Although the direct role of Ad5 neutralizing antibodies (NAbs) in the increase of HIV susceptibility during the STEP trial is still under debate, vector-specific NAbs remain a major hurdle for vector-based gene therapies or vaccine strategies. To surmount this obstacle, vectors based on ''rare'' Ad serotypes including Ad6, Ad26, Ad36 and Ad41 were engineered.Methods: The present study aimed to determine whether Ad ICmediated DC maturation could be circumvented using these Advector candidates.Results: We found that all Ad vectors tested forming ICs with plasma containing serotype-specific NAbs had the capacity to 1) mature human DCs as monitored by the up-regulation of costimulatory molecules and the release of pro-inflammatory cytokines (TNF-a), via the stabilization of Ad capsid at endosomal but not lysosomal pH rendering Ad DNA/TLR9 interactions possible and 2) potentiate Ad-specific CD4 and CD8 T cell responses.Conclusion: In conclusion, despite a conserved DC maturation potential, the low prevalence of serotype-specific NAbs renders rare Ad vectors attractive for vaccine strategies.

A comparison between joint coordinate system and attitude vector for multi-segment foot kinematics.

The joint angles of multi-segment foot models have been primarily described using two mathematical methods: the joint coordinate system and the attitude vector. This study aimed to determine whether the angles obtained through these two descriptors are comparable, and whether these descriptors have similar sensitivity to experimental errors. Six subjects walked eight times on an instrumented walkway while the joint angles among shank, hindfoot, medial forefoot, and lateral forefoot were measured. The angles obtained using both descriptors and their sensitivity to experimental errors were compared. There was no overall significant difference between the ranges of motion obtained using both descriptors. However, median differences of more than 6° were noticed for the medial-lateral forefoot joint. For all joints and rotation planes, both descriptors provided highly similar angle patterns (median correlation coefficient: R>0.90), except for the medial-lateral forefoot angle in the transverse plane (median R=0.77). The joint coordinate system was significantly more sensitive to anatomical landmarks misplacement errors. However, the absolute differences of sensitivity were small relative to the joints ranges of motion. In conclusion, the angles obtained using these two descriptors were not identical, but were similar for at least the shank-hindfoot and hindfoot-medial forefoot joints. Therefore, the angle comparison across descriptors is possible for these two joints. Comparison should be done more carefully for the medial-lateral forefoot joint. Moreover, despite different sensitivities to experimental errors, the effects of the experimental errors on the angles were small for both descriptors suggesting that both descriptors can be considered for multi-segment foot models.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.