Development of disabled, replication-defective gene transfer vectors from the Jembrana disease virus, a new infectious agent of cattle

Jembrana disease virus (JDV) is a newly isolated and characterised bovine lentivirus. It causes an acute disease in Ball cattle (Bos javanicus). which can be readily transmitted to susceptible cattle with 17% mortality. There is as yet no treatment or preventive vaccine. We have developed a gene transfer vector system based on JDV that has three components. The first of the components is a bicistronic transfer vector plasmid that was constructed to contain cis-sequences from the JDV genome, including 5 '- and 3 ' -long terminal repeats (LTRs), 0.4 kb of truncated gag and 1.1 kb of 3 ' -env, a multiple cloning site to accommodate the gene(s) of interest for transfer, and an internal ribosome entry site plus the neomycin phosphotransferase (Neo) gene cassette for antibiotic selection. The second element is a packaging plasmid that contains trans-sequences. including gag, pol. vif, tar and rev: but without the env and packaging signals. The third is a plasmid encoding the G glycoprotein of vesicular stomatitis virus (VSV-G) to supply the vector an envelope for pseudotyping. Cotransfection of 293T cells with these three plasmid components produced VSV-G pseudotyped. disabled, replication defective, bicistronic JDV vectors encoding the green fluorescent protein (EGFP) and the Neo resistance selection maker simultaneously with a titre range of (0.4-1.2) x 10(6) CFU/ml. Transduction of several replicating primary and transformed cells from cattle, primate and human sources and importantly growth-arrested cells with the JDV vectors showed high efficiency of EGFP gene transfer at 35-75%, which was stable and the expression of EGFP was long term. Furthermore, these JDV vectors were designed to suit the inclusion and expression of genes corresponding to JDV specific proteins, such as gag or env, for the development of vaccines for Jembrana disease. This strategy should also be applicable to other bovine diseases as wall. The design and construction of the JDV vector system should facilitate the study of the lentivirology and pathogenesis of the diseases associated with JDV or other bovine virus infections. To our knowledge, this is the first such vector system developed from a cattle virus. (C) 2001 Elsevier Science B.V. All rights reserved.

Extensive multiple test centre evaluation of the VecTest malaria antigen panel assay

To determine which species and populations of Anopheles transmit malaria in any given situation, immunological assays for malaria sporozoite antigen can replace traditional microscopical examination of freshly dissected Anopheles. We developed a wicking assay for use with mosquitoes that identifies the presence or absence of specific peptide epitopes of circumsporozoite (CS) protein of Plasmodium falciparum and two strains of Plasmodium vivax (variants 210 and 247). The resulting assay (VecTest(TM) Malaria) is a rapid, one-step procedure using a 'dipstick' test strip capable of detecting and distinguishing between P. falciparum and P. vivax infections in mosquitoes. The objective of the present study was to test the efficacy, sensitivity, stability and field-user acceptability of this wicking dipstick assay. In collaboration with 16 test centres world-wide, we evaluated more than 40 000 units of this assay, comparing it to the standard CS ELISA. The 'VecTest(TM) Malaria' was found to show 92% sensitivity and 98.1% specificity, with 97.8% accuracy overall. In accelerated storage tests, the dipsticks remained stable for >15 weeks in dry conditions up to 45degreesC and in humid conditions up to 37degreesC. Evidently, this quick and easy dipstick test performs at an acceptable level of reliability and offers practical advantages for field workers needing to make rapid surveys of malaria vectors.

The modeling of turbulent reactive flows based on multiple mapping conditioning

A new modeling approach-multiple mapping conditioning (MMC)-is introduced to treat mixing and reaction in turbulent flows. The model combines the advantages of the probability density function and the conditional moment closure methods and is based on a certain generalization of the mapping closure concept. An equivalent stochastic formulation of the MMC model is given. The validity of the closuring hypothesis of the model is demonstrated by a comparison with direct numerical simulation results for the three-stream mixing problem. (C) 2003 American Institute of Physics.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

The Multiple-partners assignment game with heterogeneous sales and multi-unit demands: competitive equilibria

A multiple-partners assignment game with heterogeneous sales and multiunit demands consists of a set of sellers that own a given number of indivisible units of (potentially many different) goods and a set of buyers who value those units and want to buy at most an exogenously fixed number of units. We define a competitive equilibrium for this generalized assignment game and prove its existence by using only linear programming. In particular, we show how to compute equilibrium price vectors from the solutions of the dual linear program associated to the primal linear program defined to find optimal assignments. Using only linear programming tools, we also show (i) that the set of competitive equilibria (pairs of price vectors and assignments) has a Cartesian product structure: each equilibrium price vector is part of a competitive equilibrium with all optimal assignments, and vice versa; (ii) that the set of (restricted) equilibrium price vectors has a natural lattice structure; and (iii) how this structure is translated into the set of agents' utilities that are attainable at equilibrium.

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.

Multiple sclerosis risk variant HLA-DRB1*1501 associates with high expression of DRB1 gene in different human populations.

The human leukocyte antigen (HLA) DRB1*1501 has been consistently associated with multiple sclerosis (MS) in nearly all populations tested. This points to a specific antigen presentation as the pathogenic mechanism though this does not fully explain the disease association. The identification of expression quantitative trait loci (eQTL) for genes in the HLA locus poses the question of the role of gene expression in MS susceptibility. We analyzed the eQTLs in the HLA region with respect to MS-associated HLA-variants obtained from genome-wide association studies (GWAS). We found that the Tag of DRB1*1501, rs3135388 A allele, correlated with high expression of DRB1, DRB5 and DQB1 genes in a Caucasian population. In quantitative terms, the MS-risk AA genotype carriers of rs3135388 were associated with 15.7-, 5.2- and 8.3-fold higher expression of DQB1, DRB5 and DRB1, respectively, than the non-risk GG carriers. The haplotype analysis of expression-associated variants in a Spanish MS cohort revealed that high expression of DRB1 and DQB1 alone did not contribute to the disease. However, in Caucasian, Asian and African American populations, the DRB1*1501 allele was always highly expressed. In other immune related diseases such as type 1 diabetes, inflammatory bowel disease, ulcerative colitis, asthma and IgA deficiency, the best GWAS-associated HLA SNPs were also eQTLs for different HLA Class II genes. Our data suggest that the DR/DQ expression levels, together with specific structural properties of alleles, seem to be the causal effect in MS and in other immunopathologies rather than specific antigen presentation alone.

Potential risk factors for multiple sclerosis in Rio de Janeiro: a case-control study.

PURPOSE. To evaluate potential risk factors for the development of multiple sclerosis in Brazilian patients. METHOD. A case control study was carried out in 81 patients enrolled at the Department of Neurology of the Hospital da Lagoa in Rio de Janeiro, and 81 paired controls. A standardized questionnaire on demographic, social and cultural variables, and medical and family history was used. Statistical analysis was performed using descriptive statistics and conditional logistic regression models with the SPSS for Windows software program. RESULTS. Having standard vaccinations (vaccinations specified by the Brazilian government) (OR=16.2; 95% CI=2.3-115.2), smoking (OR=7.6; 95% CI=2.1-28.2), being single (OR=4.7; 95% CI=1.4-15.6) and eating animal brain (OR=3.4; 95% CI=1.2-9.8) increased the risk of developing MS. CONCLUSIONS. RESULTS of this study may contribute towards better awareness of the epidemiological characteristics of Brazilian patients with multiple sclerosis.

Multiple miscarriages are associated with the risk of ovarian cancer: results from the European Prospective Investigation into Cancer and Nutrition.

While the risk of ovarian cancer clearly reduces with each full-term pregnancy, the effect of incomplete pregnancies is unclear. We investigated whether incomplete pregnancies (miscarriages and induced abortions) are associated with risk of epithelial ovarian cancer. This observational study was carried out in female participants of the European Prospective Investigation into Cancer and Nutrition (EPIC). A total of 274,442 women were followed from 1992 until 2010. The baseline questionnaire elicited information on miscarriages and induced abortions, reproductive history, and lifestyle-related factors. During a median follow-up of 11.5 years, 1,035 women were diagnosed with incident epithelial ovarian cancer. Despite the lack of an overall association (ever vs. never), risk of ovarian cancer was higher among women with multiple incomplete pregnancies (HR(≥4vs.0): 1.74, 95% CI: 1.20-2.70; number of cases in this category: n = 23). This association was particularly evident for multiple miscarriages (HR(≥4vs.0): 1.99, 95% CI: 1.06-3.73; number of cases in this category: n = 10), with no significant association for multiple induced abortions (HR(≥4vs.0): 1.46, 95% CI: 0.68-3.14; number of cases in this category: n = 7). Our findings suggest that multiple miscarriages are associated with an increased risk of epithelial ovarian cancer, possibly through a shared cluster of etiological factors or a common underlying pathology. These findings should be interpreted with caution as this is the first study to show this association and given the small number of cases in the highest exposure categories.

DRB1*03:01 haplotypes: differential contribution to multiple sclerosis risk and specific association with the presence of intrathecal IgM bands.

BACKGROUND Multiple sclerosis (MS) is a multifactorial disease with a genetic basis. The strongest associations with the disease lie in the Human Leukocyte Antigen (HLA) region. However, except for the DRB1*15:01 allele, the main risk factor associated to MS so far, no consistent effect has been described for any other variant. One example is HLA-DRB1*03:01, with a heterogeneous effect across populations and studies. We postulate that those discrepancies could be due to differences in the diverse haplotypes bearing that allele. Thus, we aimed at studying the association of DRB1*03:01 with MS susceptibility considering this allele globally and stratified by haplotypes. We also evaluated the association with the presence of oligoclonal IgM bands against myelin lipids (OCMB) in cerebrospinal fluid. METHODS Genotyping of HLA-B, -DRB1 and -DQA1 was performed in 1068 MS patients and 624 ethnically matched healthy controls. One hundred and thirty-nine MS patients were classified according to the presence (M+, 58 patients)/absence (M-, 81 patients) of OCMB. Comparisons between groups (MS patients vs. controls and M+ vs. M-) were performed with the chi-square test or the Fisher exact test. RESULTS Association of DRB1*03:01 with MS susceptibility was observed but with different haplotypic contribution, being the ancestral haplotype (AH) 18.2 the one causing the highest risk. Comparisons between M+, M- and controls showed that the AH 18.2 was affecting only M+ individuals, conferring a risk similar to that caused by DRB1*15:01. CONCLUSIONS The diverse DRB1*03:01-containing haplotypes contribute with different risk to MS susceptibility. The AH 18.2 causes the highest risk and affects only to individuals showing OCMB.

Genetics and molecular epidemiology of multiple myeloma: the rationale for the IMMEnSE consortium (review).

There is strong evidence suggesting the presence of a genetic component in the aetiology of multiple myeloma (MM). However no genetic risk factors have been unequivocally established so far. To further our understanding of the genetic determinants of MM risk, a promising strategy is to collect a large set of patients in a consortium, as successfully done for other cancers. In this article, we review the main findings in the genetic susceptibility and pharmacogenetics of MM and present the strategy of the IMMEnSE (International Multiple Myeloma rESEarch) consortium in contributing to determine the role of genetic variation in pharmacogenetics and in MM risk.

Multiple causes of the evolution of dispersal in metapopulations

Abstract This work investigates the outcome of the interaction of the multiple causes of selection acting on dispersal in metapopulations. Dispersal, defined here as the ability of individuals to move out of their natal population to reproduce in an other one, has three main causes. First, population variability, as caused by random population extinctions, induces high incentives to disperse through the probability to recolonize an empty population and thus to escape competition for space. This adds to the second cause, kin competition avoidance where individuals in a crowded patch will benefit from the release of competition with relatives caused by dispersal. Dispersal may thus be viewed as an altruistic act. Third, dispersal might evolve as a strategy of avoiding inbred matings which are expected to bear fitness costs due to the presence of a mutation load. The interaction of inbreeding avoidance and kin competition is explored in chapter 2. Conditions conducive to the establishment of a high relatedness within population are expected to induce high dispersal through both kin competition avoidance and inbreeding avoidance. However, the dynamics of inbreeding depression is bound to depend on the level of gene flow as well as on the deleterious mutation parameters. Mutations more prone to settle a high level of inbreeding depression will select for increased dispersal. Chapter 3 investigates the effect of the mating system on the joint dynamics of dispersal and inbreeding depression. Higher inbreeding rates as those found in various mating systems lead to a more efficient purge of the deleterious mutations. However, this decrease in the costs of inbreeding are usually accompanied by a higher within deme relatedness which balances the decreased effect of inbreeding avoidance on the evolution of dispersal. Finally, population turnover, as found in most natural populations has a dual effect on dispersal. Indeed, it increases dispersal by the increased probability of winning a breeding slot in extinct demes it creates but, on the other hand, it counter-selects for dispersal through the slow establishment of unsaturated demic conditions which contribute to lower the local competition for space. Résumé Ce travail se propose d'étudier les effets conjoints des multiples causes de l'évolution de la dispersion en métapopulation. La dispersion, définie ici comme étant la capacité de quitter sa population d'origine pour se reproduire dans une antre population, possède trois principales causes. Premièrement, l'extinction aléatoire de populations sélectionne pour plus de dispersion car elle augmente la Probabilité de recoloniser un patch éteint et donc d'échapper à la compétition locale. La seconde cause, l'évitement de la compétition de parentèle, sélectionne pour plus de dispersion par les bénéfices qu'elle apporte par diminution de la compétition entre individus apparentés. Troisièmement, la dispersion évolue "comme stratégie d'évitement de la dépression de consanguinité présente dans des petites populations isolées. L'interaction entre l'évitement de la consanguinité et de la compétition de parentèle est étudiée dans le chapitre 2. Les conditions conduisant à l'établissement d'un fort apparentement à l'intérieur des populations sont celles qui génèrent le plus de sélection pour la dispersion. Cependant, la dynamique de la dépression de consanguinité est dépendante de la dispersion entre populations ainsi que des paramètres des mutations délétères. Les mutations créant le plus de dépression de consanguinité sont celles qui sélectionneront le plus pour de la dispersion. Le chapitre 3 s'intéresse aux effets du système de reproduction sur la dynamique conjointe du fardeau de mutation et de la dispersion. La purge des mutations délétère étant plus sévère dans des conditions de forte consanguinité, elle diminue les coûts de la consanguinité mais est habituellement accompagné par une augmentation de l'apparentement et donc l'effet peut être neutre sur la dispersion. Finalement, le turnover de populations a un effet dual sur la dispersion. La dispersion est sélectionnée par l'augmentation de la probabilité de gagner une place de reproduction dans des patchs éteints mais elle est également contre sélectionnée par la désaturation des patchs causée par l'extinction et la diminution de la compétition pour l'espace qui intervient dans ce cas.

She’s a femme fatale: low-density larval development produces good disease vectors

Two hypotheses for how conditions for larval mosquitoes affect vectorial capacity make opposite predictions about the relationship of adult size and frequency of infection with vector-borne pathogens. Competition among larvae produces small adult females. The competition-susceptibility hypothesis postulates that small females are more susceptible to infection and predicts frequency of infection should decrease with size. The competition-longevity hypothesis postulates that small females have lower longevity and lower probability of becoming competent to transmit the pathogen and thus predicts frequency of infection should increase with size. We tested these hypotheses for Aedes aegypti in Rio de Janeiro, Brazil, during a dengue outbreak. In the laboratory, longevity increases with size, then decreases at the largest sizes. For field-collected females, generalised linear mixed model comparisons showed that a model with a linear increase of frequency of dengue with size produced the best Akaike’s information criterion with a correction for small sample sizes (AICc). Consensus prediction of three competing models indicated that frequency of infection increases monotonically with female size, consistent with the competition-longevity hypothesis. Site frequency of infection was not significantly related to site mean size of females. Thus, our data indicate that uncrowded, low competition conditions for larvae produce the females that are most likely to be important vectors of dengue. More generally, ecological conditions, particularly crowding and intraspecific competition among larvae, are likely to affect vector-borne pathogen transmission in nature, in this case via effects on longevity of resulting adults. Heterogeneity among individual vectors in likelihood of infection is a generally important outcome of ecological conditions impacting vectors as larvae.

HERV-W polymorphism in chromosome X is associated with multiple sclerosis risk and with differential expression of MSRV.

BACKGROUND Multiple Sclerosis (MS) is an autoimmune demyelinating disease that occurs more frequently in women than in men. Multiple Sclerosis Associated Retrovirus (MSRV) is a member of HERV-W, a multicopy human endogenous retroviral family repeatedly implicated in MS pathogenesis. MSRV envelope protein is elevated in the serum of MS patients and induces inflammation and demyelination but, in spite of this pathogenic potential, its exact genomic origin and mechanism of generation are unknown. A possible link between the HERV-W copy on chromosome Xq22.3, that contains an almost complete open reading frame, and the gender differential prevalence in MS has been suggested. RESULTS MSRV transcription levels were higher in MS patients than in controls (U-Mann-Whitney; p = 0.004). Also, they were associated with the clinical forms (Spearman; p = 0.0003) and with the Multiple Sclerosis Severity Score (MSSS) (Spearman; p = 0.016). By mapping a 3 kb region in Xq22.3, including the HERV-W locus, we identified three polymorphisms: rs6622139 (T/C), rs6622140 (G/A) and rs1290413 (G/A). After genotyping 3127 individuals (1669 patients and 1458 controls) from two different Spanish cohorts, we found that in women rs6622139 T/C was associated with MS susceptibility: [χ2; p = 0.004; OR (95% CI) = 0.50 (0.31-0.81)] and severity, since CC women presented lower MSSS scores than CT (U-Mann-Whitney; p = 0.039) or TT patients (U-Mann-Whitney; p = 0.031). Concordantly with the susceptibility conferred in women, rs6622139*T was associated with higher MSRV expression (U-Mann-Whitney; p = 0.003). CONCLUSIONS Our present work supports the hypothesis of a direct involvement of HERV-W/MSRV in MS pathogenesis, identifying a genetic marker on chromosome X that could be one of the causes underlying the gender differences in MS.