Development of an animal model for allergic conjunctivitis: influence of genetic factors and allergen concentration on immune response

Purpose: Animal models of diseases are extremely important in the study of the physiopathogenesis of human diseases and for testing novel therapeutic interventions. The present study aimed to develop an animal model that simulates human allergic conjunctivitis and to study how allergic response may be influenced by the allergen dose used for immunization and by genetic factors. Methods: Sixty C57Bl/6 mice and 60 BALB/c mice were immunized with placebo, or 5 mu g or 500 mu g of allergen derived from Dermatophagoides pteronyssinus. After ocular challenge, the mice were examined in order to clinically verify the occurrence or not of conjunctivitis. Material obtained from animals was used for total and specific IgE and IgG1 dosage, for assays of Der p-specific lymphocyte proliferation and supernatant cytokine dosage, and for histopathological evaluation of conjunctiva. Results: We developed a murine model of allergic conjunctivitis induced by D. pteronyssinus. The model is similar to human disease both clinically and according to laboratory findings. In mouse, conjunctivitis was associated with a Th2 cytokine profile. However, IL-10 appeared to be involved with disease blockade. Mice of different strains have distinct immune responses, depending on the sensitization dose. Conclusions: The murine model developed is suitable for the study of immunopathogenesis and as a template for future therapies. Using BALB/c and C57BL/6 mice, we demonstrated that genetic factors play a role in determining susceptibility and resistance, as well as in establishing the allergen concentration needed to induce or to block disease development.

Modeling the optical constants of solids using acceptance-probability-controlled simulated annealing with an adaptive move generation procedure

The acceptance-probability-controlled simulated annealing with an adaptive move generation procedure, an optimization technique derived from the simulated annealing algorithm, is presented. The adaptive move generation procedure was compared against the random move generation procedure on seven multiminima test functions, as well as on the synthetic data, resembling the optical constants of a metal. In all cases the algorithm proved to have faster convergence and superior escaping from local minima. This algorithm was then applied to fit the model dielectric function to data for platinum and aluminum.

Genetic population structure of the catadromous perciform: Macquaria novemaculeata (Percichthyidae)

Genetic population structure in the catadromous Australian bass Macquaria novemaculeata was investigated using samples from four locations spanning 600 km along the eastern Australian coastline. Both allozymes and mtDNA control region sequences were examined. Population subdivision estimates based on allozymes revealed low levels of population structuring (G(st)=0.043, P<0.05). However, mtDNA indicated moderate levels of geographic population structure (G(st)=0.146, P<0.01). Phylogenetic analysis of mtDNA control region sequences (mean sequence divergence 1.9%) indicated little phylogeographic structuring. Results suggested that genotypic variation within each river population, while bring affected primarily by genetic drift, was also prevented from more significant divergence by homogenizing levels of gene flow-synonymous with a one-dimensional stepping-stone model of population structure. The catadromous life history of Macquaria novemaculeata was considered to br influential on the pattern of population structure displayed. Results were compared to the few population genetic studies involving catadromous fishes, indicating that catadromy alone is unlikely to be a good predictor of population structure. A more comprehensive suite of biological characteristics than simple life-history traits must be considered fully to allow reliable predictive models of population structure to be formulated. (C) 1997 The Fisheries Society of the British Isles.

Strategies for genetic model specification in the screening of genome-wide meta-analysis signals for further replication

Background Meta-analysis is increasingly being employed as a screening procedure in large-scale association studies to select promising variants for follow-up studies. However, standard methods for meta-analysis require the assumption of an underlying genetic model, which is typically unknown a priori. This drawback can introduce model misspecifications, causing power to be suboptimal, or the evaluation of multiple genetic models, which augments the number of false-positive associations, ultimately leading to waste of resources with fruitless replication studies. We used simulated meta-analyses of large genetic association studies to investigate naive strategies of genetic model specification to optimize screenings of genome-wide meta-analysis signals for further replication. Methods Different methods, meta-analytical models and strategies were compared in terms of power and type-I error. Simulations were carried out for a binary trait in a wide range of true genetic models, genome-wide thresholds, minor allele frequencies (MAFs), odds ratios and between-study heterogeneity (tau(2)). Results Among the investigated strategies, a simple Bonferroni-corrected approach that fits both multiplicative and recessive models was found to be optimal in most examined scenarios, reducing the likelihood of false discoveries and enhancing power in scenarios with small MAFs either in the presence or in absence of heterogeneity. Nonetheless, this strategy is sensitive to tau(2) whenever the susceptibility allele is common (MAF epsilon 30%), resulting in an increased number of false-positive associations compared with an analysis that considers only the multiplicative model. Conclusion Invoking a simple Bonferroni adjustment and testing for both multiplicative and recessive models is fast and an optimal strategy in large meta-analysis-based screenings. However, care must be taken when examined variants are common, where specification of a multiplicative model alone may be preferable.

On the extent of genetic variation for transpiration efficiency in sorghum

A glasshouse study examined 49 diverse sorghum lines for variation in transpiration efficiency. Three of the 49 lines grown were Sorghum spp, native to Australia; one was the major weed Johnson grass (Sorghum halepense), and the remaining 45 lines were cultivars of Sorghum bicolor. All plants were grown under non-limiting water and nutrient conditions using a semi-automatic pot watering system designed to facilitate accurate measurement of water use. Plants were harvested 56-58 days after sowing and dry weights of plant parts were determined. Transpiration efficiency differed significantly among cultivars. The 3 Australian native sorghums had much lower transpiration efficiency than the other 46 cultivars, which ranged from 7.7 to 6.0 g/kg. For the 46 diverse cultivars, the ratio of range in transpiration efficiency to its l.s.d. was 2.0, which was similar to that found among more adapted cultivars in a previous study. This is a significant finding as it suggests that there is likely to be little pay-off from pursuing screening of unadapted material for increased variation in transpiration efficiency. It is necessary, however, also to examine absolute levels of transpiration efficiency to determine whether increased levels have been found. The cultivar with greatest transpiration efficiency in this study (IS9710) had a value 9% greater (P < 0.05) than the accepted standard for adapted sorghum cultivars. The potential impact of such an increase in transpiration efficiency warrants continued effort to capture it. Transpiration efficiency has been related theoretically and experimentally to the degree of carbon isotope discrimination in leaf tissue in sorghum, which thus offers a relatively simple selection index. In this study, the variation in transpiration efficiency was not related simply to carbon isotope discrimination. Significant associations of transpiration efficiency with ash content and indices of photosynthetic capacity were found. However, the associations were not strong. These results suggest that a simple screening technique could not be based on any of the measures or indices analysed in this study. A better understanding of the physiological basis of the observed genetic differences in transpiration efficiency may assist in developing reliable selection indices. It was concluded that the potential value of the improvement in transpiration efficiency over the accepted standard and the degree of genetic variation found warrant further study on this subject. It was suggested that screening for genetic variation under water-limiting conditions may provide useful insights and should be pursued.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Genetic basis of the formation and identity of type I and type II neurons in Drosophila embryos

The embryonic peripheral nervous system of Drosophila contains two main types of sensory neurons: type I neurons, which innervate external sense organs and chordotonal organs, and type II multidendritic neurons, Here, we analyse the origin of the difference between type I and type II in the case of the neurons that depend on the proneural genes of the achaete-scute complex (ASC), We show that, in Notch(-) embryos, the type I neurons are missing while type nr neurons are produced in excess, indicating that the type I/type II choice relies on Notch-mediated cell communication, In contrast, both type I and type II neurons are absent in numb(-) embryos and after ubiquitous expression of tramtrack, indicating that the activity of numb and the absence of tramtrack are required to produce both external sense organ and multidendritic neural fates, The analysis of string(-) embryos reveals that when the precursors are unable to divide they differentiate mostly into type II neurons, indicating that the type II is the default neuronal fate, We also report a new mutant phenotype where the ASC-dependent neurons are converted into-type II neurons, providing evidence for the existence of one or more genes required for maintaining the alternative (type I) fate, Our results suggest that the same mechanism of type I/type II specification may operate at a late step of the ASC-dependent lineages, when multidendritic neurons arise as siblings of the external sense organ neurons and, at an early step, when other multidendritic neurons precursors arise as siblings of external sense organ precursors.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

A high-resolution genetic map of the familial Mediterranean fever candidate region allows identification of haplotype-sharing among ethnic groups

Familial Mediterranean fever (FMF) is a recessive disorder of inflammation caused by mutations in a gene (designated MEFV) on chromosome 16p13.3, We have recently constructed a 1-Mb cosmid contig that includes the FMF critical region. Here we show genotype data for 12 markers from our physical map, including 5 newly identified microsatellites, in FMF families. Intrafamilial recombinations placed MEFV in the similar to 285 kb between D16S468/D16S3070 and D16S3376. We observed significant linkage disequilibrium in the North African Jewish population, and historical recombinants in the founder haplotype placed MEFV between D16S3082 and D16S3373 (similar to 200 kb). In smaller panels of Iraqi Jewish, Arab, and Armenian families, there were significant allelic associations only for D16S3370 and D16S2617 among the Armenians. A sizable minority of Iraqi Jewish and Armenian carrier chromosomes appeared to be derived from the North African Jewish ancestral haplotype. We observed a unique FMF haplotype common to Iraqi Jews, Arabs, and Armenians and two other haplotypes restricted to either the Iraqi Jewish or the Armenian population. These data support the view that a few major mutations account for a large percentage of the cases of FMF and suggest that same of these mutations arose before the affected Middle Eastern populations diverged from one another. (C) 1997 Academic Press.

Genetic heterogeneity in familial acute myelogenous leukemia: Evidence for a second locus at chromosome 16q21-23.2

The identification of genes responsible for the rare cases of familial leukemia may afford insight into the mechanism underlying the more common sporadic occurrences. Here we test a single family with 11 relevant meioses transmitting autosomal dominant acute myelogenous leukemia (AML) and myelodysplasia for linkage to three potential candidate loci. In a different family with inherited AML, linkage to chromosome 21q22.1-22.2 was recently reported; we exclude linkage to 21q22.1-22.2, demonstrating that familial AML is a heterogeneous disease. After reviewing familial leukemia and observing anticipation in the form of a declining age of onset with each generation, we had proposed 9p21-22 and 16q22 as additional candidate loci. Whereas linkage to 9p21-22 can be excluded, the finding of a maximum two-point LOD score of 2.82 with the microsatellite marker D16S522 at a recombination fraction theta = 0 provides evidence supporting linkage to 16q22. Haplotype analysis reveals a 23.5-cM (17.9-Mb) commonly inherited region among all affected family members extending from D16S451 to D1GS289, In order to extract maximum linkage information with missing individuals, incomplete informativeness with individual markers in this interval, and possible deviance from strict autosomal dominant inheritance, we performed nonparametric linkage analysis (NPL) and found a maximum NPL statistic corresponding to a P-value of .00098, close to the maximum conditional probability of linkage expected for a pedigree with this structure. Mutational analysis in this region specifically excludes expansion of the AT-rich minisatellite repeat FRA16B fragile site and the CAG trinucleotide repeat in the E2F-4 transcription factor. The ''repeat expansion detection'' method, capable of detecting dynamic mutation associated with anticipation, more generally excludes large CAG repeat expansion as a cause of leukemia in this family.

Modeling of subcutaneous absorption kinetics of infusion solutions in the elderly using technetium

Absorption kinetics of solutes given with the subcutaneous administration of fluids is ill-defined. The gamma emitter, technitium pertechnetate, enabled estimates of absorption rate to be estimated independently using two approaches. In the first approach, the counts remaining at the site were estimated by imaging above the subcutaneous administration site, whereas in the second approach, the plasma technetium concentration-time profiles were monitored up to 8 hr after technetium administration. Boluses of technetium pertechnetate were given both intravenously and subcutaneously on separate occasions with a multiple dosing regimen using three doses on each occasion. The disposition of technetium after iv administration was best described by biexponential kinetics with a V-ss of 0.30 +/- 0.11 L/kg and a clearance of 30.0 +/- 13.1 ml/min. The subcutaneous absorption kinetics was best described as a single exponential process with a half-life of 18.16 +/- 3.97 min by image analysis and a half-life of 11.58 +/- 2.48 min using plasma technetium time data. The bioavailability of technetium by the subcutaneous route was estimated to be 0.96 +/- 0.12. The absorption half-life showed no consistent change with the duration of the subcutaneous infusion. The amount remaining at the absorption site with time was similar when analyzed using image analysis, and plasma concentrations assuming multiexponential disposition kinetics and a first-order absorption process. Profiles of fraction remaining at the absorption sire generated by deconvolution analysis, image analysis, and assumption of a constant first-order absorption process were similar. Slowing of absorption from the subcutaneous administration site is apparent after the last bolus dose in three of the subjects and can De associated with the stopping of the infusion. In a fourth subject, the retention of technetium at the subcutaneous site is more consistent with accumulation of technetium near the absorption site as a result of systemic recirculation.

Molecular epidemiology and genetic diversity of hepatitis B virus genotype E in an isolated Afro-Colombian community

Hepatitis B virus (HBV) infection is a significant public health concern with 350 million chronic carriers worldwide. Eight HBV genotypes (A-H) have been described so far. Genotype E (HBV/E) is widely distributed in West Africa and has rarely been found in other continents, except for a few cases in individuals with an African background. In this study, we characterized HBV genotypes in Quibdo, Colombia, by partial S/P gene sequencing, and found, for the first time, HBV/E circulating in nine Afro-Colombian patients who had no recent contact with Africa. The presence of HBV/E in this community as a monophyletic group suggests that it was a result of a recent introduction by some Afro-descendent contact or, alternatively, that the virus came with slaves brought to Colombia. By using sequences with sampling dates, we estimated the substitution rate to be about 3.2x10(-4) substitutions per site per year, which resulted in a time to the most recent common ancestor (TMRCA) of 29 years. In parallel, we also estimated the TMRCA for HBV/E by using two previously estimated substitution rates (7.7x10(-4) and 1.5x10(-5) substitutions per site per year). The TMRCA was around 35 years under the higher rate and 1500 years under the slower rate. In sum, this work reports for the first time the presence of an exclusively African HBV genotype circulating in South America. We also discuss the time of the entry of this virus into America based on different substitution rates estimated for HBV.

Frequency of concurrent autoimmune disorders in patients with autoimmune hepatitis - Effect of age, gender, and genetic background

Background: Concurrent autoimmune disorders (CAIDs) have been shown to occur in 22% to 34% of the patients with autoimmune hepatitis (AIH). Their presence has been linked to female gender, older age, and to certain HLA antigens, namely HLA-A11. DRB1*04, and DRB4*01. Aims: To assess the frequency and nature of CAID in Brazilian patients with AIH types 1 (AIH-1) and 2 (AIH-2) and to investigate the influence of age, gender, and genetic background in their occurrence. Patients and Methods: The presence and nature of CAID was studied in 143 patients [117 females, median age 11 (1.3 to 69)] with AIH-1 (n = 125) and AIH-2 (n = 28). HLA typing and tumor necrosis factor a gene promoter and exon I cytotoxic T lymphocyte associated antigen 4 (CTLA-4) gene polymorphisms were determined by polymerase chain reaction-based techniques. Results: The frequency of CAID was similar in patients with AIH-1 (14%) and AIH-2 (18%), but their nature was shown to vary. Arthritis was seen in half of the patients (n = 8) with CAID and AIH-1 and in none of those with AIH-2. Subjects with AIH-1 and CAID were shown to be older [24 (1.3 to 6 1) vs. 11 (1.3 to 69) y P = 0.02] and to have more often circulating antinuclear antibody (76% vs. 40%, P = 0.008) and less frequently antiactin antibodies (33% vs. 75%, P = 0.008) when compared with their counterparts without CAID. No particular HLA-DR and DQ alleles, as well as tumor necrosis factor a and CTLA-4 genotypes, were associated with CAID. Conclusions: The nature, but not the frequency, of CAID was shown to vary in AIH-1 and AIH-2. In subjects with AIH-1, CAID was linked to older subjects and to the presence of antinuclear antibody. No predisposition to CAID was associated to HLA-DRB1*04 or DDB4*01 alleles. The observed lower frequency of CAID could be attributed to the lower age of disease onset in Brazilians and to differences in HLA-encoded susceptibility to AIH-1 observed in South America.

Genetic algorithms for continuous optimization problems - a concept of parameter-space size adjustment

The concept of parameter-space size adjustment is pn,posed in order to enable successful application of genetic algorithms to continuous optimization problems. Performance of genetic algorithms with six different combinations of selection and reproduction mechanisms, with and without parameter-space size adjustment, were severely tested on eleven multiminima test functions. An algorithm with the best performance was employed for the determination of the model parameters of the optical constants of Pt, Ni and Cr.