433 resultados para genetic background


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Background Maize streak virus -strain A (MSV-A; Genus Mastrevirus, Family Geminiviridae), the maize-adapted strain of MSV that causes maize streak disease throughout sub-Saharan Africa, probably arose between 100 and 200 years ago via homologous recombination between two MSV strains adapted to wild grasses. MSV recombination experiments and analyses of natural MSV recombination patterns have revealed that this recombination event entailed the exchange of the movement protein - coat protein gene cassette, bounded by the two genomic regions most prone to recombination in mastrevirus genomes; the first surrounding the virion-strand origin of replication, and the second around the interface between the coat protein gene and the short intergenic region. Therefore, aside from the likely adaptive advantages presented by a modular exchange of this cassette, these specific breakpoints may have been largely predetermined by the underlying mechanisms of mastrevirus recombination. To investigate this hypothesis, we constructed artificial, low-fitness, reciprocal chimaeric MSV genomes using alternating genomic segments from two MSV strains; a grass-adapted MSV-B, and a maize-adapted MSV-A. Between them, each pair of reciprocal chimaeric genomes represented all of the genetic material required to reconstruct - via recombination - the highly maize-adapted MSV-A genotype, MSV-MatA. We then co-infected a selection of differentially MSV-resistant maize genotypes with pairs of reciprocal chimaeras to determine the efficiency with which recombination would give rise to high-fitness progeny genomes resembling MSV-MatA. Results Recombinants resembling MSV-MatA invariably arose in all of our experiments. However, the accuracy and efficiency with which the MSV-MatA genotype was recovered across all replicates of each experiment depended on the MSV susceptibility of the maize genotypes used and the precise positions - in relation to known recombination hotspots - of the breakpoints required to re-create MSV-MatA. Although the MSV-sensitive maize genotype gave rise to the greatest variety of recombinants, the measured fitness of each of these recombinants correlated with their similarity to MSV-MatA. Conclusions The mechanistic predispositions of different MSV genomic regions to recombination can strongly influence the accessibility of high-fitness MSV recombinants. The frequency with which the fittest recombinant MSV genomes arise also correlates directly with the escalating selection pressures imposed by increasingly MSV-resistant maize hosts.

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Background Post traumatic stress disorder (PTSD) and depressive disorder are over represented in combat veterans. Veterans with both disorders have an increased risk of suicide. The nitric oxide synthase 1 adaptor protein (NOS1AP) gene, which modulates stress-evoked N-methyl-D-aspartate (NMDA) activity, was investigated in combat veterans. Methods A comprehensive genetic analysis of NOS1AP and its association with PTSD was investigated in Vietnam combat veterans with PTSD (n=121) and a group of healthy control individuals (n=237). PTSD patients were assessed for symptom severity and level of depression using the Mississippi Scale for Combat-Related PTSD and the Beck Depression Inventory-II (BDI). Results The G allele of NOS1AP SNP rs386231 was significantly associated with PTSD (p = 0.002). Analysis of variance revealed significant differences in BDI-II and Mississippi scores between genotypes for rs386231 with the GG genotype associated with increased severity of depression (p = 0.002 F = 6.839) and higher Mississippi Scale for Combat-Related PTSD scores (p = 0.033). Haplotype analysis revealed that the C/G haplotype (rs451275/rs386231) was significantly associated with PTSD (p = 0.001). Limitations The sample sizes in our study were not sufficient to detect SNP associations with very small effects. In addition the study was limited by its cross sectional design. Conclusions This is the first study reporting that a variant of the NOS1AP gene is associated with PTSD. Our data also suggest that a genetic variant in NOS1AP may increase the susceptibility to severe depression in patients with PTSD and increased risk for suicide.

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Deciding the appropriate population size and number of is- lands for distributed island-model genetic algorithms is often critical to the algorithm’s success. This paper outlines a method that automatically searches for good combinations of island population sizes and the number of islands. The method is based on a race between competing parameter sets, and collaborative seeding of new parameter sets. This method is applicable to any problem, and makes distributed genetic algorithms easier to use by reducing the number of user-set parameters. The experimental results show that the proposed method robustly and reliably finds population and islands settings that are comparable to those found with traditional trial-and-error approaches.

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Distributed Genetic Algorithms (DGAs) designed for the Internet have to take its high communication cost into consideration. For island model GAs, the migration topology has a major impact on DGA performance. This paper describes and evaluates an adaptive migration topology optimizer that keeps the communication load low while maintaining high solution quality. Experiments on benchmark problems show that the optimized topology outperforms static or random topologies of the same degree of connectivity. The applicability of the method on real-world problems is demonstrated on a hard optimization problem in VLSI design.

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This paper examines whether recent innovation in market design can address persistent problems of housing choice and affordability in the inner and middle suburbs of Australian cities. Australia's ageing middle suburbs are the result of a low density and highly car-dependent garden city greenfield approach to planning that failed to consider possible future resource or environmental constraints on urban development (Newton et al., 2011). Described as 'greyfield' sites in contrast to greenfield (signalling the change from rural to urban land use) and 'brownfield' (being the transformation of former industrial use to mixed use, including housing), intensification of development in such areas is expected to deliver positive social, economic and environmental outcomes (Trubka et al., 2008; Gurran et al., 2006; Newton et al., 2011; Goodman et al., 2010). Yet despite broad policy consensus progress remains elusive (Major Cities Unit, 2010). In this paper we argue that the application of market design theory, specifically through the internet-based coordination of market information, offers a new policy approach and practical measures to address these problems.

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Background: Dopamine D2 receptor (DRD2) is thought to be critical in regulating the dopaminergic pathway in the brain which is known to be important in the aetiology of schizophrenia. It is therefore not surprising that most antipsychotic medication acts on the Dopamine D2 receptor. DRD2 is widely expressed in brain, levels are reduced in brains of schizophrenia patients and DRD2 polymorphisms have been associated with reduced brain expression. We have previously identified a genetic variant in DRD2, rs6277 to be strongly implicated in schizophrenia susceptibility. Methods: To identity new associations in the DRD2 gene with disease status and clinical severity, we genotyped seven single nucleotide polymorphisms (SNPs) in DRD2 using a multiplex mass spectrometry method. SNPs were chosen using a haplotype block-based gene-tagging approach so the entire DRD2 gene was represented. Results: One polymorphism rs2734839 was found to be significantly associated with schizophrenia as well as late onset age. Individuals carrying the genetic variation were more than twice as likely to have schizophrenia compared to controls. Conclusions: Our results suggest that DRD2 genetic variation is a good indicator for schizophrenia risk and may also be used as a predictor age of onset.

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The Australian Research Council (ARC) Innovative Products Project aims to facilitate project innovation by exploring means to improve the diffusion of innovative products to road and bridge projects. It adopts a highly novel approach to achieve this end, developing three different ways of viewing the problem: (1) as a relational governance issue, (2) as an absorptive capacity issue and (3) as a knowlege intermediation issue. This report presents teh results of teh first phase of a three phase fieldwork program.

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The Kallikrein (KLK) gene locus encodes a family of serine proteases and is the largest contiguous cluster of protease-encoding genes attributed an evolutionary age of 330 million years. The KLK locus has been implicated as a high susceptibility risk loci in numerous cancer studies through the last decade. The KLK3 gene already has established clinical relevance as a biomarker in prostate cancer prognosis through its encoded protein, prostate-specific antigen. Data mined through genome-wide association studies (GWAS) and next-generation sequencing point to many important candidate single nucleotide polymorphisms (SNPs) in KLK3 and other KLK genes. SNPs in the KLK locus have been found to be associated with several diseases including cancer, hypertension, cardiovascular disease and atopic dermatitis. Moreover, introducing a model incorporating SNPs to improve the efficiency of prostate-specific antigen in detecting malignant states of prostate cancer has been recently suggested. Establishing the functional relevance of these newly-discovered SNPs, and their interactions with each other, through in silico investigations followed by experimental validation, can accelerate the discovery of diagnostic and prognostic biomarkers. In this review, we discuss the various genetic association studies on the KLK loci identified either through candidate gene association studies or at the GWAS and post-GWAS front to aid researchers in streamlining their search for the most significant, relevant and therapeutically promising candidate KLK gene and/or SNP for future investigations.

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The Kallikrein-related peptidase, KLK4, has been shown to be significantly overexpressed in prostate tumours in numerous studies and is suggested to be a potential biomarker for prostate cancer. KLK4 may also play a role in prostate cancer progression through its involvement in epithelial-mesenchymal transition, a more aggressive phenotype, and metastases to bone. It is well known that genetic variation has the potential to affect gene expression and/or various protein characteristics and hence we sought to investigate the possible role of single nucleotide polymorphisms (SNPs) in the KLK4 gene in prostate cancer. Assessment of 61 SNPs in the KLK4 locus (±10 kb) in approximately 1300 prostate cancer cases and 1300 male controls for associations with prostate cancer risk and/or prostate tumour aggressiveness (Gleason score <7 versus ≥7) revealed 7 SNPs to be associated with a decreased risk of prostate cancer at the Ptrend<0.05 significance level. Three of these SNPs, rs268923, rs56112930 and the HapMap tagSNP rs7248321, are located several kb upstream of KLK4; rs1654551 encodes a non-synonymous serine to alanine substitution at position 22 of the long isoform of the KLK4 protein, and the remaining 3 risk-associated SNPs, rs1701927, rs1090649 and rs806019, are located downstream of KLK4 and are in high linkage disequilibrium with each other (r2≥0.98). Our findings provide suggestive evidence of a role for genetic variation in the KLK4 locus in prostate cancer predisposition.

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Background: A random QTL effects model uses a function of probabilities that two alleles in the same or in different animals at a particular genomic position are identical by descent (IBD). Estimates of such IBD probabilities and therefore, modeling and estimating QTL variances, depend on marker polymorphism, strength of linkage and linkage disequilibrium of markers and QTL, and the relatedness of animals in the pedigree. The effect of relatedness of animals in a pedigree on IBD probabilities and their characteristics was examined in a simulation study. Results: The study based on nine multi-generational family structures, similar to a pedigree structure of a real dairy population, distinguished by an increased level of inbreeding from zero to 28 % across the studied population. Highest inbreeding level in the pedigree, connected with highest relatedness, was accompanied by highest IBD probabilities of two alleles at the same locus, and by lower relative variation coefficients. Profiles of correlation coefficients of IBD probabilities along the marked chromosomal segment with those at the true QTL position were steepest when the inbreeding coefficient in the pedigree was highest. Precision of estimated QTL location increased with increasing inbreeding and pedigree relatedness. A method to assess the optimum level of inbreeding for QTL detection is proposed, depending on population parameters. Conclusions: An increased overall relationship in a QTL mapping design has positive effects on precision of QTL position estimates. But the relationship of inbreeding level and the capacity for QTL detection depending on the recombination rate of QTL and adjacent informative marker is not linear. © 2010 Freyer et al., licensee BioMed Central Ltd.

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BACKGROUND: Infection by dengue virus (DENV) is a major public health concern in hundreds of tropical and subtropical countries. French Polynesia (FP) regularly experiences epidemics that initiate, or are consecutive to, DENV circulation in other South Pacific Island Countries (SPICs). In January 2009, after a decade of serotype 1 (DENV-1) circulation, the first cases of DENV-4 infection were reported in FP. Two months later a new epidemic emerged, occurring about 20 years after the previous circulation of DENV-4 in FP. In this study, we investigated the epidemiological and molecular characteristics of the introduction, spread and genetic microevolution of DENV-4 in FP. METHODOLOGY/PRINCIPAL FINDINGS: Epidemiological data suggested that recent transmission of DENV-4 in FP started in the Leeward Islands and this serotype quickly displaced DENV-1 throughout FP. Phylogenetic analyses of the nucleotide sequences of the envelope (E) gene of 64 DENV-4 strains collected in FP in the 1980s and in 2009-2010, and some additional strains from other SPICs showed that DENV-4 strains from the SPICs were distributed into genotypes IIa and IIb. Recent FP strains were distributed into two clusters, each comprising viruses from other but distinct SPICs, suggesting that emergence of DENV-4 in FP in 2009 resulted from multiple introductions. Otherwise, we observed that almost all strains collected in the SPICs in the 1980s exhibit an amino acid (aa) substitution V287I within domain I of the E protein, and all recent South Pacific strains exhibit a T365I substitution within domain III. CONCLUSIONS/SIGNIFICANCE: This study confirmed the cyclic re-emergence and displacement of DENV serotypes in FP. Otherwise, our results showed that specific aa substitutions on the E protein were present on all DENV-4 strains circulating in SPICs. These substitutions probably acquired and subsequently conserved could reflect a founder effect to be associated with epidemiological, geographical, eco-biological and social specificities in SPICs.

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For any discipline to be regarded as a professional undertaking by which its members may be treated as true “professionals” in a specific area, practitioners must clearly understand that discipline’s history as well as the place and significance of that history in current practice as well as its relevance to available technologies and artefacts at the time. This is common for many professional disciplines such as medicine, pharmacy, engineering, law and so on but not yet, this paper submits, in information technology. Based on twenty five elapsed years of experience in developing and delivering Cybersecurity courses at undergraduate and postgraduate levels, this paper proposes a rationale and set of differing perspectives for the planning and development of curricula relevant to the delivery of appropriate courses in the history of cybersecurity or information assurance to information and communications technology (ICT) students and thus to potential information technology professionals.

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Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.