979 resultados para Gene isolation


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Fusarium oxysporum f. sp. cubense (Foc), causal agent of fusarium wilt of banana, is among the most destructive pathogens of banana and plantain. The development of a molecular diagnostic capable of reliably distinguishing between the various races of the pathogen is of key importance to disease management. However, attempts to distinguish isolates using the standard molecular loci typically used for fungal phylogenetics have been complicated by a poor correlation between phylogeny and pathogenicity. Among the available alternative loci are several putative effector genes, known as SIX genes, which have been successfully used to differentiate the three races of F. oxysporum f. sp. lycopersici. In this study, an international collection of Foc isolates was screened for the presence of the putative effector SIX8. Using a PCR and sequencing approach, variation in Foc-SIX8 was identified which allowed race 4 to be differentiated from race 1 and 2 isolates, and tropical and subtropical race 4 isolates to be distinguished from one another.

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A procedure has been developed for the isolation of very low density lipoproteins from hen's egg yolk plasma using DEAE-cellulose chromatography. This procedure is rapid and does not require ultracentrifugation and should, therefore, serve as a useful procedure for use in laboratories where this facility does not exist.

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Circulating tumor cells (CTCs) are the seeds for cancer metastases development, which is responsible for >90% of cancer-related deaths. Accurate quantification of CTCs in human fluids could be an invaluable tool for understanding cancer prognosis, delivering personalized medicine to prevent metastasis and finding cancer therapy effectiveness. Although CTCs were first discovered more than 200 years ago, until now it has been a nightmare for clinical practitioners to capture and diagnose CTCs in clinical settings. Our society needs rapid, sensitive, and reliable assays to identify the CTCs from blood in order to help save millions of lives. Due to the phenotypic EMT transition, CTCs are undetected for more than one-third of metastatic breast cancer patients in clinics. To tackle the above challenges, the first volume in “Circulating Tumor Cells (CTCs): Detection Methods, Health Impact and Emerging Clinical Challenges discusses recent developments of different technologies, which have the capability to target and elucidate the phenotype heterogenity of CTCS. It contains seven chapters written by world leaders in this area, covering basic science to possible device design which can have beneficial applications in society. This book is unique in its design and content, providing an in-depth analysis to elucidate biological mechanisms of cancer disease progression, CTC detection challenges, possible health effects and the latest research on evolving technologies which have the capability to tackle the above challenges. It describes the broad range of coverage on understanding CTCs biology from early predictors of the metastatic spread of cancer, new promising technology for CTC separation and detection in clinical environment and monitoring therapy efficacy via finding the heterogeneous nature of CTCs. (Imprint: Nova Biomedical)

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Natural biological suppression of soil-borne diseases is a function of the activity and composition of soil microbial communities. Soil microbe and phytopathogen interactions can occur prior to crop sowing and/or in the rhizosphere, subsequently influencing both plant growth and productivity. Research on suppressive microbial communities has concentrated on bacteria although fungi can also influence soil-borne disease. Fungi were analyzed in co-located soils 'suppressive' or 'non-suppressive' for disease caused by Rhizoctonia solani AG 8 at two sites in South Australia using 454 pyrosequencing targeting the fungal 28S LSU rRNA gene. DNA was extracted from a minimum of 125 g of soil per replicate to reduce the micro-scale community variability, and from soil samples taken at sowing and from the rhizosphere at 7 weeks to cover the peak Rhizoctonia infection period. A total of ∼994,000 reads were classified into 917 genera covering 54% of the RDP Fungal Classifier database, a high diversity for an alkaline, low organic matter soil. Statistical analyses and community ordinations revealed significant differences in fungal community composition between suppressive and non-suppressive soil and between soil type/location. The majority of differences associated with suppressive soils were attributed to less than 40 genera including a number of endophytic species with plant pathogen suppression potentials and mycoparasites such as Xylaria spp. Non-suppressive soils were dominated by Alternaria , Gibberella and Penicillum. Pyrosequencing generated a detailed description of fungal community structure and identified candidate taxa that may influence pathogen-plant interactions in stable disease suppression. © 2014 Penton et al.

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Fifty-four different sugarcane resistance gene analogue (RGA) sequences were isolated, characterized, and used to identify molecular markers linked to major disease-resistance loci in sugarcane. Ten RGAs were identified from a sugarcane stem expressed sequence tag (EST) library; the remaining 44 were isolated from sugarcane stem, leaf, and root tissue using primers designed to conserved RGA motifs. The map location of 31 of the RGAs was determined in sugarcane and compared with the location of quantitative trait loci (QTL) for brown rust resistance. After 2 years of phenotyping, 3 RGAs were shown to generate markers that were significantly associated with resistance to this disease. To assist in the understanding of the complex genetic structure of sugarcane, 17 of the 31 RGAs were also mapped in sorghum. Comparative mapping between sugarcane and sorghum revealed syntenic localization of several RGA clusters. The 3 brown rust associated RGAs were shown to map to the same linkage group (LG) in sorghum with 2 mapping to one region and the third to a region previously shown to contain a major rust-resistance QTL in sorghum. These results illustrate the value of using RGAs for the identification of markers linked to disease resistance loci and the value of simultaneous mapping in sugarcane and sorghum.

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We isolated and characterized 21 microsatellite loci in the vulnerable and iconic Australian lungfish, Neoceratodus forsteri. Loci were screened across eight individuals from the Burnett River and 40 individuals from the Pine River. Genetic diversity was low with between one and six alleles per locus within populations and a maximum expected heterozygosity of 0.774. These loci will now be available to assess effective population sizes and genetic structure in N. forsteri across its natural range in South East Queensland, Australia.

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Bats have been found to harbor a number of new emerging viruses with zoonotic potential and there has been a great deal of interest in identifying novel bat pathogens to determine risk to human and animal health. Many groups have identified novel viruses in bats by detection of viral nucleic acid, however virus isolation is still a challenge and there are few reports of viral isolates from bats. In recent years, our group has developed optimized procedures for virus isolation from bat urine, including the use of primary bat cells. In previous reports we have described the isolation of Hendra virus, Menangle virus and Cedar virus, in Queensland, Australia. Here, we report the isolation of four additional novel bat paramyxoviruses from urine collected from beneath pteropid bat (flying fox) colonies in Queensland and New South Wales during 2009-2011.

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Key message We detected seven QTLs for 100-grain weight in sorghum using an F 2 population, and delimited qGW1 to a 101-kb region on the short arm of chromosome 1, which contained 13 putative genes. Abstract Sorghum is one of the most important cereal crops. Breeding high-yielding sorghum varieties will have a profound impact on global food security. Grain weight is an important component of grain yield. It is a quantitative trait controlled by multiple quantitative trait loci (QTLs); however, the genetic basis of grain weight in sorghum is not well understood. In the present study, using an F2 population derived from a cross between the grain sorghum variety SA2313 (Sorghum bicolor) and the Sudan-grass variety Hiro-1 (S. bicolor), we detected seven QTLs for 100-grain weight. One of them, qGW1, was detected consistently over 2 years and contributed between 20 and 40 % of the phenotypic variation across multiple genetic backgrounds. Using extreme recombinants from a fine-mapping F3 population, we delimited qGW1 to a 101-kb region on the short arm of chromosome 1, containing 13 predicted gene models, one of which was found to be under purifying selection during domestication. However, none of the grain size candidate genes shared sequence similarity with previously cloned grain weight-related genes from rice. This study will facilitate isolation of the gene underlying qGW1 and advance our understanding of the regulatory mechanisms of grain weight. SSR markers linked to the qGW1 locus can be used for improving sorghum grain yield through marker-assisted selection.

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Microarrays are high throughput biological assays that allow the screening of thousands of genes for their expression. The main idea behind microarrays is to compute for each gene a unique signal that is directly proportional to the quantity of mRNA that was hybridized on the chip. A large number of steps and errors associated with each step make the generated expression signal noisy. As a result, microarray data need to be carefully pre-processed before their analysis can be assumed to lead to reliable and biologically relevant conclusions. This thesis focuses on developing methods for improving gene signal and further utilizing this improved signal for higher level analysis. To achieve this, first, approaches for designing microarray experiments using various optimality criteria, considering both biological and technical replicates, are described. A carefully designed experiment leads to signal with low noise, as the effect of unwanted variations is minimized and the precision of the estimates of the parameters of interest are maximized. Second, a system for improving the gene signal by using three scans at varying scanner sensitivities is developed. A novel Bayesian latent intensity model is then applied on these three sets of expression values, corresponding to the three scans, to estimate the suitably calibrated true signal of genes. Third, a novel image segmentation approach that segregates the fluorescent signal from the undesired noise is developed using an additional dye, SYBR green RNA II. This technique helped in identifying signal only with respect to the hybridized DNA, and signal corresponding to dust, scratch, spilling of dye, and other noises, are avoided. Fourth, an integrated statistical model is developed, where signal correction, systematic array effects, dye effects, and differential expression, are modelled jointly as opposed to a sequential application of several methods of analysis. The methods described in here have been tested only for cDNA microarrays, but can also, with some modifications, be applied to other high-throughput technologies. Keywords: High-throughput technology, microarray, cDNA, multiple scans, Bayesian hierarchical models, image analysis, experimental design, MCMC, WinBUGS.

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The future use of genetically modified (GM) plants in food, feed and biomass production requires a careful consideration of possible risks related to the unintended spread of trangenes into new habitats. This may occur via introgression of the transgene to conventional genotypes, due to cross-pollination, and via the invasion of GM plants to new habitats. Assessment of possible environmental impacts of GM plants requires estimation of the level of gene flow from a GM population. Furthermore, management measures for reducing gene flow from GM populations are needed in order to prevent possible unwanted effects of transgenes on ecosystems. This work develops modeling tools for estimating gene flow from GM plant populations in boreal environments and for investigating the mechanisms of the gene flow process. To describe spatial dimensions of the gene flow, dispersal models are developed for the local and regional scale spread of pollen grains and seeds, with special emphasis on wind dispersal. This study provides tools for describing cross-pollination between GM and conventional populations and for estimating the levels of transgenic contamination of the conventional crops. For perennial populations, a modeling framework describing the dynamics of plants and genotypes is developed, in order to estimate the gene flow process over a sequence of years. The dispersal of airborne pollen and seeds cannot be easily controlled, and small amounts of these particles are likely to disperse over long distances. Wind dispersal processes are highly stochastic due to variation in atmospheric conditions, so that there may be considerable variation between individual dispersal patterns. This, in turn, is reflected to the large amount of variation in annual levels of cross-pollination between GM and conventional populations. Even though land-use practices have effects on the average levels of cross-pollination between GM and conventional fields, the level of transgenic contamination of a conventional crop remains highly stochastic. The demographic effects of a transgene have impacts on the establishment of trangenic plants amongst conventional genotypes of the same species. If the transgene gives a plant a considerable fitness advantage in comparison to conventional genotypes, the spread of transgenes to conventional population can be strongly increased. In such cases, dominance of the transgene considerably increases gene flow from GM to conventional populations, due to the enhanced fitness of heterozygous hybrids. The fitness of GM plants in conventional populations can be reduced by linking the selectively favoured primary transgene to a disfavoured mitigation transgene. Recombination between these transgenes is a major risk related to this technique, especially because it tends to take place amongst the conventional genotypes and thus promotes the establishment of invasive transgenic plants in conventional populations.

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This thesis studies human gene expression space using high throughput gene expression data from DNA microarrays. In molecular biology, high throughput techniques allow numerical measurements of expression of tens of thousands of genes simultaneously. In a single study, this data is traditionally obtained from a limited number of sample types with a small number of replicates. For organism-wide analysis, this data has been largely unavailable and the global structure of human transcriptome has remained unknown. This thesis introduces a human transcriptome map of different biological entities and analysis of its general structure. The map is constructed from gene expression data from the two largest public microarray data repositories, GEO and ArrayExpress. The creation of this map contributed to the development of ArrayExpress by identifying and retrofitting the previously unusable and missing data and by improving the access to its data. It also contributed to creation of several new tools for microarray data manipulation and establishment of data exchange between GEO and ArrayExpress. The data integration for the global map required creation of a new large ontology of human cell types, disease states, organism parts and cell lines. The ontology was used in a new text mining and decision tree based method for automatic conversion of human readable free text microarray data annotations into categorised format. The data comparability and minimisation of the systematic measurement errors that are characteristic to each lab- oratory in this large cross-laboratories integrated dataset, was ensured by computation of a range of microarray data quality metrics and exclusion of incomparable data. The structure of a global map of human gene expression was then explored by principal component analysis and hierarchical clustering using heuristics and help from another purpose built sample ontology. A preface and motivation to the construction and analysis of a global map of human gene expression is given by analysis of two microarray datasets of human malignant melanoma. The analysis of these sets incorporate indirect comparison of statistical methods for finding differentially expressed genes and point to the need to study gene expression on a global level.

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Octachlorocyclotetraphosphazene, N4P4CIa, reacts with dibenzylamine to give the chloro(dibenzy1amino) derivatives, N4P,C18,[N(CH2Ph)2],,, n = 1, 2 (two isomers), and 4 (three isomers). Nongeminal structures have been assigned to these compounds on the basis of ‘H and jlP NMR spectra. The presence of at least two tris(dibenzylamin0) derivatives in some reaction mixtures is also inferred from NMR spectra. Steric effects become important at the tetrakis stage of chlorine replacement, and further substitution by dibenzylamine to give monocyclic tetrameric derivatives does not occur. A “bicyclic” phosphazene, N4P4[N(CH2Ph)2]6(NCHzPh)is, obtained from the reaction of N4P4Claw ith an excess of dibenzylamine in boiling methyl cyanide. The formation of this derivative and its spectroscopic data are discussed.

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This thesis presents methods for locating and analyzing cis-regulatory DNA elements involved with the regulation of gene expression in multicellular organisms. The regulation of gene expression is carried out by the combined effort of several transcription factor proteins collectively binding the DNA on the cis-regulatory elements. Only sparse knowledge of the 'genetic code' of these elements exists today. An automatic tool for discovery of putative cis-regulatory elements could help their experimental analysis, which would result in a more detailed view of the cis-regulatory element structure and function. We have developed a computational model for the evolutionary conservation of cis-regulatory elements. The elements are modeled as evolutionarily conserved clusters of sequence-specific transcription factor binding sites. We give an efficient dynamic programming algorithm that locates the putative cis-regulatory elements and scores them according to the conservation model. A notable proportion of the high-scoring DNA sequences show transcriptional enhancer activity in transgenic mouse embryos. The conservation model includes four parameters whose optimal values are estimated with simulated annealing. With good parameter values the model discriminates well between the DNA sequences with evolutionarily conserved cis-regulatory elements and the DNA sequences that have evolved neutrally. In further inquiry, the set of highest scoring putative cis-regulatory elements were found to be sensitive to small variations in the parameter values. The statistical significance of the putative cis-regulatory elements is estimated with the Two Component Extreme Value Distribution. The p-values grade the conservation of the cis-regulatory elements above the neutral expectation. The parameter values for the distribution are estimated by simulating the neutral DNA evolution. The conservation of the transcription factor binding sites can be used in the upstream analysis of regulatory interactions. This approach may provide mechanistic insight to the transcription level data from, e.g., microarray experiments. Here we give a method to predict shared transcriptional regulators for a set of co-expressed genes. The EEL (Enhancer Element Locator) software implements the method for locating putative cis-regulatory elements. The software facilitates both interactive use and distributed batch processing. We have used it to analyze the non-coding regions around all human genes with respect to the orthologous regions in various other species including mouse. The data from these genome-wide analyzes is stored in a relational database which is used in the publicly available web services for upstream analysis and visualization of the putative cis-regulatory elements in the human genome.