| 1. |
- Hyde, Kevin D., et al.
(författare)
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One stop shop: backbones trees for important phytopathogenic genera: I (2014)
- 2014
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Ingår i: Fungal diversity. - : Springer Science and Business Media LLC. - 1560-2745 .- 1878-9129. ; 67:1, s. 21-125
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Tidskriftsartikel (refereegranskat)abstract
- Many fungi are pathogenic on plants and cause significant damage in agriculture and forestry. They are also part of the natural ecosystem and may play a role in regulating plant numbers/density. Morphological identification and analysis of plant pathogenic fungi, while important, is often hampered by the scarcity of discriminatory taxonomic characters and the endophytic or inconspicuous nature of these fungi. Molecular (DNA sequence) data for plant pathogenic fungi have emerged as key information for diagnostic and classification studies, although hampered in part by non-standard laboratory practices and analytical methods. To facilitate current and future research, this study provides phylogenetic synopses for 25 groups of plant pathogenic fungi in the Ascomycota, Basidiomycota, Mucormycotina (Fungi), and Oomycota, using recent molecular data, up-to-date names, and the latest taxonomic insights. Lineage-specific laboratory protocols together with advice on their application, as well as general observations, are also provided. We hope to maintain updated backbone trees of these fungal lineages over time and to publish them jointly as new data emerge. Researchers of plant pathogenic fungi not covered by the present study are invited to join this future effort. Bipolaris, Botryosphaeriaceae, Botryosphaeria, Botrytis, Choanephora, Colletotrichum, Curvularia, Diaporthe, Diplodia, Dothiorella, Fusarium, Gilbertella, Lasiodiplodia, Mucor, Neofusicoccum, Pestalotiopsis, Phyllosticta, Phytophthora, Puccinia, Pyrenophora, Pythium, Rhizopus, Stagonosporopsis, Ustilago and Verticillium are dealt with in this paper.
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| 2. |
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| 3. |
- Jiye, A, et al.
(författare)
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Extraction and GC/MS analysis of the human blood plasma metabolome
- 2005
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Ingår i: ANALYTICAL CHEMISTRY. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 77:24, s. 8086-94
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Tidskriftsartikel (refereegranskat)abstract
- Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of experiment), for analyzing the human blood plasma metabolome by GC/MS. The protocol was optimized by evaluating the data for more than 500 resolved peaks using multivariate statistical tools including principal component analysis and partial least-squares projections to latent structures (PLS). The performance of five organic solvents (methanol, ethanol, acetonitrile, acetone, chloroform), singly and in combination, was investigated to optimize the LMC extraction. PLS analysis demonstrated that methanol extraction was particularly efficient and highly reproducible. The extraction and derivatization conditions were also optimized. Quantitative data for 32 endogenous compounds showed good precision and linearity. In addition, the determined amounts of eight selected compounds agreed well with analyses by independent methods in accredited laboratories, and most of the compounds could be detected at absolute levels of similar to 0.1 pmol injected, corresponding to plasma concentrations between 0.1 and 1 mu M. The results suggest that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.
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| 4. |
- Jiye, A, et al.
(författare)
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Global analysis of low-molecular-weight compounds in human plasma using GC/TOF-MS
- 2004
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Ingår i: DRUG METABOLISM REVIEWS. - 0360-2532. ; 36, s. 246-246
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- We developed a method for analysis of low-molecular-weight compounds (LMWC) in human plasma involving extraction of metabolites by organic solvents, derivatization of extract and final analysis by GC/TOF-MS. The overall strategy for the development of the method was based on using design-of-experimental (DOE), and data evaluation based on multivariate statistical tools like PCA and PLS. The results showed that the extraction efficiency for different solvents varied, and that methanol was important for high reproducibility. More than 300 compounds could be detected in one analysis. Forty five of them were identified, including amino acids, lipids and free fatty acids, organic acids, carbohydrates and so on. The quantitative data of these metabolites showed, with two exceptions, high precision and good linearity between response and concentration. By using this method it is now possible to analyze plasma samples with high throughput to identify metabolic biomarkers for different kinds of diseases.
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| 5. |
- Jonsson, Pär, et al.
(författare)
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High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses
- 2005
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Ingår i: Analytical Chemistry. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 77:17, s. 5635-5642
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Tidskriftsartikel (refereegranskat)abstract
- In metabolomics, the objective is to identify differences in metabolite profiles between samples. A widely used tool in metabolomics investigations is gas chromatography-mass spectrometry (GC/MS). More than 400 compounds can be detected in a single analysis, if overlapping GC/ MS peaks are deconvoluted. However, the deconvolution process is time-consuming and difficult to automate, and additional processing is needed in order to compare samples. Therefore, there is a need to improve and automate the data processing strategy for data generated in GC/MS-based metabolomics; if not, the processing step will be a major bottleneck for high-throughput analyses. Here we describe a new semiautomated strategy using a hierarchical multivariate curve resolution approach that processes all samples simultaneously. The presented strategy generates (after appropriate treatment, e.g., multivariate analysis) tables of all the detected metabolites that differ in relative concentrations between samples. The processing of 70 samples took similar time to that of the GC/TOFMS analyses of the samples. The strategy has been validated using two different sets of samples: a complex mixture of standard compounds and Arabidopsis samples.KeyWords Plus: CHROMATOGRAPHY MASS-SPECTROMETRY; PRINCIPAL COMPONENT ANALYSIS; SYSTEMS BIOLOGY; ARABIDOPSIS-THALIANA; CHEMOMETRIC ANALYSIS; 2-WAY DATA; MS; REGRESSION; RESOLUTION; ALIGNMENT
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| 6. |
- Nilsson, R. Henrik, 1976, et al.
(författare)
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Improving ITS sequence data for identification of plant pathogenic fungi
- 2014
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Ingår i: Fungal Diversity. - : Springer Science and Business Media LLC. - 1560-2745 .- 1878-9129. ; 67:1, s. 11-19
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Tidskriftsartikel (refereegranskat)abstract
- Plant pathogenic fungi are a large and diverse assemblage of eukaryotes with substantial impacts on natural ecosystems and human endeavours. These taxa often have complex and poorly understood life cycles, lack observable, discriminatory morphological characters, and may not be amenable to in vitro culturing. As a result, species identification is frequently difficult. Molecular (DNA sequence) data have emerged as crucial information for the taxonomic identification of plant pathogenic fungi, with the nuclear ribosomal internal transcribed spacer (ITS) region being the most popular marker. However, international nucleotide sequence databases are accumulating numerous sequences of compromised or low-resolution taxonomic annotations and substandard technical quality, making their use in the molecular identification of plant pathogenic fungi problematic. Here we report on a concerted effort to identify high-quality reference sequences for various plant pathogenic fungi and to re-annotate incorrectly or insufficiently annotated public ITS sequences from these fungal lineages. A third objective was to enrich the sequences with geographical and ecological metadata. The results – a total of 31,954 changes – are incorporated in and made available through the UNITE database for molecular identification of fungi (http://unite.ut.ee), including standalone FASTA files of sequence data for local BLAST searches, use in the next-generation sequencing analysis platforms QIIME and mothur, and related applications. The present initiative is just a beginning to cover the wide spectrum of plant pathogenic fungi, and we invite all researchers with pertinent expertise to join the annotation effort.
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| 7. |
- Zhang, Qi, et al.
(författare)
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GC/MS analysis of the rat urine for metabonomic research
- 2007
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Ingår i: Journal of chromatography. B. - Amsterdam : Elsevier. - 1570-0232 .- 1873-376X. ; 854:1-2, s. 20-25
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, an optimized protocol was established and validated for the metabonomic profiling in rat urine using GC/MS. The urine samples were extracted by methanol after treatment with urease to remove excessive urea, then the resulted supernatant was dried, methoximated, trimethylsilylated, and analyzed by GC/MS. Forty-nine endogenous metabolites were separated and identified in GC/MS chromatogram, of which 26 identified compounds were selected for quantitative analysis to evaluate the linearity, precision, and sensitivity of the method. It showed good linearity between mass spectrometry responses and relative concentrations of the 26 endogenous compounds over the range from 0.063 to 1.000(v/v, urine/urine+ water) and satisfactory reproducibility with intra-day and inter-days precision values all below 15%. The metabonomic profiling method based on GC/MS was successfully applied to urine samples from hyperlipidemia model rats. Obviously, separated clustering of model rats and the control rats were shown by principal components analysis (PCA); time-dependent metabonomic modification was detected as well. It was suggested that metabonomic profiling based on GC/MS be a robust method for urine samples.
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