Ontology highlight
ABSTRACT:
PROVIDER: PRJNA964706 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Although the major food-borne pathogen Campylobacter jejuni has been isolated from diverse animal, human and environmental sources, our knowledge of genomic diversity in C. jejuni is based exclusively on human or human food-chain-associated isolates. Studies employing multilocus sequence typing have indicated that some clonal complexes are more commonly associated with particular sources. Using comparative genomic hybridization on a collection of 80 isolates representing diverse sources and clonal complexes, we identified a separate clade comprising a group of water/wildlife isolates of C. jejuni with multilocus sequence types uncharacteristic of human food-chain-associated isolates. By genome sequencing one representative of this diverse group (C. jejuni 1336), and a representative of the bank-vole niche specialist ST-3704 (C. jejuni 414), we identified deletions of genomic regions normally carried by human food-chain-associated C. jejuni. Several of the deleted regions included genes implicated in chicken colonization or in virulence. Novel genomic insertions contributing to the accessory genomes of strains 1336 and 414 were identified. Comparative analysis using PCR assays indicated that novel regions were common but not ubiquitous among the water/wildlife group of isolates, indicating further genomic diversity among this group, whereas all ST-3704 isolates carried the same novel accessory regions. While strain 1336 was able to colonize chicks, strain 414 was not, suggesting that regions specifically absent from the genome of strain 414 may play an important role in this common route of Campylobacter infection of humans. We suggest that the genomic divergence observed constitutes evidence of adaptation leading to niche specialization. Data is also available from <ahref=http://bugs.sgul.ac.uk/E-BUGS-95 target=_blank>BuG@Sbase</a>
2011-04-15 | E-BUGS-95 | biostudies-arrayexpress
Project description:We have applied whole-genome microarray hybridization to compare the transcriptome of wild-type yeast strain Σ1278b during growth on a minimal medium containing 21 different single nitrogen sources including urea used as a reference condition. Keywords: growth conditions comparison
2006-09-01 | GSE4861 | GEO
Project description:Diverse Sources of C. difficile Infection Identified on Whole-Genome Sequencing
| PRJEB4556 | ENA
Project description:A common technique used for sensitive and specific diagnostic virus detection in clinical samples is PCR. However, an unbiased diagnostic microarray containing probes for all human pathogens could replace hundreds of individual PCR-reactions and remove the need for a clear clinical hypothesis regarding a suspected pathogen. We have established such a diagnostic platform for unbiased random amplification and subsequent microarray identification of viral pathogens in clinical samples. We show that Phi29 polymerase-amplification of a diverse set of clinical samples generates enough viral material for successful identification by the Microbial Detection Array developed at the Lawrence Livermore National Laboratory, California, USA, demonstrating the potential of the microarray technique for broad-spectrum pathogen detection. We conclude that this method detects both DNA and RNA virus, present in the same sample, as well as differentiates between different virus subtypes. We propose this assay for unbiased diagnostic analysis of all viruses in clinical samples. 19 clinical samples were analyzed for presence of virus using the MDA microarray. One of the samples is a negative control (water). One HCV-positive serum sample is included twice (HCV+1 and HCV+2).
2011-04-14 | E-GEOD-28597 | biostudies-arrayexpress
Project description:Background: The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children. Results: We conducted the largest genome by metabolome-wide association study to date of children (N=441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (>0.8) for 15.9% of features and low h2 (<0.2) for most of features (62.0%). The features with high h2 were enriched for amino acid and nucleic acid metabolism while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait locus (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5x10-12 (= 5 x 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride; CALN1 and a triglyceride; RBFOX1 and dimethylarginine. A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for ADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. Conclusion: Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.
2022-10-13 | ST002331 | MetabolomicsWorkbench
Project description:AbuOun2009 - Genome-scale metabolic network
of Salmonella typhimurium (iMA945)
This model is described in the article:
Genome scale reconstruction
of a Salmonella metabolic model: comparison of similarity and
differences with a commensal Escherichia coli strain.
AbuOun M, Suthers PF, Jones GI,
Carter BR, Saunders MP, Maranas CD, Woodward MJ, Anjum MF.
J. Biol. Chem. 2009 Oct; 284(43):
29480-29488
Abstract:
Salmonella are closely related to commensal Escherichia coli
but have gained virulence factors enabling them to behave as
enteric pathogens. Less well studied are the similarities and
differences that exist between the metabolic properties of
these organisms that may contribute toward niche adaptation of
Salmonella pathogens. To address this, we have constructed a
genome scale Salmonella metabolic model (iMA945). The model
comprises 945 open reading frames or genes, 1964 reactions, and
1036 metabolites. There was significant overlap with genes
present in E. coli MG1655 model iAF1260. In silico growth
predictions were simulated using the model on different carbon,
nitrogen, phosphorous, and sulfur sources. These were compared
with substrate utilization data gathered from high throughput
phenotyping microarrays revealing good agreement. Of the
compounds tested, the majority were utilizable by both
Salmonella and E. coli. Nevertheless a number of differences
were identified both between Salmonella and E. coli and also
within the Salmonella strains included. These differences
provide valuable insight into differences between a commensal
and a closely related pathogen and within different pathogenic
strains opening new avenues for future explorations.
This model is hosted on
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MODEL1507180009.
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2015-07-30 | MODEL1507180009 | BioModels
Project description:We enriched endothelial cells and other bone marrow cells in both fetal and adult stage to investigate Wnt signaling interaction using targeted scRNA-seq analysis. This analysis facilitate identification of sources of Wnt ligands and detection of Wnt receptor expression in bone marrow. The comparison of fetal and adult stage reveals differences of Wnt signaling in fetal and adult BM.
2022-02-05 | GSE183131 | GEO
Project description:Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes.Furthermore, all models were quality-controlled using Mᴇᴍᴏᴛᴇ, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.
2022-02-16 | MODEL2007110001 | BioModels