Ontology highlight
ABSTRACT:
PROVIDER: PRJNA540913 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9005310.fastq.gz | Fastqsanger.gz | |||
| SRR9005320.fastq.gz | Fastqsanger.gz | |||
| SRR9005330.fastq.gz | Fastqsanger.gz | |||
| SRR9005340.fastq.gz | Fastqsanger.gz | |||
| SRR9005350.fastq.gz | Fastqsanger.gz |
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2025-04-03 | GSE267394 | GEO
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Project description:To identify soybean genes and QTLs associated with quantitative resistance to infection by the oomycete pathogen Phytophthora sojae, we conducted a very large-scale microarray experiment using 2522 Affymetrix GeneChips. The experiment involved assaying a total of 298 soybean recombinant inbred lines together with internal checks.
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Project description:Lausannevirus one year genome evolution in allopatric versus sympatric conditions
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Project description:Raghunathan2010 - Genome-scale metabolic
network of Francisella tularensis (iRS605)
This model is described in the article:
Systems approach to
investigating host-pathogen interactions in infections with the
biothreat agent Francisella. Constraints-based model of
Francisella tularensis.
Raghunathan A, Shin S, Daefler
S.
BMC Syst Biol 2010; 4: 118
Abstract:
BACKGROUND: Francisella tularensis is a prototypic example
of a pathogen for which few experimental datasets exist, but
for which copious high-throughout data are becoming available
because of its re-emerging significance as biothreat agent. The
virulence of Francisella tularensis depends on its growth
capabilities within a defined environmental niche of the host
cell. RESULTS: We reconstructed the metabolism of Francisella
as a stoichiometric matrix. This systems biology approach
demonstrated that changes in carbohydrate utilization and amino
acid metabolism play a pivotal role in growth, acid resistance,
and energy homeostasis during infection with Francisella. We
also show how varying the expression of certain metabolic genes
in different environments efficiently controls the metabolic
capacity of F. tularensis. Selective gene-expression analysis
showed modulation of sugar catabolism by switching from
oxidative metabolism (TCA cycle) in the initial stages of
infection to fatty acid oxidation and gluconeogenesis later on.
Computational analysis with constraints derived from
experimental data revealed a limited set of metabolic genes
that are operational during infection. CONCLUSIONS: This
integrated systems approach provides an important tool to
understand the pathogenesis of an ill-characterized biothreat
agent and to identify potential novel drug targets when rapid
target identification is required should such microbes be
intentionally released or become epidemic.
This model is hosted on
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and identified by:
MODEL1507180003.
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2015-07-30 | MODEL1507180003 | BioModels