Ruegeria pomeroyi transporter mutant substrate drawdown
Ontology highlight
ABSTRACT:
ORGANISM(S): Ruegeria Pomeroyi
TISSUE(S): Water
SUBMITTER: 
Mario Uchimiya
PROVIDER: ST002381 | MetabolomicsWorkbench | Wed Nov 16 00:00:00 GMT 2022
REPOSITORIES: MetabolomicsWorkbench
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Project description:Polyamines, such as putrescine and spermidine, are aliphatic organic compounds with multiple amino groups. They are found ubiquitously in marine systems. However, compared with the extensive studies on the concentration and fate of other dissolved organic nitrogen compounds in seawater, such as dissolved free amino acids (DFAA), investigations of bacterially-mediated polyamine transformations have been rare. Bioinformatic analysis identified genes encoding polyamine transporters in 74 of 109 marine bacterial genomes surveyed, a surprising frequency for a class of organic nitrogen compounds not generally recognized as an important source of carbon and nitrogen for marine bacterioplankton. The genome sequence of marine model bacterium Silicibacter pomeroyi DSS-3 contains a number of genes putatively involved in polyamine use, including six four-gene ATP-binding cassette transport systems. In the present study, polyamine uptake and metabolism by S. pomeroyi was examined to confirm the role of putative polyamine-related genes, and to investigate how well current gene annotations reflect function. A comparative whole-genome microarray approach (Bürgmann et al., 2007) allowed us to identify key genes for transport and metabolism of spermidine in this bacterium, and specify candidate genes for in situ monitoring of polyamine transformations in marine bacterioplankton communities. Silicibacter pomeroyi DSS-3 cells were grown in chemostat in a modified marine basal medium (MBM) containing spermidine as sole carbon and nitrogen source. Serine was used as a substrate to provide comparative data for an amino acid. After reach stable condition, total RNA were extracted, mRNA were purified and aa-aRNA were amplified and fluoresently labled before hybridize on array chips. The array design is described in Burgmann et al., 2007
2010-05-14 | E-GEOD-13781 | biostudies-arrayexpress
Project description:Sohn2012 - Genome-scale metabolic network of
Schizosaccharomyces pombe (SpoMBEL1693)
This model is described in the article:
Genome-scale metabolic model
of the fission yeast Schizosaccharomyces pombe and the
reconciliation of in silico/in vivo mutant growth.
Sohn SB, Kim TY, Lee JH, Lee
SY.
BMC Syst Biol 2012; 6: 49
Abstract:
BACKGROUND: Over the last decade, the genome-scale metabolic
models have been playing increasingly important roles in
elucidating metabolic characteristics of biological systems for
a wide range of applications including, but not limited to,
system-wide identification of drug targets and production of
high value biochemical compounds. However, these genome-scale
metabolic models must be able to first predict known in vivo
phenotypes before it is applied towards these applications with
high confidence. One benchmark for measuring the in silico
capability in predicting in vivo phenotypes is the use of
single-gene mutant libraries to measure the accuracy of
knockout simulations in predicting mutant growth phenotypes.
RESULTS: Here we employed a systematic and iterative process,
designated as Reconciling In silico/in vivo mutaNt Growth
(RING), to settle discrepancies between in silico prediction
and in vivo observations to a newly reconstructed genome-scale
metabolic model of the fission yeast, Schizosaccharomyces
pombe, SpoMBEL1693. The predictive capabilities of the
genome-scale metabolic model in predicting single-gene mutant
growth phenotypes were measured against the single-gene mutant
library of S. pombe. The use of RING resulted in improving the
overall predictive capability of SpoMBEL1693 by 21.5%, from
61.2% to 82.7% (92.5% of the negative predictions matched the
observed growth phenotype and 79.7% the positive predictions
matched the observed growth phenotype). CONCLUSION: This study
presents validation and refinement of a newly reconstructed
metabolic model of the yeast S. pombe, through improving the
metabolic model's predictive capabilities by reconciling the in
silico predicted growth phenotypes of single-gene knockout
mutants, with experimental in vivo growth data.
This model is hosted on
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MODEL1507180061.
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