Dataset Information

Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis.

ABSTRACT: Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA). TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1) previous genome-wide direct gene-essentiality assignments; and, 2) flux balance analysis (FBA) predictions from an existing genome-scale metabolic model of Shewanella oneidensis MR-1. A three-way comparison between FBA, TIFA, and the direct essentiality calls was made to validate the TIFA approach. The refinement in the interpretation of observed transposon insertions demonstrated that genes without insertions are not necessarily essential, and that genes that contain insertions are not always nonessential. The TIFA calls were in reasonable agreement with direct essentiality calls for S. oneidensis, but agreed more closely with E. coli essentiality calls for orthologs. The TIFA gene essentiality calls were in good agreement with the MR-1 FBA essentiality predictions, and the agreement between TIFA and FBA predictions was substantially better than between the FBA and the direct gene essentiality predictions.

SUBMITTER: Yang H

PROVIDER: S-EPMC4168976 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis.

Yang Hong H Krumholz Elias W EW Brutinel Evan D ED Palani Nagendra P NP Sadowsky Michael J MJ Odlyzko Andrew M AM Gralnick Jeffrey A JA Libourel Igor G L IG

PLoS computational biology 20140918 9

Transposon mutagenesis, in combination with parallel sequencing, is becoming a powerful tool for en-masse mutant analysis. A probability generating function was used to explain observed miniHimar transposon insertion patterns, and gene essentiality calls were made by transposon insertion frequency analysis (TIFA). TIFA incorporated the observed genome and sequence motif bias of the miniHimar transposon. The gene essentiality calls were compared to: 1) previous genome-wide direct gene-essentialit ...[more]

PMID: 25233219

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Project description:UNLABELLED:Shewanella oneidensis is unable to metabolize the sugar xylose as a carbon and energy source. In the present study, an otherwise silent xylose catabolic pathway was activated in S. oneidensis by following an adaptive evolution strategy. Genome-wide scans indicated that the S. oneidensis genome encoded two proteins similar to the xylose oxido-reductase pathway enzymes xylose reductase (SO_0900) and xylulokinase (SO_4230), and purified SO_0900 and SO_4230 displayed xylose reductase and xylulokinase activities, respectively. The S. oneidensis genome was missing, however, an Escherichia coli XylE-like xylose transporter. After 12 monthly transfers in minimal growth medium containing successively higher xylose concentrations, an S. oneidensis mutant (termed strain XM1) was isolated for the acquired ability to grow aerobically on xylose as a carbon and energy source. Whole-genome sequencing indicated that strain XM1 contained a mutation in an unknown membrane protein (SO_1396) resulting in a glutamine-to-histidine conversion at amino acid position 207. Homology modeling demonstrated that the Q207H mutation in SO_1396 was located at the homologous xylose docking site in XylE. The expansion of the S. oneidensis metabolic repertoire to xylose expands the electron donors whose oxidation may be coupled to the myriad of terminal electron-accepting processes catalyzed by S. oneidensis Since xylose is a lignocellulose degradation product, this study expands the potential substrates to include lignocellulosic biomass. IMPORTANCE:The activation of an otherwise silent xylose metabolic system in Shewanella oneidensis is a powerful example of how accidental mutations allow microorganisms to adaptively evolve. The expansion of the S. oneidensis metabolic repertoire to xylose expands the electron donors whose oxidation may be coupled to the myriad of terminal electron-accepting processes catalyzed by S. oneidensis Since xylose is a lignocellulose degradation product, this study expands the potential substrates to include lignocellulosic biomass.

Project description:BACKGROUND: Iron homeostasis of Shewanella oneidensis, a gamma-proteobacterium possessing high iron content, is regulated by a global transcription factor Fur. However, knowledge is incomplete about other biological pathways that respond to changes in iron concentration, as well as details of the responses. In this work, we integrate physiological, transcriptomics and genetic approaches to delineate the iron response of S. oneidensis. RESULTS: We show that the iron response in S. oneidensis is a rapid process. Temporal gene expression profiles were examined for iron depletion and repletion, and a gene co-expression network was reconstructed. Modules of iron acquisition systems, anaerobic energy metabolism and protein degradation were the most noteworthy in the gene network. Bioinformatics analyses suggested that genes in each of the modules might be regulated by DNA-binding proteins Fur, CRP and RpoH, respectively. Closer inspection of these modules revealed a transcriptional regulator (SO2426) involved in iron acquisition and ten transcriptional factors involved in anaerobic energy metabolism. Selected genes in the network were analyzed by genetic studies. Disruption of genes encoding a putative alcaligin biosynthesis protein (SO3032) and a gene previously implicated in protein degradation (SO2017) led to severe growth deficiency under iron depletion conditions. Disruption of a novel transcriptional factor (SO1415) caused deficiency in both anaerobic iron reduction and growth with thiosulfate or TMAO as an electronic acceptor, suggesting that SO1415 is required for specific branches of anaerobic energy metabolism pathways. CONCLUSION: Using a reconstructed gene network, we identified major biological pathways that were differentially expressed during iron depletion and repletion. Genetic studies not only demonstrated the importance of iron acquisition and protein degradation for iron depletion, but also characterized a novel transcriptional factor (SO1415) with a role in anaerobic energy metabolism.

Project description:Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

Dataset Information

Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis.

Publications

Genome-scale metabolic network validation of Shewanella oneidensis using transposon insertion frequency analysis.

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