Project description:Development of an updated genome-scale metabolic model of Clostridium thermocellum and its application for integration of multi-omics datasets
Project description:Purpose: The purpose of this study is to clarify the response of Clostridium perfringens ATCC 13124 to host polysaccharide. Methods: Clostridium perfringens ATCC 13124 cells were cultured anaerobically in a medium containing Minimal medium-like condition Poor + medium, medium in which hyaluronic acid or mucin was added to Poor + medium. Total RNA was extracted from bacterial cells by the Hot-Phenol method. Samples for RNA-seq were prepared according to the Illmina protocol available from the manufacturer. Array leads passed through quality filters were analyzed at the transcript isoform level using bowtie v 1.1.2. Results: Using the optimized data analysis workflow, we mapped about 50 million sequence leads per sample to the whole genome of Clostridium perfringens ATCC 13124. In addition, 2735 transcripts in C. perfringens ATCC 13124 were identified using a Bowtie aligner. Lead counts per genome were extracted from known gene annotations using the HTSeq program.
Project description:Lee2008 - Genome-scale metabolic network of
Clostridium acetobutylicum (iJL432)
This model is described in the article:
Genome-scale reconstruction
and in silico analysis of the Clostridium acetobutylicum ATCC
824 metabolic network.
Lee J, Yun H, Feist AM, Palsson
BØ, Lee SY.
Appl. Microbiol. Biotechnol. 2008 Oct;
80(5): 849-862
Abstract:
To understand the metabolic characteristics of Clostridium
acetobutylicum and to examine the potential for enhanced
butanol production, we reconstructed the genome-scale metabolic
network from its annotated genomic sequence and analyzed
strategies to improve its butanol production. The generated
reconstructed network consists of 502 reactions and 479
metabolites and was used as the basis for an in silico model
that could compute metabolic and growth performance for
comparison with fermentation data. The in silico model
successfully predicted metabolic fluxes during the acidogenic
phase using classical flux balance analysis. Nonlinear
programming was used to predict metabolic fluxes during the
solventogenic phase. In addition, essential genes were
predicted via single gene deletion studies. This genome-scale
in silico metabolic model of C. acetobutylicum should be useful
for genome-wide metabolic analysis as well as strain
development for improving production of biochemicals, including
butanol.
This model is hosted on
BioModels Database
and identified by:
MODEL1507180030.
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To the extent possible under law, all copyright and related or
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Project description:Investigation of whole genome gene expression level changes in a Clostridium difficile fur (ferric uptake regulator) mutant, compared to the wild type strain 630 erm. The fur mutant analyzed in this study is further described in Ho and Ellermeier (2015) J. Bacteriology
