Making life difficult for Clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization

Table 2 Percent change in model-predicted biomass production (growth) of C. difficile in different conditions

Microarray data accession	Condition	% change in
number/database		biomass (h ⁻¹)
E-GEOD-37442/
ArrayExpress
	Heat shock from 30 °C to 43 °C	↓24.3%
E-BUGS-56/
ArrayExpress
	Sub-MIC level of amoxicillin	↓27.4%
	Sub-MIC level of clindamycin	↓16.6%
	Sub-MIC level of metronidazole	↓2.3%
	BHI broth	↑1.0%
GSE22423/GEO
	Supplementation of 10mM cysteine	↑1.1%

The microarray data for each condition was obtained from the GEO or ArrayExpress databases, using the specified accession numbers. The differential gene expression levels obtained from analysis of this microarray data was used to make a metabolic model for each condition. These context-specific metabolic models were used to predict change in biomass production for each condition compared with the control of each microarray dataset

ISSN: 1752-0509