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This article describes in extended detail the methodology applied for acquisition of transcriptomic data, and subsequent statistical data
modelling, published by Wilson et al. (2015) in a study of the effects
of carbon monoxide releasing molecule 3 (CORM 3 [Ru(CO)3Cl(glycinate)])
on heme deficient bacteria. The objective was to identify non heme
targets of CORM action. Carbon monoxide (CO) interacts with heme
containing proteins, in particular respiratory cytochromes; however,
CORMs have been shown to elicit multifaceted effects in bacteria,
suggesting that the compounds may have additional targets. We therefore
sought to elucidate the activity of CORM 3, the first water soluble CORM
and one of the most characterised CORMs to date, in bacteria devoid of
heme synthesis. Importantly, we also tested inactive CORM 3 (iCORM 3), a
ruthenium co ligand fragment that does not release CO, in order to
differentiate between CO and compound related effects. A well
established hemA mutant of Escherichia coli was used for the study and,
for comparison, parallel experiments were performed on the corresponding
wild type strain. Global transcriptomic changes induced by CORM 3 and
iCORM 3 were evaluated using a Two Color Microarray Based Prokaryote
Analysis (FairPlay III Labeling) by Agilent Technologies (Inc. 2009).
Data acquisition was carried out using Agilent Feature Extraction
software (v6.5) and data normalisation, as well as information about
gene products and their function was obtained from GeneSpring GX v7.3
(Agilent Technologies). Functional category lists were created using
KEGG (Kyoto Encyclopedia of Genes and Genomes). Relevant regulatory
proteins for each gene were identified, where available, using regulonDB
and EcoCyc (World Wide Web). Statistical data modelling was performed
on the gene expression data to infer transcription factor activities.
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