Analysis of transcript changes in a heme-deficient mutant of Escherichia coli in response to CORM-3 [Ru(CO)3Cl(glycinate)]

Jayne Louise Wilson, Samantha Mclean, Ronald Begg, Guido Sanguinetti, Robert K. Poole

Research output: Contribution to journalArticlepeer-review


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. The transcriptomic data can be accessed through NCBI's Gene Expression Omnibus (GEO): series accession number GSE55097 (
Original languageEnglish
Pages (from-to)231-234
Number of pages4
JournalGenomics Data
Publication statusPublished - 1 Sep 2015


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