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

Wilson JL, McLean S, Begg R, Sanguinetti G, Poole RK - Genom Data (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology and Biotechnology, The University of Sheffield, Sheffield S10 2TN, UK.

ABSTRACT

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 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55097).

No MeSH data available.


Related in: MedlinePlus

Schematic representation of the TFInfer modelling framework. The conceptual model underpinning TFInfer is that external stimulation elicits transcriptional responses through changes in the activity of transcription factors (TFs). Hence, a stimulus (left-hand side) will determine a change in TF activity (middle layer) which will then result in observable changes in gene expression (right panel). The changes in gene expression depend on the TF activity changes and the wiring diagram of the regulatory network, determining which TF regulates which gene(s). TFInfer adopts a log-linear approximation to model TF-gene interactions in order to solve the inverse problem.
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f0010: Schematic representation of the TFInfer modelling framework. The conceptual model underpinning TFInfer is that external stimulation elicits transcriptional responses through changes in the activity of transcription factors (TFs). Hence, a stimulus (left-hand side) will determine a change in TF activity (middle layer) which will then result in observable changes in gene expression (right panel). The changes in gene expression depend on the TF activity changes and the wiring diagram of the regulatory network, determining which TF regulates which gene(s). TFInfer adopts a log-linear approximation to model TF-gene interactions in order to solve the inverse problem.

Mentions: Statistical data modelling was used to infer transcription factor (TF) activities based on the gene expression time-series generated from the microarray analyses. We used a probabilistic model [6], which integrates gene expression data with TF-target information (obtained from data bases such as EcoCyc) to determine the optimal TF activity profiles that can explain the expression data and compatibly with the constraints imposed by the network structure. The model adopts a log-linear approximation, expressing gene expression (log) changes as a weighted linear combination of changes in the activity of the TFs that regulate the genes in the network. A schematic representation of the model is given in Fig. 2; the approach is freely available as open-source software in the TFInfer tool [7]. Although the log-linear approximation is a simplification of the dynamics of transcription, its simplicity permits efficient, large-scale statistical inference, so that one may obtain data-driven estimates of many TF activities simultaneously. The approach has already been extensively adopted for bacterial transcriptomics, leading to numerous novel insights [8].


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

Wilson JL, McLean S, Begg R, Sanguinetti G, Poole RK - Genom Data (2015)

Schematic representation of the TFInfer modelling framework. The conceptual model underpinning TFInfer is that external stimulation elicits transcriptional responses through changes in the activity of transcription factors (TFs). Hence, a stimulus (left-hand side) will determine a change in TF activity (middle layer) which will then result in observable changes in gene expression (right panel). The changes in gene expression depend on the TF activity changes and the wiring diagram of the regulatory network, determining which TF regulates which gene(s). TFInfer adopts a log-linear approximation to model TF-gene interactions in order to solve the inverse problem.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4543538&req=5

f0010: Schematic representation of the TFInfer modelling framework. The conceptual model underpinning TFInfer is that external stimulation elicits transcriptional responses through changes in the activity of transcription factors (TFs). Hence, a stimulus (left-hand side) will determine a change in TF activity (middle layer) which will then result in observable changes in gene expression (right panel). The changes in gene expression depend on the TF activity changes and the wiring diagram of the regulatory network, determining which TF regulates which gene(s). TFInfer adopts a log-linear approximation to model TF-gene interactions in order to solve the inverse problem.
Mentions: Statistical data modelling was used to infer transcription factor (TF) activities based on the gene expression time-series generated from the microarray analyses. We used a probabilistic model [6], which integrates gene expression data with TF-target information (obtained from data bases such as EcoCyc) to determine the optimal TF activity profiles that can explain the expression data and compatibly with the constraints imposed by the network structure. The model adopts a log-linear approximation, expressing gene expression (log) changes as a weighted linear combination of changes in the activity of the TFs that regulate the genes in the network. A schematic representation of the model is given in Fig. 2; the approach is freely available as open-source software in the TFInfer tool [7]. Although the log-linear approximation is a simplification of the dynamics of transcription, its simplicity permits efficient, large-scale statistical inference, so that one may obtain data-driven estimates of many TF activities simultaneously. The approach has already been extensively adopted for bacterial transcriptomics, leading to numerous novel insights [8].

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology and Biotechnology, The University of Sheffield, Sheffield S10 2TN, UK.

ABSTRACT

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 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55097).

No MeSH data available.


Related in: MedlinePlus