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3 Publications visible to you, out of a total of 3
Systems analysis of transcription factor activities in environments with stable and dynamic oxygen concentrations

Abstract (Expand)

Understanding gene regulation requires knowledge of changes in transcription factor (TF) activities. Simultaneous direct measurement of numerous TF activities is currently impossible. Nevertheless, … statistical approaches to infer TF activities have yielded non-trivial and verifiable predictions for individual TFs. Here, global statistical modelling identifies changes in TF activities from transcript profiles of Escherichia coli growing in stable (fixed oxygen availabilities) and dynamic (changing oxygen availability) environments. A core oxygen-responsive TF network, supplemented by additional TFs acting under specific conditions, was identified. The activities of the cytoplasmic oxygen-responsive TF, FNR, and the membrane-bound terminal oxidases implied that, even on the scale of the bacterial cell, spatial effects significantly influence oxygen-sensing. Several transcripts exhibited asymmetrical patterns of abundance in aerobic to anaerobic and anaerobic to aerobic transitions. One of these transcripts, ndh, encodes a major component of the aerobic respiratory chain and is regulated by oxygen-responsive TFs ArcA and FNR. Kinetic modelling indicated that ArcA and FNR behaviour could not explain the ndh transcript profile, leading to the identification of another TF, PdhR, as the source of the asymmetry. Thus, this approach illustrates how systematic examination of regulatory responses in stable and dynamic environments yields new mechanistic insights into adaptive processes.

Authors: , Andrea Ocone, Melanie R Stapleton, Simon Hall, Eleanor W Trotter, , ,

Date Published: 8th Aug 2012

Publication Type: Not specified

Transcript profiling and inference of Escherichia coli K-12 ArcA activity across the range of physiologically relevant oxygen concentrations

Abstract (Expand)

Oxygen availability is the major determinant of the metabolic modes adopted by Escherichia coli. Whilst much is known about E. coli gene expression and metabolism under fully aerobic and anaerobic … conditions, the intermediate oxygen tensions that are encountered in natural niches are understudied. Here for the first time the transcript profiles of E. coli K-12 across the physiologically significant range of oxygen availabilities are described. These suggested a progressive switch to aerobic respiratory metabolism and a remodeling of the cell envelope as oxygen availability increased. The transcriptional responses were consistent with changes in the abundances of cytochrome bd and bo and outer membrane protein W. The observed transcript and protein profiles result from changes in the activities of regulators that respond to oxygen itself, or to metabolic and environmental signals that are sensitive to oxygen availability (aerobiosis). A probabilistic model (TFinfer) was used to predict the activity of the indirect oxygen-sensing two-component system ArcBA across the aerobiosis range. The model implied that the activity of the regulator ArcA correlated with aerobiosis, but not with the redox state of the ubiquinone pool, challenging the idea that ArcA activity is inhibited by oxidized ubiquinone. Measurement of the amount of phosphorylated ArcA correlated with the predicted ArcA activities and with aerobiosis, suggesting that fermentation product-mediated inhibition of ArcB phosphatase activity is the dominant mechanism for regulating ArcA activity under the conditions used here.

Authors: , , , Eleanor W Trotter, H M Shahzad Asif, Guido Sanguinetti, , ,

Date Published: 22nd Jan 2011

Publication Type: Not specified

TFInfer: a tool for probabilistic inference of transcription factor activities

Abstract (Expand)

SUMMARY: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now … been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively. AVAILABILITY: http://homepages.inf.ed.ac.uk/gsanguin/TFInfer.html CONTACT: gsanguin@staffmail.ed.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors: H M Shahzad Asif, , , Neil D Lawrence, Magnus Rattray,

Date Published: 24th Aug 2010

Publication Type: Not specified

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