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Systems analysis of transcription factor activities in environments with stable and dynamic oxygen concentrations.

TitleSystems analysis of transcription factor activities in environments with stable and dynamic oxygen concentrations.
Publication TypeJournal Article
Year of Publication2012
AuthorsRolfe, MD, Ocone, A, Stapleton, MR, Hall, S, Trotter, EW, Poole, RK, Sanguinetti, G, Green, J
JournalOpen Biol
Volume2
Issue7
Pagination120091
Date Published2012 Jul
ISSN2046-2441
Abstract

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.

DOI10.1098/rsob.120091
Alternate JournalOpen Biol
PubMed ID22870390
PubMed Central IDPMC3411108

Comments

Paper on using stochastic models in analysing the response of E. coli to dynamic changes in O2.

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