Systems analysis of transcription factor activities in environments with stable and dynamic oxygen concentrations
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.
PubMed ID: 22870390
Publication type: Not specified
Journal: Open Biol
Date Published: 8th Aug 2012
Registered Mode: Not specified
The major theme of the research in my laboratory is bacterial gene regulation. We are interested in signal perception mechanisms (in particular oxygen); signal transduction (ligand induced protein confromational changes); interaction of transcription factors with the core transcription machinery; interactions between transcription factors to integrate multiple signals; and the influence of promoter architectures on these events. We are also interested in aome aspects of post-transcriptional ...
Work in my laboratory is focussed on microbial physiology - the study of how bacteria and other microorganisms work. Although rooted in the tradition of bacterial growth and intermediary metabolism, microbial physiology now embraces molecular biology, genetics, biochemistry, and indeed any discipline that can shed light on bacterial function. Much of our experimental work is conducted with Escherichia coli, the pre-eminent ‘model’ organism with unrivalled ease of genetic and physiological ...
I am a post-doctoral research associate working in Sheffield in the SUMO consortium. My research focuses on transcriptional regulation in E. coli, with particular emaphasis on the transcriptomic analysis of steady-state chemostat cultures using both microarray and qRT-PCR approaches.
Previous experience, especially that gained during my PhD, involved work on Salmonella physiology and lag phase growth, focusing particularly on gene-expression and transcriptional regulation. Other techniques used ...
SysMO is a European transnational funding and research initiative on "Systems Biology of Microorganisms".
The goal pursued by SysMO was to record and describe the dynamic molecular processes going on in unicellular microorganisms in a comprehensive way and to present these processes in the form of computerized mathematical models.
Systems biology will raise biomedical and biotechnological research to a new quality level and contribute markedly to progress in understanding. Pooling European research ...
Web page: http://sysmo.net/
"Systems Understanding of Microbial Oxygen responses" (SUMO) investigates how Escherichia coli senses oxygen, or the associated changes in oxidation/reduction balance, via the Fnr and ArcA proteins, how these systems interact with other regulatory systems, and how the redox response of an E. coli population is generated from the responses of single cells. There are five sub-projects to determine system properties and behaviour and three sub-projects to employ different and complementary modelling ...
This assay involved the determination of transcriptional profiles at 0, 2, 5, 10, 15 and 20 minutes through aerobic to anaerobic gas transitions and anaerobic to aerobic gas transitions. In each case an aerobic or anaerobic steady state was created, RNA sampled (0 min) and then the gas supply changed. RNA samples were then taken from the time at which the gas supply was changed.
For anaerobic conditions 5% CO2, 95% N2 was used.
The full transcriptional dataset is available from ArrayExpress ...
Submitter: Matthew Rolfe
Assay type: Transcriptional Profiling
Technology type: Microarray
Investigation: Dynamical studies for different oxygen availabi...
Data files: Dissolved oxygen tension aerobic to anaerobic .pdf, Dissolved oxygen tension anaerobic to aerobic .pdf, Entire transcriptional dataset - aerobic to ana..., Entire transcriptional dataset - anaerobic to a..., Filtered dataset - anaerobic to aerobic .csv, Filtered datset - aerobic to anaerobic transiti...
Snapshots: No snapshots
This .csv file contains the filtered datset of the aerobic to anaerobic transition. Values are shown if they show a statistically significant change relative to the 0 minute transcriptional profile (t-test p<0.05 and 2-fold cut-off).
This .csv file contains the filtered datset of the anaerobic to aerobic transition. Values are shown if they show a statistically significant change relative to the 0 minute transcriptional profile (t-test p<0.05 and 2-fold cut-off).
This is a pdf showing a graph of the dissolved oxygen tension of the culture during an aerobic to anaerobic transition.
This is a pdf showing a graph of the dissolved oxygen tension of the culture during an anaerobic to aerobic transition.
This .csv file contains the entire transcriptional dataset of the aerobic to anaerobic transition. The values shown are the gene-expression ratio at 2, 5, 10, 15 and 20 minutes relative to the 0 minute timepoint.
This .csv file contains the entire transcriptional dataset of the anaerobic to aerobic transition. The values shown are the gene-expression ratio at 2, 5, 10, 15 and 20 minutes relative to the 0 minute timepoint.
Bayesian model for inference of the activity of transcription factors from targets' mRNA levels. A standalone C sharp package (runs on linux and mac under MONO).