Publications

What is a Publication?
8 Publications visible to you, out of a total of 8

Abstract (Expand)

Despite the plethora of information on (S)-selective amine transaminases, the (R)-selective ones are still not well-studied; only a few structures are known to the day, and their substrate scope is limited, apart from a few stellar works on the field. Herein, Luminiphilus syltensis (R)-selective amine transaminase’s structure was elucidated to facilitate the engineering towards variants active on bulkier substrates. V37A variant led to increased activity towards 1-phenylpropylamine and to activity against 1-butylamine. On the contrary, S248 and T249 positions, located on the β-turn in P-pocket, seem crucial for maintaining enzyme’s activity.

Authors: Eleni Konia, Konstantinos Chatzicharalampous, Athina Drakonaki, Cornelia Muenke, Ulrich Ermler, Georgios Tsiotis, Ioannis V. Pavlidis

Date Published: 2021

Publication Type: Journal

Abstract (Expand)

For adaptation between anaerobic, micro-aerobic and aerobic conditions Escherichia coli's metabolism and in particular its electron transport chain (ETC) is highly regulated. Although it is known that the global transcriptional regulators FNR and ArcA are involved in oxygen response it is unclear how they interplay in the regulation of ETC enzymes under micro-aerobic chemostat conditions. Also, there are diverse results which and how quinones (oxidised/reduced, ubiquinone/other quinones) are controlling the ArcBA two-component system. In the following a mathematical model of the E. coli ETC linked to basic modules for substrate uptake, fermentation product excretion and biomass formation is introduced. The kinetic modelling focusses on regulatory principles of the ETC for varying oxygen conditions in glucose-limited continuous cultures. The model is based on the balance of electron donation (glucose) and acceptance (oxygen or other acceptors). Also, it is able to account for different chemostat conditions due to changed substrate concentrations and dilution rates. The parameter identification process is divided into an estimation and a validation step based on previously published and new experimental data. The model shows that experimentally observed, qualitatively different behaviour of the ubiquinone redox state and the ArcA activity profile in the micro-aerobic range for different experimental conditions can emerge from a single network structure. The network structure features a strong feed-forward effect from the FNR regulatory system to the ArcBA regulatory system via a common control of the dehydrogenases of the ETC. The model supports the hypothesis that ubiquinone but not ubiquinol plays a key role in determining the activity of ArcBA in a glucose-limited chemostat at micro-aerobic conditions.

Editor:

Date Published: 30th Sep 2014

Publication Type: Not specified

Abstract (Expand)

The efficient redesign of bacteria for biotechnological purposes, such as biofuel production, waste disposal or specific biocatalytic functions, requires a quantitative systems-level understanding of energy supply, carbon, and redox metabolism. The measurement of transcript levels, metabolite concentrations and metabolic fluxes per se gives an incomplete picture. An appreciation of the interdependencies between the different measurement values is essential for systems-level understanding. Mathematical modeling has the potential to provide a coherent and quantitative description of the interplay between gene expression, metabolite concentrations, and metabolic fluxes. Escherichia coli undergoes major adaptations in central metabolism when the availability of oxygen changes. Thus, an integrated description of the oxygen response provides a benchmark of our understanding of carbon, energy, and redox metabolism. We present the first comprehensive model of the central metabolism of E. coli that describes steady-state metabolism at different levels of oxygen availability. Variables of the model are metabolite concentrations, gene expression levels, transcription factor activities, metabolic fluxes, and biomass concentration. We analyze the model with respect to the production capabilities of central metabolism of E. coli. In particular, we predict how precursor and biomass concentration are affected by product formation.

Editor:

Date Published: 27th Mar 2014

Publication Type: Not specified

Abstract (Expand)

In Escherichia coli several systems are known to transport glucose into the cytoplasm. The main glucose uptake system under batch conditions is the glucose phosphoenolpyruvate:carbohydrate phosphotransferase system (glucose-PTS), but also the mannose-PTS, as well as the galactose and maltose transporters can translocate glucose. Mutant strains which lack the EIIBC protein of the glucose-PTS have been previously investigated because their lower rate of acetate formation offers advantages in industrial applications. Nevertheless, a systematic study to analyze the impact of the different glucose uptake systems has not been undertaken. Specifically, how the bacteria cope with the deletion of the major glucose uptake system and which alternative transporters react to compensate for this deficit has not been studied in detail. Therefore, a series of mutant strains were analyzed in aerobic and anaerobic batch cultures, as well as in glucose limited continuous cultivations. Deletion of EIIBC, disturbs glucose transport severely. cAMP-CRP levels rise, induction of the mgl-operon occurs. Nevertheless mgl transcription is not essential, as deletion of this transporter did not affect growth rate; the activities of the remaining transporters seems to be sufficient by induction of the galactose and maltose transporters. Despite the strong up-regulation of mgl under glucose limitations, deletion of this transport-system did not lead to further changes.

Editor:

Date Published: 8th Oct 2012

Publication Type: Not specified

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

Abstract

Not specified

Authors: , S. Frixel, ,

Date Published: 1st Jun 2011

Publication Type: Not specified

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

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

Powered by
(v.1.14.2)
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH