Steady state extracellular fluxes in anaerobic yeast Saccharomyces cerevisiae
MOSES

experimentally measured extracellular fluxes in yeast Saccharomyces cerevisiae in anaerobic glucose limited chemostat (D=0.1 h-1) on minimal medium

Submitter: Maksim Zakhartsev

Assay type: Metabolite Profiling

Technology type: HPLC

Investigation: Steady state metabolic fluxes and metabolite co...

Study: Steady state fluxes in yeast Saccharomyces cere...

Fermentation G
COSMIC

acetic acid pulse 100mM acetic acid step 50mM, 70mM

Creator: Sebastian Curth

Submitter: Sebastian Curth

Fermentation F
COSMIC

acetic acid addition

step experiment: 50mM, 70mM, 100mM, 120mM

pulse 200mM

Creator: Sebastian Curth

Submitter: Sebastian Curth

Fermentation E
COSMIC

pulse acetic acid: 70mM (2x), 80mM, shift to 4.5, pulse acetic acid: 70mM

Creator: Sebastian Curth

Submitter: Sebastian Curth

Fermentation D
COSMIC

acetic acid addition 70mM

Creator: Sebastian Curth

Submitter: Sebastian Curth

Steady state transcriptional profiles at different oxygen concentrations
SUMO

Transcriptional profiles of steady-state chemostat cultures were measured at a range of oxygen levels using microarrays. Oxygen levels were defined by the aerobiosis scale of Alexeeva et al. (2002).

Creator: Matthew Rolfe

Submitter: Matthew Rolfe

Measured steady state concentrations of intracellular metabolites in yeast Saccharomyces cerevisiae anaerobic chemostat
MOSES

Steady state concentrations of intracellular metabolites in yeast Saccharomyces cerevisiae anaerobic chemostat at D = 0.1 h-1 on minimal medium. All metabolite concentrations are in mmol/L(CV).

Creator: Maksim Zakhartsev

Submitter: Maksim Zakhartsev

Reprogramming of Escherichia coli K-12 metabolism during the initial phase of transition from an anaerobic to a micro-aerobic environment
SUMO

Abstract (Expand)

Many bacteria undergo transitions between environments with differing O₂ availabilities as part of their natural lifestyles and during biotechnological processes. However, the dynamics of adaptation … when bacteria experience changes in O₂ availability are understudied. The model bacterium and facultative anaerobe Escherichia coli K-12 provides an ideal system for exploring this process.

Authors: Eleanor W Trotter, , Andrea M Hounslow, C Jeremy Craven, Michael P Williamson, , ,

Date Published: 27th Sep 2011

Publication Type: Not specified

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

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

Simplified absolute metabolite quantification by gas chromatography–isotope dilution mass spectrometry on the basis of commercially available source material
MOSES

Abstract (Expand)

In the field of metabolomics, GC–MS has rather established itself as a tool for semi-quantitative strategies like metabolic fingerprinting or metabolic profiling. Absolute quantification of intra- or … extracellular metabolites is nowadays mostly accomplished by application of diverse LC–MS techniques. Only few groups have so far adopted GC–MS technology for this exceptionally challenging task. Besides numerous and deeply investigated problems related to sample generation, the pronounced matrix effects in biological samples have led to the almost mandatory application of isotope dilution mass spectrometry (IDMS) for the accurate determination of absolute metabolite concentrations. Nevertheless, access to stable isotope labeled internal standards (ILIS), which are in many cases commercially unavailable, is quite laborious and very expensive. Here we present an improved and simplified gas chromatography–isotope dilution mass spectrometry (GC–IDMS) protocol for the absolute determination of intra- and extracellular metabolite levels. Commercially available 13C-labeled algal cells were used as a convenient source for the preparation of internal standards. Advantages as well as limitations of the described method are discussed.

Authors: Oliver Vielhauer, , Thomas Horn, Ralf Takors,

Date Published: 1st Dec 2011

Publication Type: Not specified

Berlin Progress Report
COSMIC
No description specified

Creator: Sebastian Curth

Submitter: Sebastian Curth

YEAST_chemostat_steady_state_culture
FAIRDOM

SAMPLE TYPE to describe samples of CEN.PK-113-7D yeast strain upon metabolic perturbation of the steady state culture by glucose pulse

SEEK ID (Integer) *, STRAIN (Registered Strain) , Genotype (String) , Cell culture ID (String) , Cell culture start date (Date) , Glucose pulse (concentration) (Integer) , Sampling date (Date) , Time after glucose pulse (Integer) , Cell part (Controlled Vocabulary - SysMO Sample Organism Part) , Age at sampling (Integer) *

yeast, genotype, chemostat, glucose pulse, Metabolomics

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