minimal medium with either glucose, mannose, galactose or pyruvate as sole carbon source
General protocol information
Flux analysis method:
13C constrained MFA
Platform:
GC-MS
Methods description - Notes
Strains, medium and cultivation conditions - Allliquid cultivations were carried out using minimal medium as described in Blank and Sauer (2004) [1]. The pre-cultures were always cultured in glucose minimal medium. Other carbon sources or labeled substrates were only added t
... o the experiment culture at 10 g/l each.
Determination of growth rate, uptake and secretion rates - Growth rates were determined in eight independent experiments on the naturally labeled carbon sources. To determine the growth rate, the optical density at a wavelength of 600 nm was measured in a spectra-photometer (Molecular Devices, Sunnyvale, USA) for 8 to 12 times over the whole growth curve of FY4. Specific growth rates were determined by linear regression of the logarithmic OD600 values over time from at least 6 data points at maximum rate.
Flux analysis - The labeled cultures were inoculated with an OD600 of 0.015 or less. 1 ml of culture was harvested during midexponential growth (OD600 0.5 - 1.2). The cells were washed three times with ddH2O and stored at -20°C for GC-MS analysis. The supernatant was stored for determining uptake and secretion rates of glucose, mannose, galactose, pyruvate, ethanol, acetate, glycerol and succinate at -20°C. The experiment was repeated at least two times.
Protein abundance measurement - The enzyme expression level was calculated from the slope between the biomass signal (light scattering) [2] (excitation at 620 nm) and the GFP signal (excitation at 486 nm, emission at 510 nm) gained from the protein GFP-fusion strains [3].
Prediction of involved transcription factors - Transcription factors are more often associated with a subset of differentially expressed proteins than expected by chance were determined by a statistical analysis adopted from Boyle et al. [4] as outlined in Kümmel et al. [5]: The likelihood is calculated with the hypergeometric distribution that a transcription factor is associated with the differential expressed enzymes between two conditions compared to all enzymes.
-----------References------------
[1] Blank LM, Sauer U: TCA cycle activity in Saccharomyces cerevisiae is a function of the environmentally determined specific growth and glucose uptake rates. Microbiol 2004, 150:1085-1093. https://doi.org/cq4g5w [2] Kensy F, Büchs J: Online-Monitoring von Microfermentationen. Laborwelt 2006, 7.
[3] Huh WK, Flavo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, O’Shea EK: Global analysis of protein localization in budding yeast. Nature 2003, 425:686-691. https://doi.org/fvhxr7 [4] Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G: GO: TermFinder - open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 2004, 20(18):3710-3715. https://doi.org/cpcms8 [5] Kümmel A, Ewald JC, Fendt SM, Jol S, Picotti P, Aebersold R, Sauer U, Zamboni N, Heinemann M: Differential glucose repression in common yeast strains in response to HXK2 deletions. FEMS Yeast Res 2010. https://doi.org/fjfnjr
Strains, medium and cultivation conditions - Allliquid cultivations were carried out using minimal medium as described in Blank and Sauer (2004) [1]. The pre-cultures were always cultured in glucose minimal medium. Other carbon sources or labeled substrates were only added t o the experiment culture at 10 g/l each.
Determination of growth rate, uptake and secretion rates - Growth rates were determined in eight independent experiments on the naturally labeled carbon sources. To determine the growth rate, the optical density at a wavelength of 600 nm was measured in a spectra-photometer (Molecular Devices, Sunnyvale, USA) for 8 to 12 times over the whole growth curve of FY4. Specific growth rates were determined by linear regression of the logarithmic OD600 values over time from at least 6 data points at maximum rate.
Flux analysis - The labeled cultures were inoculated with an OD600 of 0.015 or less. 1 ml of culture was harvested during midexponential growth (OD600 0.5 - 1.2). The cells were washed three times with ddH2O and stored at -20°C for GC-MS analysis. The supernatant was stored for determining uptake and secretion rates of glucose, mannose, galactose, pyruvate, ethanol, acetate, glycerol and succinate at -20°C. The experiment was repeated at least two times.
Protein abundance measurement - The enzyme expression level was calculated from the slope between the biomass signal (light scattering) [2] (excitation at 620 nm) and the GFP signal (excitation at 486 nm, emission at 510 nm) gained from the protein GFP-fusion strains [3].
Prediction of involved transcription factors - Transcription factors are more often associated with a subset of differentially expressed proteins than expected by chance were determined by a statistical analysis adopted from Boyle et al. [4] as outlined in Kümmel et al. [5]: The likelihood is calculated with the hypergeometric distribution that a transcription factor is associated with the differential expressed enzymes between two conditions compared to all enzymes.
-----------References------------
[1] Blank LM, Sauer U: TCA cycle activity in Saccharomyces cerevisiae is a function of the environmentally determined specific growth and glucose uptake rates. Microbiol 2004, 150:1085-1093. https://doi.org/cq4g5w
[2] Kensy F, Büchs J: Online-Monitoring von Microfermentationen. Laborwelt 2006, 7.
[3] Huh WK, Flavo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, O’Shea EK: Global analysis of protein localization in budding yeast. Nature 2003, 425:686-691. https://doi.org/fvhxr7
[4] Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G: GO: TermFinder - open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 2004, 20(18):3710-3715. https://doi.org/cpcms8
[5] Kümmel A, Ewald JC, Fendt SM, Jol S, Picotti P, Aebersold R, Sauer U, Zamboni N, Heinemann M: Differential glucose repression in common yeast strains in response to HXK2 deletions. FEMS Yeast Res 2010. https://doi.org/fjfnjr
KiMoSys (https://kimosys.org). Data EntryID 128 (Saccharomyces cerevisiae). [online], [Accessed 21 November 2024]. Available from: https://kimosys.org/repository/128