| Metadata |
| datasetIdentifier | PASS00035 |
| datasetType | SRM |
| submitter | Christina Ludwig <ludwig@imsb.biol.ethz.ch> |
| submitter_organization | IMSB |
| lab_head_full_name | Ruedi Aebersold |
| lab_head_email | aebersold@imsb.biol.ethz.ch |
| lab_head_organization | IMSB |
| lab_head_country | Switzerland |
| datasetTag | Msn2_phospho |
| datasetTitle | Dynamic Msn2 phosphorylation changes in yeast upon glucose addition to starved cells |
| publicReleaseDate | 2012-06-01 00:00:00 |
| finalizedDate | |
| summary | Predictive dynamical models are critical for the analysis of complex biological systems. However, methods to systematically develop and discriminate between systems biology models are still lacking. Here, we describe a computational method that incorporates all hypothetical mechanisms about the architecture of a biological system into a single model, and automatically generates a set of simpler models compatible with observational data. As a proof-of-principle, we analyze the dynamic control of the transcription factor Msn2 in S. cerevisiae, specifically the short-term mechanisms of stress-release after starvation. Our method determined eleven out of 192 possible models as compatible with available Msn2 localization data. Iterations between model predictions and rationally designed phosphoproteomics and imaging experiments identified a single circuit topology whose relative probability of being most plausible among the topologies given the data exceeded 99 percent. This revealed a rapid switch in the phosphorylation state of Msn2 that results primarily from nuclear Msn2 phosphorylation as the key Msn2 control mechanism. Our computational method allows for the systematic construction of detailed dynamic models that yield detailed insight into non-obvious molecular mechanisms. |
| contributors | Christina Ludwig
Mikael Sunnaker
Elias Zamora-Sillero |
| publication | so far data not published |
| growth | Cells were grown in synthetic media as described in (Dechant et al, 2010). Yeast cells expressing Msn2-GFP were grown to mid-exponential phase (OD 0.7-1), harvested, washed twice and resuspended in synthetic complete (SC) medium without glucose. 20 min after the first wash with SC medium glucose was added to a final concentration of 2% and samples were withdrawn at the indicated time points. Cells were harvested after quenching with TCA (6.25% final) and washing with ice-cold acetone. |
| treatment | |
| extraction | Yeast cells were lysed in lysis buffer (8 M urea, 100 mM NH4HCO3, 5 mM ethylenediaminetetraacetic acid (EDTA), 1 mM tris(2-carboxyethyl)phosphine (TCEP), pH 8.0) using beads beating. Cell debris was removed by centrifugation and the protein content was determined with a bicinchoninic acid (BCA) protein assay (Pierce). |
| separation | |
| digestion | For each sample 2.0 mg of total protein was reduced (5 mM TCEP), alkylated (70 mM iodoacetamide), digested with trypsin (Promega) and prepared for a phospho-peptide enrichment procedure as described previously (Bodenmiller et al, 2007). Briefly, phospho-peptides were enriched with titanium dioxide (GL Sience), eluted with 0.3 M NH4OH (pH 10.5) and subsequently purified using C18 cartridges (C18 Micro Spin columns, The Nest Group Inc.). Finally, the phospho-peptide mixtures were dried, resolubilized in 0.1% formic acid and immediately analyzed. All samples were processed in parallel. |
| acquisition | For each sample 2.0 mg of total protein was reduced (5 mM TCEP), alkylated (70 mM iodoacetamide), digested with trypsin (Promega) and prepared for a phospho-peptide enrichment procedure as described previously (Bodenmiller et al, 2007). Briefly, phospho-peptides were enriched with titanium dioxide (GL Sience), eluted with 0.3 M NH4OH (pH 10.5) and subsequently purified using C18 cartridges (C18 Micro Spin columns, The Nest Group Inc.). Finally, the phospho-peptide mixtures were dried, resolubilized in 0.1% formic acid and immediately analyzed. All samples were processed in parallel. |
| informatics | All obtained SRM traces were analyzed using the software Skyline (MacLean et al, 2010). Interfered or noisy transitions were removed manually. For quantification the ratio between a given endogenous (light) and its isotopically-labeled reference peptide (heavy) was calculated from the sum of all light and heavy transition peak areas, respectively. As the reference phospho-peptide amount was kept constant through all samples, endogenous abundance changes between samples could be determined. All data were normalized relative to the mean of the starved condition from three independent experiments (see Supplementary Table S2). |
| instruments | TSQ Vantage |
| species | saccharomyces cerevisiae |
| massModifications | static: C+57.021464
variable: K+8.014199, R+10.008269
variable: P+ |
