ABSTRACT: Triplicate chemostat cultivations were carried out with L. lactis subsp. lactis IL1403 at specific growth rates 0.1 and 0.5 h-1 on a chemically defined medium, where both, glucose and lysin (Lys) were jointly limiting the growth. After reaching the steady physiological state, medium was switched to heavy Lys (13C615N2-Lys) containing medium and incorporation of heavy amino acid into biomass was observed over the time course. Samples were collected before the medium switch (0 h) and 0.5; 1.5; 5; and 10 h after the medium switch. Cells were lysed in SDS lysis buffer and digested with trypsin according to Filter Aided Sample Preparation protocol (FASP). LC-MS/MS analysis were performed on Agilent 1200 series nanoflow system connected to a LTQ Orbitrap mass-spectrometer equipped with a nanoelectrospray ion source. Raw data files were analyzed with the MaxQuant software package (version 18.104.22.168). Generated peak lists were searched using the Andromeda search engine (built into MaxQuant) against L. lactis database (downloaded 28.06.2012 from http://www.genome.jp/kegg/genes.html). MaxQuant searches were performed with full tryptic specificity, a maximum of two missed cleavages and a mass tolerance of 0.5 Da for fragment ions. Carbamidomethylation of cysteine was set as a fixed modification and methionine oxidation and protein N-terminal acetylation were set as variable modification. The required false discovery rate (FDR) was set to 1% both for peptide and protein levels and the minimum required peptide length was set to six amino acids. In addition, "Match between runs" option with a time window of 1.5 min was allowed.
Project description:The comprehensive transcriptome of Streptomyces lividans TK24 in a biolector milliliter-scale cultivation system was compared in detail against an established 1000-fold larger lab-scale bioreactor system. For main-cultures, the modified minimal medium NMMP (D'Huys et al. 2011) was applied, containing 2.07 g/L NaH2PO4 x 1 H2O, 2.6 g/L K2HPO4, 0.6 MgSO4 x 7 H2O, 3.0 g/L (NH4)2SO4, 10 g/L D-glucose and 5 g/L BactoTM Casamino acids as well as several trace elements (1 mg/L ZnSO4 x 7 H2O, 1 mg/L FeSO4 x 7 H2O, 1 mg/L MnCl2 x 4 H2O, 1 mg/L CaCl2). pH was adjusted to 6.8 using 1 M KOH or 1 M H2SO4. In all MTP BioLector (BL) cultivations and in some bioreactor experiments (when indicated) 100 mM 2-(N-morpholino)ethanesulfonic acid (MES) was added as biological inert buffer system. Except for the phosphate buffer, all other components were prepared as stock solutions and sterilized separately. For MTP based cultivations the BL device, all stock solutions kept frozen at -20 C in aliquots sufficient for 50 mL final medium, to ensure identical conditions between experimental runs. For the system comparison experiments the same medium charge was used for all cultivations. 1 mL MTP-based BL cultivations MTP cultivations were performed in 48 well FlowerPlates (m2p-labs GmbH, Baesweiler, Germany), covered by a gas-permeable sealing foil (m2p-labs GmbH, Baesweiler, Germany). 1000 L cultivation medium was inoculated to a final OD600 of 0.2. Temperature and humidity was controlled in the incubation chamber of the BL device at 30 C and 89 % respectively. All signals (BS, DO, and pH), were measured with a cycle time of around 10 min. Altogether, 96 identical parallel milliliter-scale cultivations were performed in two separate but identical BL devices. One of these devices was additionally embedded in a lab-robotic liquid handling unit in the style of (Unthan et al. 2015; Rohe et al. 2012), to provide automated hourly sampling for substrate and cell dry weight analysis. The cultivations in the second BL unit served to generate samples for the omics-based analysis, discussed in the later sections. Bioreactor cultivations were carried out in a 1.5 L lab-scale system (DasGip, Jlich, Germany), equipped with two six blade, 46 mm Rushton turbines as well as sensors for DO (Hamilton, Visiferm DO 225) and pH (MettlerToledo, 405-DPAS-SC-K8S/225/120). Temperature was controlled at 30 C; evaporation was minimized by offgas cooling at 10 C. 700 mL phosphate buffer was sterilized directly within the reactor glass vessel; all other media components were added sterile and premixed subsequently by using a 500 mL feeding flask. 0.5 mL anti-foam (AF204 (Sigma, MO, USA)) was added before DO calibration. The final 1000 mL medium was inoculated with washed mycelium to a final OD600 of 0.2. Both, aeration and agitation were set to fixed values at 0.5 vvm and 800 rpm respectively. For both techniques samples were taken during the late growth phase, two hours after the switch to process phase II.
Project description:Wild type yeast cells were metabolically labeled in SILAC medium. The heavy (H)-SILAC-labeled cells were then continuously exposed to 0.01% MMS to induce replication stress, and the light (L)-SILAC-labeled cells were mock-exposed. After 3 hours, heavy and light cells were harvested and lysed. To ensure reproducibility, the entire experiment was repeated, and the labels were swapped such that the (L)-SILAC-labeled wild type yeast cells were exposed to 0.01% MMS for 3 hours, and the (H)-SILAC-labeled wild type yeast cells were mock-exposed. Lysates from heavy and light cells were mixed 1:1 by protein mass, reduced and treated with iodoacetamide and then digested with trypsin. 5 mg of each protein lysate was fractionated by high-pH reverse phase (RP) liquid chromatography into 12 samples. For proteome analysis, 2% of each fraction was dried down and re-suspended for LC-MS/MS analysis. The remaining 98% was processed to enrich for phosphopeptides using immobilized metal affinity chromatography (IMAC), dried down, and re-suspended in 0.1% FA, 3% ACN for LC-MS/MS analysis. Global and phosphopeptide-enriched samples were analyzed by LC-MS/MS on a Thermo LTQ-Orbitrap Velos mass spectrometer. Raw MS/MS spectra were searched against version 3.69 of the Yeast International Protein Index (IPI) sequence database using three independent search engines (MaxQuant/Andromeda, Spectrum Mill, and xTandem). All searches were performed with the tryptic enzyme constraint set for up to two missed cleavages, oxidized methionine set as a variable modification, and carbamidomethylated cysteine set as a static modification. For MaxQuant, the peptide MH+ mass tolerances were set at 20 ppm. For X!Tandem, the peptide MH+ mass tolerances were set at ±2.0 Da with post-search filtering of the precursor mass to 50 ppm and the fragment MH+ mass tolerances were set at ±0.5 Da. For Spectrum Mill, peptide MH+ mass tolerances were set at 20 ppm and fragment MH+ mass tolerances were set at ±0.7 Da. The overall FDR was set at ≤3% based on a decoy database search. Phosphosite localization probabilities are reported in the MaxQuant results. Any site with a probability greater than 0.8 was considered to be localized. Quantification of the Heavy:Light ratios was performed using MaxQuant software, with a minimum ratio count of 2 and using unique + razor peptides for quantification.
Project description:we report the identification and sequences of the tRNAome of industrially relevant microorganism Lactococcus lactis Three Next Generation sequencing runs annotated as S1, S2 and S3 were performed. Cells were harvested at exponential phase and tRNA was isolated. S1 and S2 were spiked with Phe-tRNAGAA from yeast and Lys-tRNAUUU from E. coli prior to cell lysis. S3 was spiked with Phe-tRNAGAA from yeast and Lys-tRNAUUU from E. coli before the library preparation to estimate the possible loss of tRNA in the extraction process.
Project description:We used phosphoproteomics to compare the responses of the ERK1/2 inhibitors, SCH772984 and GDC0994, and the MKK1/2 inhibitor, trametinib. These are compared with responses to the MKK1/2 inhibitor, selumetinib (AZD6244), previously measured by our lab in the same metastatic melanoma cell line. In three replicate experiments, we quantified a total of 12,805 class I phosphosites on 3,819 proteins in the trametinib-SCH772984-DMSO experiment, and 7,074 class I phosphosites on 2,453 in the GDC0994-SCH772984-DMSO experiment. This included 466 phosphosites that reproducibly decreased in response to at least one inhibitor in the trametinib-SCH772984-DMSO experiment and 414 phosphosites in the GDC0994-SCH772984-DMSO experiment. The results demonstrate linearity in signaling through the MAP kinase pathway. By comparing multiple inhibitors targeted to multiple tiers of protein kinases in the MAPK pathway, we gain insight into regulation and new targets of the oncogenic BRAF driver pathway in cancer cells, and a useful approach for evaluating the specificity of drugs and drug candidates. SILAC Experimental Design Experiment 1 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – Trametinib Replicate 2: Heavy – SCH772984, Medium – Trametinib, Light – DMSO Replicate 3: Heavy – Trametinib, Medium – DMSO, Light – SCH772984 SILAC Experimental Design Experiment 2 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – GDC0994 Replicate 2: Heavy – SCH772984, Medium – GDC0994, Light – DMSO Replicate 3: Heavy – GDC0994, Medium – DMSO, Light – SCH772984 File List 1. Zipped MaxQuant search results folder containing index and output folders for each raw file, ‘combined’ output folder, and mqpar.xml MaxQuant search parameters file 2. Individual raw files of phosphopeptide-enriched ERLIC fractions 3. Zipped MaxQuant version used for analysis 4. FASTA file containing Uniprot human identifications 5. Instructions for viewing annotated spectra
Project description:U2OS cells were split into two aliquots and cultured in one of
the following media (DMEM + 10 % dialysed FCS):
i) light medium: contains Arg-0 and Lys-0
ii) heavy medium: contains Arg-10 and Lys-8
Cells were passaged at least three times in the respective medium to ensure complete
labelling of proteins. SILAC experiments were performed in a forward and a reverse
reaction: for the forward experiment, heavy-labelled cells were irradiated with 20 Gy and
harvested 45 minutes after irradiation, while light-labelled cells were left untreated. In the
reverse experiment, labels were swapped and light-labelled cells were irradiated, while
heavy labelled cells were left untreated.
Project description:The goal was to determine the effect of agmatine on the trancriptional profile of L. lactis CECT 8666 strain. For that we compared the expression profile of L. lactis CECT 8666 cells grown in culture medium supplemented with 20 mM agmatine with the expression profile of L. lactis CECT 8666 cells grown in culture medium without agmatine. L. lactis CECT 8666 cells grown in GalM17 medium (reference) compared to L. lactis CECT 8666 cells grown in GalM17 medium supplemented with 20 mM agmatine (test).
Project description:This model is from the article:
Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics.
Zhike Zi, Zipei Feng, Douglas A Chapnick, Markus Dahl, Difan Deng, Edda Klipp, Aristidis Moustakas & Xuedong Liu Molecular Systems Biology
2011 May 24;7:492. 21613981
Mammalian cells can decode the concentration of extracellular transforming growth factor-β (TGF-β) and transduce this cue into appropriate cell fate decisions. How variable TGF-β ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-β stimulation. The TGF-β pathway elicits a transient signaling response to a single pulse of TGF-β stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-β pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-β levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-β pathway might be critical for cell fate determination.
Developer of the model: Zhike Zi
Reference: Zi Z. et al., Quantitative Analysis of Transient and Sustained Transforming Growth Factor-beta Signaling Dynamics, Molecular Systems Biology, 2011
1. The global parameter that set the type of stimulation
(a) for sustained TGF-beta stimulation: set stimulation_type = 1.
(b) for single pulse of TGF-beta stimulation: set stimulation_type = 2.
parameter "single_pulse_duration" is for the duration of stimulation, for example,
single_pulse_duration = 0.5, for 0.5 min (30 seconds) of TGF-beta stimulation.
*Note: make sure that the time course cover the time point when the event is triggered.
(c) for single pulse of TGF-beta stimulation in COPASI
change the trigger of event "single_pulse_TGF_beta_washout"
"and(eq(stimulation_type, 2), eq(time, single_pulse_duration))" (for SBML-SAT)
"and(eq(stimulation_type, 2), gt(time, single_pulse_duration))" (for COPASI)
2. Notes for TGF-beta dose in terms of molecules per cell
(a) The following equation applies for conversion of TGF-beta dose in molecules per cell
TGF_beta_dose_mol_per_cell = initial TGF_beta_ex*1e-9*Vmed*6e23
(b) for standard experimental setup 1e6 cells in 2 mL medium
0.001 nM initial TGF_beta_ex is approximately equal to the dose of 1200 TGF-beta molecules/cell
0.050 nM initial TGF_beta_ex is approximately equal to the dose of 60000 TGF-beta molecules/cell
(c) For 1e6 cells in 10 mL medium, please change the initial compartment size of Vmed and the corresponding assignment rule for Vmed.
initial Vmed = 1e-8 (1e6 cells in 10 mL medium)
Vmed = 0.010/(1e6*exp(log(1.45)*time/1440)) (1e6 cells in 10 mL medium)
3. Please note that this model contains events and the medium compartment size is varied.
4. For the model simulation in SBML-SAT, please remove initialAssignments and save it as SBML Level 2 Verion 1 file.
Project description:Background Lactococcus lactis is recognised as a safe (GRAS) microorganism and has hence gained interest in numerous biotechnological approaches. As it is fastidious for several amino acids, optimization of processes which involve this organism requires a thorough understanding of its metabolic regulations during multisubstrate growth. Results Using glucose limited continuous cultivations, specific growth rate dependent metabolism of L. Lactis including utilization of amino acids was studied based on extracellular metabolome, global transcriptome and proteome analysis. A new growth medium was designed with reduced amino acid concentrations to increase precision of measurements of consumption of amino acids. Consumption patterns were calculated for all 20 amino acids and measured carbon balance showed good fit of the data at all growth rates studied. It was observed that metabolism of L. lactis became more efficient with rising specific growth rate in the range 0.10 – 0.60 h-1, indicated by 30% increase in biomass yield based on glucose consumption, 50% increase in efficiency of nitrogen use for biomass synthesis, and 40% reduction in energy spilling. The latter was realized by decrease in the overall product formation and higher efficiency of incorporation of amino acids into biomass. L. lactis global transcriptome and proteome profiles showed good correlation supporting the general idea of transcription level control of bacterial metabolism, but the data indicated that substrate transport systems together with lower part of glycolysis in L. lactis were presumably under allosteric control. Conclusions The current study demonstrates advantages of the usage of strictly controlled continuous cultivation methods combined with multi-omics approach for quantitative understanding of amino acid and energy metabolism of Lactococcus lactis which is a valuable new knowledge for development of balanced growth media, gene manipulations for desired product formation etc. Moreover, collected dataset is an excellent input for developing metabolic models. Overall design: For microarray analysis, steady state chemostat culture of L. lactis IL1403 was used as reference (μ = 0.10 1/h). Subsequent quasi steady state points from A-stat experiment at specific growth rates 0.52 ± 0.03; 0.42 ± 0.02; 0.29 ± 0.01 1/h in biological duplicates and 0.17 1/h were compared to the reference sample.
Project description:We used phosphoproteomics to compare the responses of the ERK1/2 inhibitors, SCH772984 and GDC0994, and the MKK1/2 inhibitor, trametinib. These are compared with responses to the MKK1/2 inhibitor, selumetinib (AZD6244), previously measured by our lab in the same metastatic melanoma cell line. In three replicate experiments, we quantified a total of 12,805 class I phosphosites on 3,819 proteins in the trametinib-SCH772984-DMSO experiment, and 7,074 class I phosphosites on 2,453 in the GDC0994-SCH772984-DMSO experiment. This included 466 phosphosites that reproducibly decreased in response to at least one inhibitor in the trametinib-SCH772984-DMSO experiment and 414 phosphosites in the GDC0994-SCH772984-DMSO experiment. The results demonstrate linearity in signaling through the MAP kinase pathway. By comparing multiple inhibitors targeted to multiple tiers of protein kinases in the MAPK pathway, we gain insight into regulation and new targets of the oncogenic BRAF driver pathway in cancer cells, and a useful approach for evaluating the specificity of drugs and drug candidates. SILAC Experimental Design Experiment 1 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – Trametinib Replicate 2: Heavy – SCH772984, Medium – Trametinib, Light – DMSO Replicate 3: Heavy – Trametinib, Medium – DMSO, Light – SCH772984 SILAC Experimental Design Experiment 2 Replicate 1: Heavy – DMSO, Medium – SCH772984, Light – GDC0994 Replicate 2: Heavy – SCH772984, Medium – GDC0994, Light – DMSO Replicate 3: Heavy – GDC0994, Medium – DMSO, Light – SCH772984 File List 1. Zipped MaxQuant search results folder containing index and output folders for each raw file, ‘combined’ output folder, and mqpar.xml MaxQuant search parameters file 2. Individual raw files of phosphopeptide-enriched ERLIC fractions and total protein fractions 3. Zipped MaxQuant version used for analysis 4. FASTA file containing Uniprot human identifications 5. Instructions for viewing annotated spectra