Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1311601 | ENA |
REPOSITORIES: ENA
Similar Datasets
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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
,
Abstract:
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.
Note:
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"
from
"and(eq(stimulation_type, 2), eq(time, single_pulse_duration))" (for SBML-SAT)
to
"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.
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Project description:Large deletions in mitochondrial DNA (mtDNA) have been linked to a variety of clinical pathologies, including somatic emergence in congenital disorders such as Pearson Syndrome (MIM:557000), a mitochondrial disease characterized by sideroblastic anemia and exocrine pancreas dysfunction. Here, we develop a multi-omics approach to quantify mtDNA deletion heteroplasmy and cell state features in thousands of single cells. By profiling primary hematopoietic cells from three patients with Pearson Syndrome, we resolve the interdependence between pathogenic mtDNA heteroplasmy and cell lineage, including purifying selection against mtDNA deletions in effector-memory CD8 T-cell populations. We further observe widespread Pearson-specific transcriptomic changes in peripheral blood mononuclear cells. Additionally, single-cell analyses of in vivo and in vitro cultured bone marrow mononuclear cells reveal multi-faceted clonal dynamics and purifying selection in a patient with both Pearson Syndrome and Myelodysplastic Syndrome (MDS). Our results identify specific molecular perturbations underlying Pearson Syndrome and more generally provide a powerful framework to utilize multi-omics in the study of evolution in disease within single cells.
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