Mus musculus
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE7615
PROVIDER: PRJNA105535 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Atm-/-,mTerc-/-,p53-/- triple knock-out lymphoma vs normal mouse DNA (GPL4092)
| PRJNA105521 | ENA
Project description:Atm-/-,mTerc-/-,p53-/- triple knock-out lymphoma vs normal mouse DNA (GPL2884)
| PRJNA105525 | ENA
Project description:Highly rearranged and mutated cancer genomes present major challenges in the identification of pathogenetic events driving the cancer process. Here, we engineered lymphoma-prone mice with chromosomal instability to assess the utility of mouse models in cancer gene discovery and the extent of cross-species overlap in cancer-associated copy number aberrations. Integrating with targeted re-sequencing, our comparative oncogenomic studies efficiently identified FBXW7 and PTEN as commonly deleted or mutated tumor suppressors in human T-cell acute lymphoblastic leukemia/lymphoma (T-ALL). More generally, the murine cancers acquire widespread recurrent clonal amplifications and deletions targeting loci syntenic to alterations present in not only human T-ALL but also diverse tumors of hematopoietic, mesenchymal and epithelial types. These results thus support the view that murine and human tumors experience common biological processes driven by orthologous genetic events as they evolve towards a malignant phenotype. The highly concordant nature of genomic events encourages the use of genome unstable murine cancer models in the discovery of biologically relevant driver events in human cancer. Experiment Overall Design: 18 lymphoma samples from Atm-/-, mTerc-/-, p53-/- triple knock-out mice were analyzed. 3 week old hymus RNA from healthy mice of p53 Hets was used as reference. Each sample was hybridized with dye-swap replica.
2008-06-15 | E-GEOD-7607 | biostudies-arrayexpress
Project description:Highly rearranged and mutated cancer genomes present major challenges in the identification of pathogenetic events driving the cancer process. Here, we engineered lymphoma-prone mice with chromosomal instability to assess the utility of mouse models in cancer gene discovery and the extent of cross-species overlap in cancer-associated copy number aberrations. Integrating with targeted re-sequencing, our comparative oncogenomic studies efficiently identified FBXW7 and PTEN as commonly deleted or mutated tumor suppressors in human T-cell acute lymphoblastic leukemia/lymphoma (T-ALL). More generally, the murine cancers acquire widespread recurrent clonal amplifications and deletions targeting loci syntenic to alterations present in not only human T-ALL but also diverse tumors of hematopoietic, mesenchymal and epithelial types. These results thus support the view that murine and human tumors experience common biological processes driven by orthologous genetic events as they evolve towards a malignant phenotype. The highly concordant nature of genomic events encourages the use of genome unstable murine cancer models in the discovery of biologically relevant driver events in human cancer. Experiment Overall Design: 22 lymphoma samples from Atm-/-, mTerc-/-, p53-/- triple knock-out mice were analyzed. DNA from healthy mice of the same line was used as reference. Each sample was hybridized with dye-swap replica.
2008-06-15 | E-GEOD-7600 | biostudies-arrayexpress
Project description:Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATM
This model is described in the article:
Explaining oscillations and
variability in the p53-Mdm2 system.
Proctor CJ, Gray DA.
BMC Syst Biol 2008; 2: 75
Abstract:
BACKGROUND: In individual living cells p53 has been found to
be expressed in a series of discrete pulses after DNA damage.
Its negative regulator Mdm2 also demonstrates oscillatory
behaviour. Attempts have been made recently to explain this
behaviour by mathematical models but these have not addressed
explicit molecular mechanisms. We describe two stochastic
mechanistic models of the p53/Mdm2 circuit and show that
sustained oscillations result directly from the key biological
features, without assuming complicated mathematical functions
or requiring more than one feedback loop. Each model examines a
different mechanism for providing a negative feedback loop
which results in p53 activation after DNA damage. The first
model (ARF model) looks at the mechanism of p14ARF which
sequesters Mdm2 and leads to stabilisation of p53. The second
model (ATM model) examines the mechanism of ATM activation
which leads to phosphorylation of both p53 and Mdm2 and
increased degradation of Mdm2, which again results in p53
stabilisation. The models can readily be modified as further
information becomes available, and linked to other models of
cellular ageing. RESULTS: The ARF model is robust to changes in
its parameters and predicts undamped oscillations after DNA
damage so long as the signal persists. It also predicts that if
there is a gradual accumulation of DNA damage, such as may
occur in ageing, oscillations break out once a threshold level
of damage is acquired. The ATM model requires an additional
step for p53 synthesis for sustained oscillations to develop.
The ATM model shows much more variability in the oscillatory
behaviour and this variability is observed over a wide range of
parameter values. This may account for the large variability
seen in the experimental data which so far has examined ARF
negative cells. CONCLUSION: The models predict more regular
oscillations if ARF is present and suggest the need for further
experiments in ARF positive cells to test these predictions.
Our work illustrates the importance of systems biology
approaches to understanding the complex role of p53 in both
ageing and cancer.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000188.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2008-09-01 | BIOMD0000000188 | BioModels
Project description:Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by p14ARF
This model is described in the article:
Explaining oscillations and
variability in the p53-Mdm2 system.
Proctor CJ, Gray DA.
BMC Syst Biol 2008; 2: 75
Abstract:
BACKGROUND: In individual living cells p53 has been found to
be expressed in a series of discrete pulses after DNA damage.
Its negative regulator Mdm2 also demonstrates oscillatory
behaviour. Attempts have been made recently to explain this
behaviour by mathematical models but these have not addressed
explicit molecular mechanisms. We describe two stochastic
mechanistic models of the p53/Mdm2 circuit and show that
sustained oscillations result directly from the key biological
features, without assuming complicated mathematical functions
or requiring more than one feedback loop. Each model examines a
different mechanism for providing a negative feedback loop
which results in p53 activation after DNA damage. The first
model (ARF model) looks at the mechanism of p14ARF which
sequesters Mdm2 and leads to stabilisation of p53. The second
model (ATM model) examines the mechanism of ATM activation
which leads to phosphorylation of both p53 and Mdm2 and
increased degradation of Mdm2, which again results in p53
stabilisation. The models can readily be modified as further
information becomes available, and linked to other models of
cellular ageing. RESULTS: The ARF model is robust to changes in
its parameters and predicts undamped oscillations after DNA
damage so long as the signal persists. It also predicts that if
there is a gradual accumulation of DNA damage, such as may
occur in ageing, oscillations break out once a threshold level
of damage is acquired. The ATM model requires an additional
step for p53 synthesis for sustained oscillations to develop.
The ATM model shows much more variability in the oscillatory
behaviour and this variability is observed over a wide range of
parameter values. This may account for the large variability
seen in the experimental data which so far has examined ARF
negative cells. CONCLUSION: The models predict more regular
oscillations if ARF is present and suggest the need for further
experiments in ARF positive cells to test these predictions.
Our work illustrates the importance of systems biology
approaches to understanding the complex role of p53 in both
ageing and cancer.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000188.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2008-09-05 | BIOMD0000000189 | BioModels
Project description:Recent observations show that the single-cell response of p53 to ionizing radiation (IR) is “digital” in that it is the number of oscillations rather than the amplitude of p53 that shows dependence on the radiation dose. We present a model of this phenomenon. In our model, double-strand break (DSB) sites induced by IR interact with a limiting pool of DNA repair proteins, forming DSB–protein complexes at DNA damage foci. The persisting complexes are sensed by ataxia telangiectasia mutated (ATM), a protein kinase that activates p53 once it is phosphorylated by DNA damage. The ATM-sensing module switches on or off the downstream p53 oscillator, consisting of a feedback loop formed by p53 and its negative regulator, Mdm2. In agreement with experiments, our simulations show that by assuming stochasticity in the initial number of DSBs and the DNA repair process, p53 and Mdm2 exhibit a coordinated oscillatory dynamics upon IR stimulation in single cells, with a stochastic number of oscillations whose mean increases with IR dose. The damped oscillations previously observed in cell populations can be explained as the aggregate behavior of single cell
2020-05-13 | MODEL2005130001 | BioModels
Project description:Mta1 gene expression reveals new targets and functions. Mta1 functions in p53 dependent and independent manner. Genes regulated by Mta1 in the presence and absence of p53 were indetified This expression data contains 5 different samples (MEFs) 1.wild type 2. Mta1 knockout 3. Mta1 re-expression in the knock out MEFs 4. P53 knockout and 5. Mta1 over expression in P53 knock out MEFs. Various sample comparisons were done and genes with p-value< 0.05 and fold change M-bM-^IM-% 2.0 were considered statistically significant 5 samples (triplicates of each, total 15) were analyzed. We generated pairwise comparisons between the WT vs Mta1-KO; Mta1-KO vs Mta1-KO/Mta1; P53-KO vs P53-KO/Mta1.
2013-08-30 | E-GEOD-25072 | biostudies-arrayexpress
Project description:Integrated-systems model of oxidative stress connecting NRF2 and p53 signaling pathways. Additional crosstalk linking oxidative stress to p53 inhibition, p53 to NRF2 through p21, and NRF2 to MDM2 was incorporated in this model. The NRF2 pathway was encoded as first- and second-order rate equations for KEAP1 oxidation and NRF2 stabilization; NRF2-mediated transcription of antioxidant enzymes was modeled as a Hill function. The p53 pathway was reconstructed from a delay differential equation model of p53 signaling in response to DNA damage. To adapt the p53 DNA-damage model to respond to oxidative stress, we used a first-order oxidation reaction of ATM/CHEK2 by intracellular H2O2.
The integrated base model of NRF2–p53 oxidative-stress signaling contains 42 reactions and 22 ordinary differential equations (ODEs).
2021-10-29 | MODEL2110270001 | BioModels