Expression data from Arabidopsis thaliana (Col-0) exposed to Arsenic, Hypoxia and their combination
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ABSTRACT:
ORGANISM(S): Arabidopsis thaliana
PROVIDER: GSE119327 | GEO | 2018/09/01
REPOSITORIES: GEO
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Project description:In natural habitats, plants are often exposed to multiple stresses. Most studies, however, for plant abiotic stress responses analyzed those to individual stress but not combined stresses. In this report, we performed comparison analyses of gene expression to individual stresses, salt, osmotic and heat, and to a combination of these three stresses, which mimics arid conditions. We show here that the combined stress treatment induces unique gene expression pattern but not a simple reflection of the additive effects of individual stresses. First, the number of genes induced by combined stresses (150 mM NaCl, 200 mM mannitol and 35°C heat) was much smaller when compared to the sum of those induced by individual stress treatments, while the number of genes downregulated by multiple stresses was larger. A large number of genes induced by mannitol were not induced by multiple stresses, while those induced by salts were less affected in combined stress treatments. In addition, 125 genes, including13 for transcription factors, were found to be induced specifically by combined stress treatments. We report here that the plant response to a multi-stress environment represents an output of complex interactions between different stress aspects and signaling events of the various components of this environmental situation, and the determinative factor in this response is to avoid/minimize the antagonistic effects and to magnify the synergistic features of the imposed environmental challenges. Based on our results, we propose that genes that are highly induced by multiple treatments may be candidate for engineering stress tolerant crop plants. Wild-type plants of Arabidopsis thaliana were treated with three abiotic stresses, high salinity (150mM and 300mM), high osmotic pressure (200mM and 400mM mannitol) and heat (35°C), individually or simultaneously. Plants were treated with salt or mannitol for 16 hrs (first 1 hr and last 7 hrs are in light and other time in dark) or with heat for 4 hrs (in light) before sampling. There are three biologcal replicates.
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Project description:In natural habitats, plants are often exposed to multiple stresses. Most studies, however, for plant abiotic stress responses analyzed those to individual stress but not combined stresses. In this report, we performed comparison analyses of gene expression to individual stresses, salt, osmotic and heat, and to a combination of these three stresses, which mimics arid conditions. We show here that the combined stress treatment induces unique gene expression pattern but not a simple reflection of the additive effects of individual stresses. First, the number of genes induced by combined stresses (150 mM NaCl, 200 mM mannitol and 35°C heat) was much smaller when compared to the sum of those induced by individual stress treatments, while the number of genes downregulated by multiple stresses was larger. A large number of genes induced by mannitol were not induced by multiple stresses, while those induced by salts were less affected in combined stress treatments. In addition, 125 genes, including13 for transcription factors, were found to be induced specifically by combined stress treatments. We report here that the plant response to a multi-stress environment represents an output of complex interactions between different stress aspects and signaling events of the various components of this environmental situation, and the determinative factor in this response is to avoid/minimize the antagonistic effects and to magnify the synergistic features of the imposed environmental challenges. Based on our results, we propose that genes that are highly induced by multiple treatments may be candidate for engineering stress tolerant crop plants.
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Project description:This a model from the article:
Stress-specific response of the p53-Mdm2 feedback loop
Alexander Hunziker, Mogens H Jensen and Sandeep Krishna
BMC Systems Biology 2010, Jul 12;4(1):94
20624280,
Abstract:
ABSTRACT: BACKGROUND: The p53 signalling pathway has hundreds of inputs and outputs. It can trigger cellular senescence, cell-cycle arrest and apoptosis in response to diverse stress conditions, including DNA damage, hypoxia and nutrient deprivation. Signals from all these inputs are channeled through a single node, the transcription factor p53. Yet, the pathway is flexible enough to produce different downstream gene expression patterns in response to different stresses. RESULTS: We construct a mathematical model of the negative feedback loop involving p53 and its inhibitor, Mdm2, at the core of this pathway, and use it to examine the effect of different stresses that trigger p53. In response to DNA damage, hypoxia, etc., the model exhibits a wide variety of specific output behaviour -- steady states with low or high levels of p53 and Mdm2, as well as spiky oscillations with low or high average p53 levels. CONCLUSIONS: We show that even a simple negative feedback loop is capable of exhibiting the kind of flexible stress-specific response observed in the p53 system. Further, our model provides a framework for predicting the differences in p53 response to different stresses and single nucleotide polymorphisms.
The parameters of the model corresponds to the resting state, with delta = 11hr-1, gamma = 0.2hr-1, kt = 0.03nM-1hr-1 and kf = 5000nM-1hr-1.
To simulate different stress conditions as in figure 2A (also look at the curation figure of this model) of the reference publication, the above parameter should be changed. The parameter values corresponding to different stress conditions are shown in the following table.
Stress Condition/Parameter
delta
gamma
kt
kf
Nutlin
11hr-1
0.2hr-1
0.03nM-1hr-1
500nM-1hr-1
Oncogene
2hr-1
0.2hr-1
0.03nM-1hr-1
5000nM-1hr-1
DNA damage
2hr-1
0.5hr-1
0.03nM-1hr-1
2500nM-1hr-1
Hypoxia
2hr-1
0.2hr-1
0.01nM-1hr-1
5000nM-1hr-1
This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.For more information see the terms of use.To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
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