The Experts below are selected from a list of 40743 Experts worldwide ranked by ideXlab platform
Kazuko Yamaguchishinozaki - One of the best experts on this subject based on the ideXlab platform.
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drought stress responses and resistance in plants from cellular responses to long distance intercellular communication
Frontiers in Plant Science, 2020Co-Authors: Fuminori Takahashi, Kazuko Yamaguchishinozaki, Takashi Kuromori, Kaoru Urano, Kazuo ShinozakiAbstract:The drought stress responses of vascular plants are complex regulatory mechanisms because they include various physiological responses from Signal Perception under water deficit conditions to the acquisition of drought stress resistance at the whole-plant level. It is thought that plants first recognize water deficit conditions in roots and that several molecular Signals then move from roots to shoots. Finally, a phytohormone, abscisic acid (ABA) is synthesized mainly in leaves. However, the detailed molecular mechanisms of stress sensors and the regulators that initiate ABA biosynthesis in response to drought stress conditions are still unclear. Another important issue is how plants adjust ABA propagation, stress-mediated gene expression and metabolite composition to acquire drought stress resistance in different tissues throughout the whole plant. In this review, we summarize recent advances in research on drought stress responses, focusing on long-distance Signaling from roots to shoots, ABA synthesis and transport, and metabolic regulation in both cellular and whole-plant levels of Arabidopsis and crops. We also discuss coordinated mechanisms for acquiring drought stress adaptations and resistance via tissue-to-tissue communication and long-distance Signaling.
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aba dependent and aba independent Signaling in response to osmotic stress in plants
Current Opinion in Plant Biology, 2014Co-Authors: Takuya Yoshida, Junro Mogami, Kazuko YamaguchishinozakiAbstract:Plants have adaptive robustness to osmotic stresses such as drought and high salinity. Numerous genes functioning in stress response and tolerance are induced under osmotic conditions in diverse plants. Various Signaling proteins, such as transcription factors, protein kinases and phosphatases, play Signal transduction roles during plant adaptation to osmotic stress, with involvement ranging from stress Signal Perception to stress-responsive gene expression. Recent progress has been made in analyzing the complex cascades of gene expression during osmotic stress response, and especially in identifying specificity and crosstalk in abscisic acid (ABA)-dependent and ABA-independent Signaling pathways. In this review, we highlight transcriptional regulation of gene expression governed by two key transcription factors: AREB/ABFs and DREB2A operating respectively in ABA-dependent and ABA-independent Signaling pathways.
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plant gene networks in osmotic stress response from genes to regulatory networks
Methods in Enzymology, 2007Co-Authors: Lamson Phan Tran, Kazuo Nakashima, Kazuo Shinozaki, Kazuko YamaguchishinozakiAbstract:Abstract Because of their sessile nature, plants grown in a dynamic climate have evolved a range of adaptations that enable them to survive in various environmental stress conditions during growth and development. Plants respond to environmental stresses at both cellular and molecular levels by altering the expression of many genes via a complexity of Signaling pathways. These pathways begin with Signal Perception and end with the expression of stress‐responsive target genes. Ultimately, the selective upregulation of target genes leads to the alteration of physiological response so as to confer tolerance of the stress. In the Signal transduction network, various regulatory and functional proteins function collectively to ensure survival of the plants. This chapter summarizes the methodology used to dissect gene regulatory networks involved in the response to osmotic stresses, such as drought and high salinity.
Peter H Quail - One of the best experts on this subject based on the ideXlab platform.
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residues clustered in the light sensing knot of phytochrome b are necessary for conformer specific binding to Signaling partner pif3
PLOS Genetics, 2009Co-Authors: Elise A Kikis, Peter H Quail, Yoshito Oka, Matthew E HudsonAbstract:The bHLH transcription factor, PHYTOCHROME INTERACTING FACTOR 3 (PIF3), interacts specifically with the photoactivated, Pfr, form of Arabidopsis phytochrome B (phyB). This interaction induces PIF3 phosphorylation and degradation in vivo and modulates phyB-mediated seedling deetiolation in response to red light. To identify missense mutations in the phyB N-terminal domain that disrupt this interaction, we developed a yeast reverse-hybrid screen. Fifteen individual mutations identified in this screen, or in previous genetic screens for Arabidopsis mutants showing reduced sensitivity to red light, were shown to also disrupt light-induced binding of phyB to PIF3 in in vitro co-immunoprecipitation assays. These phyB missense mutants fall into two general classes: Class I (eleven mutants) containing those defective in light Signal Perception, due to aberrant chromophore attachment or photoconversion, and Class II (four mutants) containing those normal in Signal Perception, but defective in the capacity to transduce this Signal to PIF3. By generating a homology model for the three-dimensional structure of the Arabidopsis phyB chromophore-binding region, based on the crystal structure of Deinococcus radiodurans phytochrome, we predict that three of the four Class II mutated phyB residues are solvent exposed in a cleft between the presumptive PAS and GAF domains. This deduction suggests that these residues could be directly required for the physical interaction of phyB with PIF3. Because these three residues are also necessary for phyB-imposed inhibition of hypocotyl elongation in response to red light, they are functionally necessary for Signal transfer from photoactivated phyB, not only to PIF3 and other related bHLH transcription factors tested here, but also to other downstream Signaling components involved in regulating seedling deetiolation.
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mutant screen distinguishes between residues necessary for light Signal Perception and Signal transfer by phytochrome b
PLOS Genetics, 2008Co-Authors: Yoshito Oka, Peter H Quail, Tomonao Matsushita, Nobuyoshi Mochizuki, Akira NagataniAbstract:The phytochromes (phyA to phyE) are a major plant photoreceptor family that regulate a diversity of developmental processes in response to light. The N-terminal 651–amino acid domain of phyB (N651), which binds an open tetrapyrrole chromophore, acts to perceive and transduce regulatory light Signals in the cell nucleus. The N651 domain comprises several subdomains: the N-terminal extension, the Per/Arnt/Sim (PAS)-like subdomain (PLD), the cGMP phosphodiesterase/adenyl cyclase/FhlA (GAF) subdomain, and the phytochrome (PHY) subdomain. To define functional roles for these subdomains, we mutagenized an Arabidopsis thaliana line expressing N651 fused in tandem to green fluorescent protein, β-glucuronidase, and a nuclear localization Signal. A large-scale screen for long hypocotyl mutants identified 14 novel intragenic missense mutations in the N651 moiety. These new mutations, along with eight previously identified mutations, were distributed throughout N651, indicating that each subdomain has an important function. In vitro analysis of the spectral properties of these mutants enabled them to be classified into two principal classes: light-Signal Perception mutants (those with defective spectral activity), and Signaling mutants (those normal in light Perception but defective in intracellular Signal transfer). Most spectral mutants were found in the GAF and PHY subdomains. On the other hand, the Signaling mutants tend to be located in the N-terminal extension and PLD. These observations indicate that the N-terminal extension and PLD are mainly involved in Signal transfer, but that the C-terminal GAF and PHY subdomains are responsible for light Perception. Among the Signaling mutants, R110Q, G111D, G112D, and R325K were particularly interesting. Alignment with the recently described three-dimensional structure of the PAS-GAF domain of a bacterial phytochrome suggests that these four mutations reside in the vicinity of the phytochrome light-sensing knot.
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two small spatially distinct regions of phytochrome b are required for efficient Signaling rates
The Plant Cell, 1996Co-Authors: Doris Wagner, Michael Koloszvari, Peter H QuailAbstract:We used a series of in vitro-generated deletion and amino acid substitution derivatives of phytochrome B (phyB) expressed in transgenic Arabidopsis to identify regions of the molecule important for biological activity. Expression of the chromophore-bearing N-terminal domain of phyB alone resulted in a fully photoactive, monomeric molecule lacking normal regulatory activity. Expression of the C-terminal domain alone resulted in a photoinactive, dimeric molecule, also lacking normal activity. Thus, both domains are necessary, but neither is sufficient for phyB activity. Deletion of a small region on each major domain (residues 6 to 57 and 652 to 712, respectively) was shown to compromise phyB activity differentially without interfering with spectral activity or dimerization. Deletion of residues 6 to 57 caused a large increase in the fluence rate of continuous red light (Rc) required for maximal seedling responsiveness, indicating a marked decrease in efficiency of light Signal Perception or processing per mole of mutant phyB. In contrast, deletion of residues 652 to 712 resulted in a photoreceptor that retained saturation of seedling responsiveness to Rc at low fluence rates but at a response level much below the maximal response elicited by the parent molecule. This deletion apparently reduces the maximal biological activity per mole of phyB without a major decrease in efficiency of Signal Perception, thus suggesting disruption of a process downstream of Signal Perception. In addition, certain phyB constructs caused dominant negative interference with endogenous phyA activity in continuous far-red light, suggesting that the two photoreceptors may share reaction partners.
Lai-sang Young - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Signal tracking in a simple v1 spiking model
Neural Computation, 2016Co-Authors: Guillaume Lajoie, Lai-sang YoungAbstract:This work is part of an effort to understand the neural basis for our visual system's ability, or failure, to accurately track moving visual Signals. We consider here a ring model of spiking neurons, intended as a simplified computational model of a single hypercolumn of the primary visual cortex of primates. Signals that consist of edges with time-varying orientations localized in space are considered. Our model is calibrated to produce spontaneous and driven firing rates roughly consistent with experiments, and our two main findings, for which we offer dynamical explanation on the level of neuronal interactions, are the following. First, we have documented consistent transient overshoots in Signal Perception following Signal switches due to emergent interactions of the E-and I-populations. Second, for continuously moving Signals, we have found that accuracy is considerably lower at reversals of orientation than when continuing in the same direction as when the Signal is a rotating bar. To measure performance, we use two metrics, called fidelity and reliability, to compare Signals reconstructed by the system to the ones presented and assess trial-to-trial variability. We propose that the same population mechanisms responsible for orientation selectivity also impose constraints on dynamic Signal tracking that manifest in Perception failures consistent with psychophysical observations.
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Dynamic Signal tracking in a simple V1 spiking model
arXiv: Neurons and Cognition, 2016Co-Authors: Guillaume Lajoie, Lai-sang YoungAbstract:This work is part of an effort to understand the neural basis for our visual system's ability, or failure, to accurately track moving visual Signals. We consider here a ring model of spiking neurons, intended as a simplified computational model of a single hypercolumn of the primary visual cortex. Signals that consist of edges with time-varying orientations localized in space are considered. Our model is calibrated to produce spontaneous and driven firing rates roughly consistent with experiments, and our two main findings, for which we offer dynamical explanation on the level of neuronal interactions, are the following: (1) We have documented consistent transient overshoots in Signal Perception following Signal switches due to emergent interactions of the E- and I-populations, and (2) for continuously moving Signals, we have found that accuracy is considerably lower at reversals of orientation than when continuing in the same direction (as when the Signal is a rotating bar). To measure performance, we use two metrics, called fidelity and reliability, to compare Signals reconstructed by the system to the ones presented, and to assess trial-to-trial variability. We propose that the same population mechanisms responsible for orientation selectivity also impose constraints on dynamic Signal tracking that manifest in Perception failures consistent with psychophysical observations.
Andre W Visser - One of the best experts on this subject based on the ideXlab platform.
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hydrodynamic Signal Perception in the copepod acartia tonsa
Marine Ecology Progress Series, 1999Co-Authors: Thomas Kiørboe, Enric Saiz, Andre W VisserAbstract:Copepods may remotely detect predators from the velocity gradients these generate in the ambient water. Each of the different components and characteristics of a velocity gradient (acceleration, vorticity, longitudinal and shear deformation) can cause a velocity difference between the copepod and the ambient water and may, therefore, be perceived by mechanoreceptory setae. We hypothesised that the threshold value for escape response to a particular component depends solely on the magnitude of the velocity difference (= Signal strength) it generates. In experiments we isolated the different components and noted the minimum intensities to which the copepod Acartia tonsa responded. As hypothesised, threshold Signal strengths due to longitudinal and shear deformation were similar, ∼0.015 cm s -1 , and were invariant with developmental stage. The latter implies that the threshold deformation rate for response scales inversely with size, i.e. that large stages respond to lower fluid deformation rates than small stages and, hence, may detect predators at longer distances. Signals due to vorticity and acceleration did not elicit escape responses, even though their magnitude exceeded threshold Signal strength due to deformation. We suggest that A. tonsa cannot distinguish such Signals from those due to their own behaviour (sinking, swimming, passive reorientation due to gravity) because they cause a similar spatial distributions of the Signal across the body. Reinterpretation of data from the literature revealed that threshold Signal strength due to deformation varies by ca 2 orders of magnitude between copepods and exceeds the neurophysiological response threshold by more than a factor of 10. In contrast, threshold deformation rates vary much less, ∼ 0.5 to 5 s -1 . Model calculations suggest that such threshold deformation rates are just sufficient to allow efficient predator detection while at the same time just below maximum turbulent deformation rates, thus preventing inordinate escapes.
Guillaume Lajoie - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Signal tracking in a simple v1 spiking model
Neural Computation, 2016Co-Authors: Guillaume Lajoie, Lai-sang YoungAbstract:This work is part of an effort to understand the neural basis for our visual system's ability, or failure, to accurately track moving visual Signals. We consider here a ring model of spiking neurons, intended as a simplified computational model of a single hypercolumn of the primary visual cortex of primates. Signals that consist of edges with time-varying orientations localized in space are considered. Our model is calibrated to produce spontaneous and driven firing rates roughly consistent with experiments, and our two main findings, for which we offer dynamical explanation on the level of neuronal interactions, are the following. First, we have documented consistent transient overshoots in Signal Perception following Signal switches due to emergent interactions of the E-and I-populations. Second, for continuously moving Signals, we have found that accuracy is considerably lower at reversals of orientation than when continuing in the same direction as when the Signal is a rotating bar. To measure performance, we use two metrics, called fidelity and reliability, to compare Signals reconstructed by the system to the ones presented and assess trial-to-trial variability. We propose that the same population mechanisms responsible for orientation selectivity also impose constraints on dynamic Signal tracking that manifest in Perception failures consistent with psychophysical observations.
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Dynamic Signal tracking in a simple V1 spiking model
arXiv: Neurons and Cognition, 2016Co-Authors: Guillaume Lajoie, Lai-sang YoungAbstract:This work is part of an effort to understand the neural basis for our visual system's ability, or failure, to accurately track moving visual Signals. We consider here a ring model of spiking neurons, intended as a simplified computational model of a single hypercolumn of the primary visual cortex. Signals that consist of edges with time-varying orientations localized in space are considered. Our model is calibrated to produce spontaneous and driven firing rates roughly consistent with experiments, and our two main findings, for which we offer dynamical explanation on the level of neuronal interactions, are the following: (1) We have documented consistent transient overshoots in Signal Perception following Signal switches due to emergent interactions of the E- and I-populations, and (2) for continuously moving Signals, we have found that accuracy is considerably lower at reversals of orientation than when continuing in the same direction (as when the Signal is a rotating bar). To measure performance, we use two metrics, called fidelity and reliability, to compare Signals reconstructed by the system to the ones presented, and to assess trial-to-trial variability. We propose that the same population mechanisms responsible for orientation selectivity also impose constraints on dynamic Signal tracking that manifest in Perception failures consistent with psychophysical observations.
