Taichiro Tomida

NFAT functions as a working memory of Ca2+ signals in decoding Ca2+ oscillation

Transcription by the nuclear factor of activated T cells (NFAT) is regulated by the frequency of Ca2+ oscillation. However, why and how Ca2+ oscillation regulates NFAT activity remain elusive. NFAT is dephosphorylated by Ca2+‐dependent phosphatase calcineurin and translocates from the cytoplasm to the nucleus to initiate transcription. We analyzed the kinetics of dephosphorylation and translocation of NFAT. We show that Ca2+‐dependent dephosphoryl ation proceeds rapidly, while the rephosphorylation and nuclear transport of NFAT proceed slowly. Therefore, after brief Ca2+ stimulation, dephosphoryl ated NFAT has a lifetime of several minutes in the cytoplasm. Thus, Ca2+ oscillation induces a build‐up of dephosphorylated NFAT in the cytoplasm, allowing effective nuclear translocation, provided that the oscillation interval is shorter than the lifetime of dephos phorylated NFAT. We also show that Ca2+ oscillation is more cost‐effective in inducing the translocation of NFAT than continuous Ca2+ signaling. Thus, the lifetime of dephosphorylated NFAT functions as a working memory of Ca2+ signals and enables the control of NFAT nuclear translocation by the frequency of Ca2+ oscillation at a reduced cost of Ca2+ signaling.

Transmembrane mucins Hkr1 and Msb2 are putative osmosensors in the SHO1 branch of yeast HOG pathway

To cope with life‐threatening high osmolarity, yeast activates the high‐osmolarity glycerol (HOG) signaling pathway, whose core element is the Hog1 MAP kinase cascade. Activated Hog1 regulates the cell cycle, protein translation, and gene expression. Upstream of the HOG pathway are functionally redundant SLN1 and SHO1 signaling branches. However, neither the osmosensor nor the signal generator of the SHO1 branch has been clearly defined. Here, we show that the mucin‐like transmembrane proteins Hkr1 and Msb2 are the potential osmosensors for the SHO1 branch. Hyperactive forms of Hkr1 and Msb2 can activate the HOG pathway only in the presence of Sho1, whereas a hyperactive Sho1 mutant activates the HOG pathway in the absence of both Hkr1 and Msb2, indicating that Hkr1 and Msb2 are the most upstream elements known so far in the SHO1 branch. Hkr1 and Msb2 individually form a complex with Sho1, and, upon high external osmolarity stress, appear to induce Sho1 to generate an intracellular signal. Furthermore, Msb2, but not Hkr1, can also generate an intracellular signal in a Sho1‐independent manner.

Adaptor functions of Cdc42, Ste50, and Sho1 in the yeast osmoregulatory HOG MAPK pathway

The yeast high osmolarity glycerol (HOG) signaling pathway can be activated by either of the two upstream pathways, termed the SHO1 and SLN1 branches. When stimulated by high osmolarity, the SHO1 branch activates an MAP kinase module composed of the Ste11 MAPKKK, the Pbs2 MAPKK, and the Hog1 MAPK. To investigate how osmostress activates this MAPK module, we isolated both gain‐of‐function and loss‐of‐function alleles in four key genes involved in the SHO1 branch, namely SHO1, CDC42, STE50, and STE11. These mutants were characterized using an HOG‐dependent reporter gene, 8xCRE‐lacZ. We found that Cdc42, in addition to binding and activating the PAK‐like kinases Ste20 and Cla4, binds to the Ste11–Ste50 complex to bring activated Ste20/Cla4 to their substrate Ste11. Activated Ste11 and its HOG pathway‐specific substrate, Pbs2, are brought together by Sho1; the Ste11–Ste50 complex binds to the cytoplasmic domain of Sho1, to which Pbs2 also binds. Thus, Cdc42, Ste50, and Sho1 act as adaptor proteins that control the flow of the osmostress signal from Ste20/Cla4 to Ste11, then to Pbs2.

Stimulus-Specific Distinctions in Spatial and Temporal Dynamics of Stress-Activated Protein Kinase Kinase Kinases Revealed by a Fluorescence Resonance Energy Transfer Biosensor

ABSTRACT The stress-activated protein kinases (SAPKs), namely, p38 and JNK, are members of the mitogen-activated protein kinase family and are important determinants of cell fate when cells are exposed to environmental stresses such as UV and osmostress. SAPKs are activated by SAPK kinases (SAP2Ks), which are in turn activated by various SAP2K kinases (SAP3Ks). Because conventional methods, such as immunoblotting using phospho-specific antibodies, measure the average activity of SAP3Ks in a cell population, the intracellular dynamics of SAP3K activity are largely unknown. Here, we developed a reporter of SAP3K activity toward the MKK6 SAP2K, based on fluorescence resonance energy transfer, that can uncover the dynamic behavior of SAP3K activation in cells. Using this reporter, we demonstrated that SAP3K activation occurs either synchronously or asynchronously among a cell population and in different cellular compartments in single cells, depending on the type of stress applied. In particular, SAP3Ks are activated by epidermal growth factor and osmostress on the plasma membrane, by anisomycin and UV in the cytoplasm, and by etoposide in the nucleus. These observations revealed previously unknown heterogeneity in SAPK responses and supplied answers to the question of the cellular location in which various stresses induce stimulus-specific SAPK responses.

The Temporal Pattern of Stimulation Determines the Extent and Duration of MAPK Activation in a Caenorhabditis elegans Sensory Neuron

Live-cell imaging in nematodes reveals the complex responses of a mitogen-activated protein kinase to neuronal stimulation. Live Dynamics of Cell Signaling Tomida et al. describe the development of a fluorescent sensor that reported the activity of a specific kinase, ERK, and its application to the analysis of cell signaling in neurons of living nematode worms. By expressing this ERK sensor in a specific neuron that responds to changes in extracellular salt concentration, they found that the activity of the kinase exhibited complex, nonlinear kinetics in response to pulsatile changes in NaCl. The nonlinear dynamics appeared to result from nonlinear calcium signals triggered by the different stimulation paradigms, and simulations were consistent with this hypothesis. Thus, tracking signaling in single cells of living organisms reveals the dynamic complexity of cellular responses. The Caenorhabditis elegans ASER sensory neuron is excited when environmental NaCl concentration is decreased. The mitogen-activated protein kinase (MAPK) MPK-1, a homolog of ERK (extracellular signal–regulated kinase), is activated during excitation of ASER sensory neurons. We created and expressed a fluorescence resonance energy transfer (FRET)–based MAPK activity probe in ASER neurons and then exposed the worms to various cyclic patterns of stimulation (changes in NaCl concentration) to monitor the dynamics of MPK-1 activity. We found that the intensity and duration of MPK-1 activity were determined by the temporal pattern of stimulation, namely, a combination of stimulation period length, stimulation duration, and time between stimuli. The complex, nonlinear relationship between stimulation and MPK-1 activation was explained by the properties of intracellular calcium responses upstream of MPK-1. Thus, we visualized the dynamics of MAPK activation in a sensory neuron in living nematodes in response to complex stimuli and present a reporter that can be used in higher eukaryotes to test in silico predictions regarding the MAPK pathway.

Intracellular delivery of glutathione S-transferase into mammalian cells

Protein transduction domains (PTDs) derived from human immunodeficiency virus Tat protein and herpes simplex virus VP22 protein are useful for the delivery of non-membrane-permeating polar or large molecules into living cells. In the course of our study aiming at evaluating PTD, we unexpectedly found that the fluorescent-dye-labeled glutathione S-transferase (GST) from Schistosoma japonicum without known PTDs was delivered into COS7 cells. The intracellular transduction of GST was also observed in HeLa, NIH3T3, and PC12 cells, as well as in hippocampal primary neurons, indicating that a wide range of cell types is permissive for GST transduction. Furthermore, we showed that the immunosuppressive peptide VIVIT fused with GST successfully inhibits NFAT activation. These results suggest that GST is a novel PTD which may be useful in the intracellular delivery of biologically active molecules, such as small-molecule drugs, bioactive peptides, or proteins.

Oscillation of p38 activity controls efficient pro-inflammatory gene expression.

The p38 MAP kinase signalling pathway controls inflammatory responses and is an important target of anti-inflammatory drugs. Although pro-inflammatory cytokines such as interleukin-1β (IL-1β) appear to induce only transient activation of p38 (over ∼60 min), longer cytokine exposure is necessary to induce p38-dependent effector genes. Here we study the dynamics of p38 activation in individual cells using a Förster resonance energy transfer (FRET)-based p38 activity reporter. We find that, after an initial burst of activity, p38 MAPK activity subsequently oscillates for more than 8 h under continuous IL-1β stimulation. However, as this oscillation is asynchronous, the measured p38 activity population average is only slightly higher than basal level. Mathematical modelling, which we have experimentally verified, indicates that the asynchronous oscillation of p38 is generated through a negative feedback loop involving the dual-specificity phosphatase MKP-1/DUSP1. We find that the oscillatory p38 activity is necessary for efficient expression of pro-inflammatory genes such as IL-6, IL-8 and COX-2.

Visualization of the spatial and temporal dynamics of MAPK signaling using fluorescence imaging techniques.

Conserved mitogen-activated protein kinase (MAPK) signaling pathways are major mechanisms through which cells perceive and respond properly to their surrounding environment. Such homeostatic responses maintain the life of the organism. Since errors in MAPK signaling pathways can lead to cancers and to defects in immune responses, in the nervous system and metabolism, these pathways have been extensively studied as potential therapeutic targets. Although much has been studied about the roles of MAPKs in various cellular functions, less is known regarding regulation of MAPK in living organisms. This review will focus on the latest understanding of the dynamic regulation of MAPK signaling in intact cells that was revealed by using novel fluorescence imaging techniques and advanced systems-analytical methods. These techniques allowed quantitative analyses of signal transduction in situ with high spatio-temporal resolution and have revealed the nature of the molecular dynamics that determine cellular responses and fates.

Spatio-temporal Dynamics and Mechanisms of Stress Granule Assembly

Stress granules (SGs) are non-membranous cytoplasmic aggregates of mRNAs and related proteins, assembled in response to environmental stresses such as heat shock, hypoxia, endoplasmic reticulum (ER) stress, chemicals (e.g. arsenite), and viral infections. SGs are hypothesized as a loci of mRNA triage and/or maintenance of proper translation capacity ratio to the pool of mRNAs. In brain ischemia, hippocampal CA3 neurons, which are resilient to ischemia, assemble SGs. In contrast, CA1 neurons, which are vulnerable to ischemia, do not assemble SGs. These results suggest a critical role SG plays in regards to cell fate decisions. Thus SG assembly along with its dynamics should determine the cell fate. However, the process that exactly determines the SG assembly dynamics is largely unknown. In this paper, analyses of experimental data and computer simulations were used to approach this problem. SGs were assembled as a result of applying arsenite to HeLa cells. The number of SGs increased after a short latent period, reached a maximum, then decreased during the application of arsenite. At the same time, the size of SGs grew larger and became localized at the perinuclear region. A minimal mathematical model was constructed, and stochastic simulations were run to test the modeling. Since SGs are discrete entities as there are only several tens of them in a cell, commonly used deterministic simulations could not be employed. The stochastic simulations replicated observed dynamics of SG assembly. In addition, these stochastic simulations predicted a gamma distribution relative to the size of SGs. This same distribution was also found in our experimental data suggesting the existence of multiple fusion steps in the SG assembly. Furthermore, we found that the initial steps in the SG assembly process and microtubules were critical to the dynamics. Thus our experiments and stochastic simulations presented a possible mechanism regulating SG assembly.

Stochastic Simulation of Stress Granules

Cells form stress granules (SGs) in response to various forms of environmental stress, including heat shock, radiation, low oxygen pressure, chemicals such as arsenite, and viral infections. SGs are nonmembranous aggregates composed of mRNAs and their binding proteins. The role of SGs is hypothesized as loci for the storage and/or sorting mRNAs leading to reuse or degradation. The number of SGs in a cell as well as their locations and temporal speed of formation are critical elements in determining cell fate. Although extensive studies have been carried out to examine the dynamics of SG formation, factors controlling the dynamics are still largely unknown. We approached this problem by utilizing computer simulation. However, the commonly used simulation method, deterministic simulation (DS), cannot be applied to SGs. DS is valid in the case of large numbers of molecules of the order of Avogadro’s number, as DS employs concentration to express the abundance of molecules. Because only several tens of SGs are present in a single cell, their abundance is preferably expressed in terms of their number. Thus, we applied the method of stochastic simulation (SS). Among various methodologies of SS, we employed our own approach, whereby the coordinates and state of single molecules, which indicate whether the molecules are bound to other molecules forming a complex or unbounded as an original species, are controlled. In this chapter, we introduce our SS method and its application to SG formation. The simulation results agreed well with experimental observations and presented possible mechanisms controlling SG formation.