Laboratory of Cell Systems

Member

Professor Mariko OKADA
Assistant Professor Kazunari IWAMOTO
Shigeyuki MAGI

 

Correspondence

Tel +81-6-6879-8617
Fax +81-6-6879-8619
E-mail
URL http://csb.gsc.riken.jp/w/

Research

The aims of the laboratory are to define the general regulatory rules in signal transduction-transcriptional networks in cell determination processes and to apply this knowledge of regulatory principles to the understanding and treatment of human diseases. For this purpose, we perform quantitative measurements of the target biological system using various experimental methods and integrate these heterogeneous data by means of mathematical modeling and informatics. Using those systems biology approaches, we uncovered several unique properties in signal-transcription networks in immune cell development and cancer. We show that a positive cooperativity in the signaling network plays an important role to determine individual cell fate.

Current Research Programs

1. Cellular function of NF-kB oscillation

Intracellular signaling pathways are important to determine the activation of transcription factors, and their dynamics are often associated with determination of particular cellular phenotypes. NF-κB activity exhibits two characteristic features, oscillations and switch-like activation, regulated by negative and positive feedback, respectively. NF-κB shows cooperative switch-like activation dynamics in response to an increased dosage of external ligands, which is important for the threshold setting of cell activation (Shinohara et al. Science 2014). On the other hand, prolonged oscillatory nuclear translocation of NF-κB is thought to be important for induction of gene expression. However, functional roles of this oscillation are not clearly elucidated. We try to reveal the functions of NF-kB oscillation for gene expression using mathematical modeling and quantitative cell experiments.

fig1e.cellsystem

 

2. Feedback regulation of ErbB receptor signaling

The potency and duration of signaling responses are controlled by self-regulatory mechanisms. One of such examples is feedback regulation within the signaling network. We found that transcriptionally-induced PHLDA1 suppresses activation of ErbB receptors and downstream kinases after growth factor stimulation of MCF-7 cells and thereby acts as a negative feedback regulator. LC-MS analysis showed that PHLDA1 binds to ErbB3. Mathematical modeling followed by single molecule analysis of fluorescent-labeled ligand binding to the receptors suggested that PHLDA1 inhibits high-order oligomerization of ErbB receptors suggesting a novel inhibitory mechanism of the ErbB receptor signaling.

fig2e.cellsystem

3. Signal-dependent epigenetic regulation

The identification of the transcriptional networks initiated by the activation of signaling cascades is important to characterize the early phase of cell determination process. Our earlier studies based on mRNA expression profiles provided important insights on the downstream targets of signaling pathways, identifying the transcription factors that are activated and the corresponding target genes. Based on these studies, we currently focus early phase of epigenetic regulation, particularly enhancer regulation, controlled by upstream signaling pathways.

fig3e.cellsystem

References

  1. Current Status of Mathematical Modeling of Cancer – From the Viewpoint of Cancer Hallmarks. Magi S, et al. (2017) Current Opinion in Systems Biology. 2, 38-47.
  2.  Oscillation dynamics underlies functional switching of NF-κB for B cell activation.  Inoue K, et al. (2016) npj Systems Biology & Application 16024, doi:10.1038/npjsba.2016.24.
  3.  Mathematical modeling of atopic dermatitis reveals "double-switch" mechanisms underlying 4 common disease phenotypes.  Domínguez-HüttingerE, et al.  (2016) J. Allergy Clin. Immunol. pii: S0091-6749(16)31433-6.
  4.  Positive feedback within a kinase signaling complex functions as a switch mechanism for NF-κB activation. Shinohara H, et al. (2014) Science 344, 760-764.
  5.  Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Nakakuki T, et al. (2010) Cell 141, 884-896.
  6. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analysis. Birtwistle MR, et al. (2007) Mol. Syst. Biol. 3, 144.
  7. Quantitative transcriptional control of ErbB receptor signaling undergoes graded to biphasic response for cell differentiation. Nagashima T, et al. (2007) J. Biol. Chem. 282, 4045-4056.