Research

Ultimate goal of our study is understanding of mechanisms of signal transduction networks that regulate various cellular functions including metabolic homeostasis, cell-fate determination, and synaptic plasticity at systems level. In these biological processes, the same input stimulation elicits distinct outcomes depending temporal patterns of input, which we denote “Temporal Coding”, and we are interested in quantitative mechanisms of the encoding/decoding systems via signaling networks that underlie these processing.
The life system is a “Trans-omic” network, consisting of interactions between numerous molecules across multi-omic layers, such as genome, transcriptome, proteome, and metabolome. Lifestyle diseases can be regarded as complex multifactorial diseases caused by breakdowns in a trans-omic network rather than breakdown of a single molecule. In spite of recent advances in measurement and analysis methods for single-omic layers, trans-omic analyses integrating multi-omic data have still been in a state of the art and under development. We have recently developed a construction method of trans-omic network by integrating phosphoproteomic, transcriptomic, and metabolomic data. We constructed a trans-omic regulatory network to explore how cells interpret a physiologically dynamic stimulus, insulin. Integrating different layers based on direct biochemical reactions (trans-omics) rather than statistical indirect relationship (multi-omics) allows us to identify the large-scale network of insulin action from insulin signaling layer to downstream layers including gene expression and cellular functions such as metabolism.
We use both experimental and computational approaches. Thus, we are trying to understand cellular processes in terms of Systems Biology.

1. Trans-omics of glucose metabolism
2. Temporal coding of insulin action
3. Information theory of cellular signaling
4. Synaptic plasticity

See also our movie of introduction of Kuroda Lab!

 

1. Trans-omics of glucose metabolism

生Insulin action involves dynamic molecular interactions between multiple layers including protein phosphorylation, and metabolites. We performed metabolomic and phospho-proteomic analysis in insulin-stimulated Fao hepatoma cells, and automatically reconstructed global molecular network of insulin action by use of trans “omics” data together with several databases. We found a landscape of global network of insulin-dependent metabolic control that involves 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites (Yugi et al, Cell Rep. 2014, Yugi et al, Trends. Biotech., 2016)(Fig. 1). We also performed the transcriptomic analysis of insulin-dependent gene expression and found that selective regulation of up-regulated and down-regulated genes by temporal patterns and doses of insulin (Sano et al, Sci. Signal, 2016).

Fig. 1

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2. Temporal coding of insulin action

Hormones and growth factors use a different combination of signaling pathways to exert specific functions depending on the temporal patterns. However, how a hormone selectively regulates multiple downstream functions through a common signaling pathway remains to be clarified, and the physiological role of the selective regulation has yet to be demonstrated.
Blood insulin, a hormone that regulates various metabolic processes, such as glycogenesis, gluconeogenesis, protein synthesis, and lipid synthesis, and reportedly exhibits several temporal patterns, including additional secretion, which is observed in response to meals, basal secretion, which is characterized by persistently low circulating insulin concentrations, and small-amplitude oscillations recurring approximately every 10 to 15 minutes.
We found that the insulin-dependent AKT pathway uses temporal patterns multiplexing for selective regulation of downstream molecules such as S6K, GSK3β and G6Pase, by use of different network structures and kinetics. (Kubota et al., Mol. Cell, 2012) (Fig. 2). We also found that glucose metabolisms including glycolysis, gluconeogenesis and glycogenesis are also selectively regulated by through temporal change and absolute concentration of insulin (Noguchi et al., Mol. Sys. Biol., 2013).

Fig 2.
Temporal coding of insulin action
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3. Information theory of cellular signaling

Signal transduction networks including ERK elicit multiple cellular functions. One of the critical properties of the signal transduction system is that the same signaling networks can code multiple cellular functions. This coding system can be generated by the combination of distinct signaling molecules/networks and by distinct temporal coding systems.
In PC12 cells, EGF and NGF induce transient and sustained ERK activation, leading to cell proliferation and differentiation, respectively. We found that transient ERK activation encode information of increasing rate of growth factors and sustained ERK activation encode that of final concentration of growth factors (Sasagawa et al., Nat. Cell Biol., 2005). We developed quantitative image cytometry (QIC), which allows us to measure signaling activities at single cell level with one minute-interval (Ozaki et al., PLoS ONE, 2010), and made a system identification of temporal decoding of ERK activation by selective immediate early genes (IEGs) expression (Saito et al., PLoS ONE, 2013). We found that pulsatile ERK phosphorylation was decoded by selective expression of EGR1 rather than c-FOS, and conjunctive NGF and PACAP stimulation was decoded by synergistic JUNB expression through a switch-like response to c-FOS.
The relationship between signaling molecules and downstream gene expression levels and cell-fate decision is a multiple-input and multiple-output (MIMO) system. We applied a statistical linear multivariate regression with reduction by variable elimination (backward elimination PLS regression) to growth factor-specific signaling and cell-fate decisions in PC12 cells, and extracted simple relationships of the MIMO system (Akimoto, et al, PLoS ONE, 2013).
We also analyzed the signaling pathway in the framework of Shannon’s information theory, and found that information transmission was generally more robust than average signal intensity despite pharmacological perturbations, and information transmission through unperturbed signaling pathways compensatorily increased in many signaling pathways (Uda, S. et al., Science, 2013) (Fig. 3).

Fig. 3
Cell-fate determination
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4. Synaptic plasticity

Synaptic plasticity is believed to be the cellular basis for learning and memory. Synaptic strength can be modified in a bi-directional manner, depending on the relative timing of pre- and postsynaptic spiking. This spike timing-dependent synaptic plasticity (STDP) is thought to play an important role in neural development and information storage. We have developed the electrophysiological and biochemical models of STDP (Urakubo et al, J. Neurosci., 2008), and analyzed the development of directional selectivity of visual system by STDP (Honda, et al, J. Neurosci., 2010). We are currently analyzing stochastic simulation of cerebellar LTD, and found the robust timing-detection via stochastic facilitation (Koumura et al, PLoS ONE, 2014, Fujii et al, Biophys. J., 2017).