Biology subjects

Nakata, S.

Publications and source records attributed to Nakata, S..

4 recordsLinked to original sources

bioRxiv2026

SIK3 links synaptic strength to sleep pressure through RhoA signaling

Sleep is regulated by homeostatic sleep pressure, which accumulates during wakefulness, triggering sleep, and dissipates during sleep, leading to wakefulness. Although recent evidence suggests that synaptic potentiation encodes sleep pressure, the intracellular mechanisms linking synaptic strength to sleep pressure remain unclear. Here we demonstrate that the sleep-promoting kinase SIK3 links synaptic strength and sleep pressure through RhoA signalling. We found that SIK3 is required for dendritic spine enlargement induced by chemical long-term potentiation (cLTP) in cultured cortical pyramidal neurons and positively regulates spine size in the prefrontal cortex in vivo. cLTP reduced RhoA activity in an SIK3-dependent manner during spine enlargement, whereas activation of RhoA reduced EEG delta density in vivo during non-rapid eye movement sleep (NREMS), an indicator of sleep pressure. SIK3 substrate screening identified several RhoA regulators including GEF-H1, and further biochemical analyses found that phosphorylation by SIK3 activates GEF-H1. However, contrary to our expectation, RhoA facilitation through pharmacological activation of GEF-H1 enlarged spine size in cultured neurons, and expression of constitutively active GEF-H1 increased NREMS delta density in a RhoA-dependent fashion. Together, these results indicate that SIK3 enlarges spine size and enhances NREMS delta density through two RhoA-dependent pathways, driving mutually opposing intermediate phenotypes. Our findings identify two clashing SIK3-RhoA signalling pathways that couple synaptic regulation to sleep homeostasis.

neuroscience↗

bioRxiv2026

Open Digital Bioassays Enabled by Aqueous Two-Phase Microreactor Arrays

Digital bioassays enable precise single-molecule quantification but are difficult to adapt to point-of-care testing (POCT) because conventional protocols include off-chip complex processes for sample treatment and sealing, requiring hardware and workflow complexity. We present OASSIS (Open Aqueous two-phase Separation System for Integrated Single- molecule digital bioassay platform), an oil-free and open ATPS platform that localizes both targets and signals in femtoliter-scale dextran (DEX) droplets beneath a polyethylene glycol (PEG) phase. We integrated a CRISPR-Cas13a assay system with a novel, branched fluorescent reporter conjugated to a dextran-binding domain (DBD), which ensures signal retention within the DEX droplets after cleavage. Fluorescence recovery after photobleaching experiments confirmed this robust signal confinement. OASSIS not only performs amplification-free digital RNA detection but also enables serial sample introductions through its open-format architecture that progressively improve sensitivity: the limit of detection (LOD) improved from 1.08 fM (first introduction) to 0.34 fM (third introduction). Furthermore, OASSIS demonstrated specific detection and [~]10-fold enrichment from a complex, denaturant-treated nasopharyngeal swab matrix. Together, these results demonstrate that the open-format architecture of OASSIS provides a practical route toward sensitive, low-complexity POCT and clinical diagnostic applications.

biophysics↗

bioRxiv2023

Anesthetic-Binding Induced Motion of GABA A Receptors Revealed by Coarse-Grained Molecular Dynamics Simulations

General anesthetics are indispensable in modern medicine because they induce a reversible loss of consciousness and sensation in humans. On the other hand, their molecular mechanisms of action have not yet been elucidated. Several studies have identified the main targets of some general anesthetics. The structures of {gamma}-aminobutyric acid A (GABAA) receptors with the intravenous anesthetics such as propofol and etomidate have recently been determined. Although these anesthetic-binding structures provide essential insights into the mechanism of action of anesthetics, the detailed molecular mechanism of how the anesthetic binding affects the Cl- permeability of GABAA receptors remains to be elucidated. In this study, we performed coarse-grained molecular dynamics simulations for GABAA receptors and analyzed the resulting simulation trajectories to investigate the effects of anesthetic binding on the motion of GABAA receptors. The results showed large structural fluctuations in GABAA receptors, correlations of motion between the amino-acid residues, large amplitude motion, and autocorrelated slow motion, which were obtained by advanced statistical analyses. In addition, comparison of the resulting trajectories in the presence or absence of the anesthetic molecules revealed a characteristic pore motion related to the gate-opening motion of GABAA receptors.

biophysics↗

bioRxiv2022

End-to-end protein-ligand complex structure generation with diffusion-based generative models

Three-dimensional structures of protein-ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug design. However, their high-dimensional and multimodal nature hinders end-to-end modeling, and earlier approaches depend inherently on existing protein structures. To overcome these limitations and expand the range of complexes that can be accurately modeled, it is necessary to develop efficient end-to-end methods. We introduce an equivariant diffusion-based generative model that learns the joint distribution of ligand and protein conformations conditioned on the molecular graph of a ligand and the sequence representation of a protein extracted from a pre-trained protein language model. Benchmark results show that this protein structure-free model is capable of generating diverse structures of protein-ligand complexes, including those with correct binding poses. Further analyses indicate that the proposed end-to-end approach is particularly effective when the ligand-bound protein structure is not available. The present results demonstrate the effectiveness and generative capability of our end-to-end complex structure modeling framework with diffusion-based generative models. We suppose that this framework will lead to better modeling of protein-ligand complexes, and we expect further improvements and wide applications.

bioinformatics↗