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Seo, J.-K.

Publications and source records attributed to Seo, J.-K..

2 recordsLinked to original sources

HSP90 interacts with VP37 to facilitate the cell-to-cell movement of broad bean wilt virus 2

The systemic spread of viruses in plants requires successful viral cell-to-cell movement through plasmodesmata (PD). Viral movement proteins (MPs) interact with cellular proteins to modify and utilize host transport routes. Broad bean wilt virus 2 (BBWV2), a widespread plant RNA virus, moves from cell to cell as a virion through the PD gated by VP37, the MP of BBWV2. However, the host proteins that function in the cell-to-cell movement of BBWV2 remain unclear. In this study, we identified cellular heat shock protein 90 (HSP90) as an interacting partner of VP37. The interaction between HSP90 and VP37 was assessed using the yeast two-hybrid assay, co-immunoprecipitation, and bimolecular fluorescence complementation. Tobacco rattle virus-based virus-induced gene silencing analysis revealed that HSP90 silencing significantly inhibited the systemic spread of BBWV2 in N. benthamiana plants. Furthermore, in planta treatment with geldanamycin (GDA), an inhibitor of the chaperone function of HSP90, demonstrated the necessity of HSP90 in successful cell-to-cell movement and systemic infection of BBWV2. Interestingly, GDA treatment inhibited the HSP90-VP37 interaction at the PD, resulting in the inhibition of VP37-derived tubule formation through the PD. Our results suggest that the HSP90-VP37 interaction regulates VP37-derived tubule formation through the PD, thereby facilitating the cell-to-cell movement of BBWV2. IMPORTANCEThis study highlights the regulatory role of heat shock protein 90 (HSP90) in facilitating the cell-to-cell movement of broad bean wilt virus 2 (BBWV2). HSP90 interacted with VP37, the movement protein of BBWV2, specifically at plasmodesmata (PD). This study demonstrated that the HSP90-VP37 interaction is crucial for viral cell-to-cell movement and the formation of VP37-derived tubules, which are essential structures for virus transport through the PD. The ATP-dependent chaperone activity of HSP90 is integral to this interaction, as demonstrated by the inhibition of virus movement upon treatment with geldanamycin (GDA), which disrupts the function of HSP90. These findings elucidate the molecular mechanisms underlying the cell-to-cell movement of plant viruses, and highlight the role of HSP90 in viral infection. This study suggests that the chaperone activity of HSP90 may function in changing the conformational structure of VP37, thereby facilitating the assembly and function of virus-induced structures required for viral cell-to-cell movement.

microbiology↗

Motor Hotspot Localization Based on Electroencephalography Using Convolutional Neural Network in Patients with Stroke

BackgroundAlthough transcranial magnetic stimulation (TMS) is the optimal tool for identifying individual motor hotspots for transcranial electrical stimulation (tES), it requires a cumbersome procedure in which patients must visit the hospital each time and rely on expert judgment to determine the motor hotspot. Therefore, in previous study, we proposed electroencephalography (EEG)-based machine learning approach to automatically identify individual motor hotspots. In this study, we proposed an advanced EEG-based motor hotspot identification algorithm using a deep learning model and assessed its clinical feasibility and benefits by applying it to stroke patient EEGs. MethodsEEG data were measured from thirty subjects as they performed a simple hand movement task. We utilized the five types of input data depending on the processing levels to assess the signal processing capability of our proposed deep learning model. The motor hotspot locations were estimated using a two-dimensional convolutional neural network (CNN) model. The error distance between the 3D coordinate information of the individual motor hotspots identified by the TMS (ground truth) and EEGs was calculated using the Euclidean distance. Additionally, we confirmed the clinical benefits of our proposed deep-learning algorithm by applying the EEG of stroke patients. ResultsA mean error distance between the motor hotspot locations identified by TMS and our approach was 2.34 {+/-} 0.19 mm when using raw data from only 9 channels around the motor area. When it was tested on stroke patients, the mean error distance was 1.77 {+/-} 0.15 mm using only 5 channels around the motor area. ConclusionWe have demonstrated that an EEG-based deep learning approach can effectively identify the individual motor hotspots. Moreover, we validated the clinical benefits of our algorithm by successfully implementing it in stroke patients. Our algorithm can be used as an alternative to TMS for identifying motor hotspots and maximizing rehabilitation effectiveness.

bioengineering↗