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Biology subjects

Fukushima, Y.

Publications and source records attributed to Fukushima, Y..

3 recordsLinked to original sources

Interaction of FlhF, SRP-like GTPase with FliF, MS ring component assembling the initial structure of flagella in marine Vibrio

Vibrio alginolyticus forms a single flagellum at its cell pole. FlhF and FlhG are known to be the main proteins responsible for the polar formation of single flagellum. FlhF, which belongs to the signal recognition particle (SRP)-type GTPase family, localizes at the cell pole and initiates flagellar generation. In contrast, FlhG negatively regulates flagellar numbers. Furthermore, MS-ring formation in the flagellar basal body appears to be an initiation step for flagellar assembly. The MS-ring is formed by a single protein, FliF, which has two transmembrane (TM) segments and a large periplasmic region. We had shown that FlhF was required for the polar localization of Vibrio FliF, and FlhF facilitated MS-ring formation when FliF was overexpressed in E. coli cells. These results suggest that FlhF interacts with FliF to facilitate MS-ring formation. Here, we attempted to detect this interaction using Vibrio FliF fragments fused to a tag of Glutathione S-transferase (GST) in E. coli. We found that the N-terminal 108 residues of FliF, including the first TM segment and the periplasmic region, could pull FlhF down. In the first step, the SRP and its receptor are involved in the transport of membrane proteins to target them, which delivers them to the translocon. FlhF may have a similar or enhanced function as SRP, which binds to a region rich in hydrophobic residues. IMPORTANCEVibrio alginolyticus forms only a single flagellum at the cell pole by regulators of FlhF and FlhG. FlhF regulates positively the formation of flagella and is required for polar positioning of the flagellum. FliF, the two transmembrane (TM) segments and a large periplasmic region, forms the MS ring of flagellar basal body in the membrane. Previous studies suggest that FlhF interacts with FliF to facilitate MS ring formation at the cell pole, but the interaction has not been detected. Here, we show the evidence that FlhF interacts with FliF at residues including the first TM segment and following periplasmic region. The hydrophobic residues of this region seem to be important for the interaction.

microbiology↗

DNA methylation signature in NSD2 loss-of-function variants appeared similar to that in Wolf-Hirschhorn syndrome

PurposeWolf-Hirschhorn syndrome (WHS), a contiguous gene syndrome caused by the hemizygous deletion of the distal short arm of chromosome 4 where NSD2 is, reportedly exhibits specific DNA methylation signatures in peripheral blood cells. However, responsible genomic loci for signatures are unreported. The objective of the study is to define the loci of WHS-related DNA methylation signatures and to explore the role of NSD2 for the signatures. MethodsWe conducted genome-wide methylation analysis of individuals with WHS or NSD2 variants using array. We studied genome-edited knock in mice or induced pluripotent stem cells to explore the function of NSD2 variants which are observed in congenital anomaly cases. ResultsThree undiagnosed cases with NSD2 variants showed WHS-related DNA methylation signatures. These variants were validated to be NSD2 loss-of-function in induced pluripotent stem cells or genome-edited knock-in mice. p.Pro905Leu variant decreased Nsd2 protein levels, and changed Histone H3-Lysine 36 demethylation levels in similar way in the same genomic regions as Nsd2 knock out mice regulated. Nsd2 knock out mice exhibited common DNA methylation changes. ConclusionThese results revealed that WHS-related DNA methylation signatures are dependent on NSD2 dysfunction and are useful in diagnosing NSD2 variants of unknown significance.

genetics↗

gr Predictor: a Deep-Learning Model for Predicting the Hydration Structures around Proteins

Among the factors affecting biological processes such as protein folding and ligand binding, hydration, which is represented by a three-dimensional water-site-distribution-function around the protein, is crucial. The typical methods for computing the distribution functions, including molecular dynamics simulations and the three-dimensional reference interaction site model (3D-RISM) theory, require a long computation time from hours to tens of hours. Here, we propose a deep-learning model rapidly estimating the distribution functions around proteins obtained by the 3D-RISM theory from the protein 3D structure. The distribution functions predicted using our deep-learning model are in good agreement with those obtained by the 3D-RISM theory. Particularly, the coefficient of determination between the distribution function obtained by the deep-learning model and that obtained using the 3D-RISM theory is approximately 0.98. Furthermore, using a graphics processing unit (GPU), the calculation by the deep learning model is completed in less than one minute, more than 2 orders of magnitude faster than the calculation time of 3D-RISM theory. Therefore, our deep learning model provides a practical and efficient way to calculate the three-dimensional water-site-distribution-functions. The program called "gr Predictor" is available under the GNU General Public License from https://github.com/YoshidomeGroup-Hydration/gr-predictor. Table of Contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=55 SRC="FIGDIR/small/488616v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@108b3c7org.highwire.dtl.DTLVardef@52ec18org.highwire.dtl.DTLVardef@a388borg.highwire.dtl.DTLVardef@10654ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗