Domain structure and function of α-1,3-glucanase Agl-EK14 from the gram-negative bacterium Flavobacterium sp. EK-14.

The α-1,3-glucanase Agl-EK14 from Flavobacterium sp. EK-14 comprises a signal peptide (SP), a catalytic domain (CAT), a first immunoglobulin-like domain (Ig1), a second immunoglobulin-like domain (Ig2), a ricin B-like lectin domain (RicinB), and a carboxy-terminal domain (CTD). SP and CTD are predicted to be involved in extracellular secretion, while the roles of Ig1, Ig2, and RicinB are unclear. To clarify their roles, domain deletion enzymes Agl-EK14ΔRicinB, Agl-EK14ΔIg2RicinB, and Agl-EK14ΔIg1Ig2RicinB were constructed. The insoluble α-1,3-glucan hydrolytic, α-1,3-glucan binding, and fungal cell wall hydrolytic activities of the deletion enzymes were almost the same and lower than those of Agl-EK14. Kinetic analysis revealed that the Km values of the deletion enzymes were similar and uniformly higher than those of Agl-EK14. These results suggest that the deletion of RicinB causes a decline in binding and hydrolytic activity and increases the Km value. To confirm the role of RicinB, Ig1, Ig2, and RicinB were fused with green fluorescent protein (GFP). As a result, RicinB-fused GFP (GFP-RicinB) showed binding to insoluble α-1,3-glucan and Aspergillus oryzae cell walls, whereas Ig1- and Ig2-fused GFP did not. These results indicated that RicinB is involved in α-1,3-glucan binding. The fusion protein GFP-Ig1Ig2RicinB was also constructed and GFP-Ig1Ig2RicinB showed strong binding to the cell wall of A. oryzae compared to GFP-RicinB. Gel filtration column chromatography suggested that the strong binding was due to GFP-Ig1Ig2RicinB loosely associated with itself.

Noise-robust optimization of quantum machine learning models for polymer properties using a simulator and validated on the IonQ quantum computer.

Quantum machine learning for predicting the physical properties of polymer materials based on the molecular descriptors of monomers was investigated. Under the stochastic variation of the expected predicted values obtained from quantum circuits due to finite sampling, the methods proposed in previous works did not make sufficient progress in optimizing the parameters. To enable parameter optimization despite the presence of stochastic variations in the expected values, quantum circuits that improve prediction accuracy without increasing the number of parameters and parameter optimization methods that are robust to stochastic variations in the expected predicted values, were investigated. The multi-scale entanglement renormalization ansatz circuit improved the prediction accuracy without increasing the number of parameters. The stochastic gradient descent method using the parameter-shift rule for gradient calculation was shown to be robust to sampling variability in the expected value. Finally, the quantum machine learning model was trained on an actual ion-trap quantum computer. At each optimization step, the coefficient of determination [Formula: see text] improved equally on the actual machine and simulator, indicating that our findings enable the training of quantum circuits on the actual quantum computer to the same extent as on the simulator.

Visualization of intracellular lipid metabolism in brown adipocytes by time-lapse ultra-multiplex CARS microspectroscopy with an onstage incubator.

We visualized a dynamic process of fatty acid uptake of brown adipocytes using a time-lapse ultra-broadband multiplex coherent anti-Stokes Raman scattering (CARS) spectroscopic imaging system with an onstage incubator. Combined with the deuterium labeling technique, the intracellular uptake of saturated fatty acids was traced up to 9 h, a substantial advance over the initial multiplex CARS system, with an analysis time of 80 min. Characteristic metabolic activities of brown adipocytes, such as resistance to lipid saturation, were elucidated, supporting the utility of the newly developed system.

Morphogens, their identification and regulation.

During the course of development, cells of many tissues differentiate according to the positional information that is set by the concentration gradients of morphogens. Morphogens are signaling molecules that emanate from a restricted region of a tissue and spread away from their source to form a concentration gradient. As the fate of each cell in the field depends on the concentration of the morphogen signal, the gradient prefigures the pattern of development. In this article, we describe how morphogens and their functions have been identified and analyzed, focusing on model systems that have been extensively studied.

Three Drosophila EXT genes shape morphogen gradients through synthesis of heparan sulfate proteoglycans.

The signaling molecules Hedgehog (Hh), Decapentaplegic (Dpp) and Wingless (Wg) function as morphogens and organize wing patterning in Drosophila. In the screen for mutations that alter the morphogen activity, we identified novel mutants of two Drosophila genes, sister of tout-velu (sotv) and brother of tout-velu (botv), and new alleles of tout-velu (ttv). The encoded proteins of these genes belong to an EXT family of proteins that have or are closely related to glycosyltransferase activities required for biosynthesis of heparan sulfate proteoglycans (HSPGs). Mutation in any of these genes impaired biosynthesis of HSPGs in vivo, indicating that, despite their structural similarity, they are not redundant in the HSPG biosynthesis. Protein levels and signaling activities of Hh, Dpp and Wg were reduced in the cells mutant for any of these EXT genes to a various degree, Wg signaling being the least sensitive. Moreover, all three morphogens were accumulated in the front of EXT mutant cells, suggesting that these morphogens require HSPGs to move efficiently. In contrast to previous reports that ttv is involved exclusively in Hh signaling, we found that ttv mutations also affected Dpp and Wg. These data led us to conclude that each of three EXT genes studied contribute to Hh, Dpp and Wg morphogen signaling. We propose that HSPGs facilitate the spreading of morphogens and therefore, function to generate morphogen concentration gradients.