Application of Metabolic 13C Labeling in Conjunction with High-Field Nuclear Magnetic Resonance Spectroscopy for Comparative Conformational Analysis of High Mannose-Type Oligosaccharides

High mannose-type oligosaccharides are enzymatically trimmed in the endoplasmic reticulum, resulting in various processing intermediates with exposed glycotopes that are recognized by a series of lectins involved in glycoprotein fate determination in cells. Although recent crystallographic data have provided the structural basis for the carbohydrate recognition of intracellular lectins, atomic information of dynamic oligosaccharide conformations is essential for a quantitative understanding of the energetics of carbohydrate–lectin interactions. Carbohydrate NMR spectroscopy is useful for characterizing such conformational dynamics, but often hampered by poor spectral resolution and lack of recombinant techniques required to produce homogeneous glycoforms. To overcome these difficulties, we have recently developed a methodology for the preparation of a homogeneous high mannose-type oligosaccharide with 13C labeling using a genetically engineered yeast strain. We herein successfully extended this method to result in the overexpression of 13C-labeled Man9GlcNAc2 (M9) with a newly engineered yeast strain with the deletion of four genes involved in N-glycan processing. This enabled high-field NMR analyses of 13C-labeled M9 in comparison with its processing product lacking the terminal mannose residue ManD2. Long-range NOE data indicated that the outer branches interact with the core in both glycoforms, and such foldback conformations are enhanced upon the removal of ManD2. The observed conformational variabilities might be significantly associated with lectins and glycan-trimming enzymes

Inhibition of β2-Microglobulin Amyloid Fibril Formation by α2-Macroglobulin*

The relationship between various amyloidoses and chaperones is gathering attention. In patients with dialysis-related amyloidosis, α2-macroglobulin (α2M), an extracellular chaperone, forms a complex with β2-microglobulin (β2-m), a major component of amyloid fibrils, but the molecular mechanisms and biological implications of the complex formation remain unclear. Here, we found that α2M substoichiometrically inhibited the β2-m fibril formation at a neutral pH in the presence of SDS, a model for anionic lipids. Binding analysis showed that the binding affinity between α2M and β2-m in the presence of SDS was higher than that in the absence of SDS. Importantly, SDS dissociated tetrameric α2M into dimers with increased surface hydrophobicity. Western blot analysis revealed that both tetrameric and dimeric α2M interacted with SDS-denatured β2-m. At a physiologically relevant acidic pH and in the presence of heparin, α2M was also dissociated into dimers, and both tetrameric and dimeric α2M interacted with β2-m, resulting in the inhibition of fibril growth reaction. These results suggest that under conditions where native β2-m is denatured, tetrameric α2M is also converted to dimeric form with exposed hydrophobic surfaces to favor the hydrophobic interaction with denatured β2-m, thus dimeric α2M as well as tetrameric α2M may play an important role in controlling β2-m amyloid fibril formation

Engineering Design of the Mini-RT Device

"The plasma experiment apparatus S-RT (Superconducting Ring Trap) is planned for the purpose of high beta plasma confinement research in the University of Tokyo. As a preceding step, Mini-RT, which is the size reduction version of S-RT, has been constructed as a joint research of NIFS, the University of Tokyo, and Kyushu University. In this experiment a magnetic-levitation coil (floating coil) operated in persistent current mode has to levitate for 8 hours in the plasma vacuum vessel. The HTS floating coil wound with a Bi-2223 tape has a diameter of 300 mm and an electromotive force of 50 kA. Since any refrigerant cannot be fed to the coil during the plasma experiment, the coil is designed so that the temperature rise after 8 hours of levitation is less than 40 K with the specific heat of the coil and radiation shield. At the end of the daily plasma experiment, the coil will be drawn down to the maintenance location at the bottom of the plasma vacuum vessel, and it will be re-cooled to 20 K.

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches.

Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between the deep learning and human approaches. Using an explainable model, we aimed to compare the deep learning-focused regions of magnetic resonance (MR) images with cancerous locations identified by radiologists and pathologists. First, 307 prostate MR images were classified using a well-established deep neural network without locational information of cancers. Subsequently, we assessed whether the deep learning-focused regions overlapped the radiologist-identified targets. Furthermore, pathologists provided histopathological diagnoses on 896 pathological images, and we compared the deep learning-focused regions with the genuine cancer locations through 3D reconstruction of pathological images. The area under the curve (AUC) for MR images classification was sufficiently high (AUC = 0.90, 95% confidence interval 0.87-0.94). Deep learning-focused regions overlapped radiologist-identified targets by 70.5% and pathologist-identified cancer locations by 72.1%. Lymphocyte aggregation and dilated prostatic ducts were observed in non-cancerous regions focused by deep learning. Deep learning algorithms can achieve highly accurate image classification without necessarily identifying radiological targets or cancer locations. Deep learning may find clues that can help a clinical diagnosis even if the cancer is not visible

Hexafluoroisopropanol Induces Amyloid Fibrils of Islet Amyloid Polypeptide by Enhancing Both Hydrophobic and Electrostatic Interactions*

Although amyloid fibrils deposit with various proteins, the comprehensive mechanism by which they form remains unclear. We studied the formation of fibrils of human islet amyloid polypeptide associated with type II diabetes in the presence of various concentrations of 1,1,1,3,3,3-hexafluoroisopropanol (HFIP) under acidic and neutral pH conditions using CD, amyloid-specific thioflavin T fluorescence, fluorescence imaging with thioflavin T, and atomic force microscopy. At low pH, the formation of fibrils was promoted by HFIP with an optimum at 5% (v/v). At neutral pH in the absence of HFIP, significant amounts of amorphous aggregates formed in addition to the fibrils. The addition of HFIP suppressed the formation of amorphous aggregates, leading to a predominance of fibrils with an optimum effect at 25% (v/v). Under both conditions, higher concentrations of HFIP dissolved the fibrils and stabilized the α-helical structure. The results indicate that fibrils and amorphous aggregates are different types of precipitates formed by exclusion from water-HFIP mixtures. The exclusion occurs through the combined effects of hydrophobic interactions and electrostatic interactions, both of which are strengthened by low concentrations of HFIP, and a subtle balance between the two types of interactions determines whether the fibrils or amorphous aggregates dominate. We suggest a general view of how the structure of precipitates varies dramatically from single crystals to amyloid fibrils and amorphous aggregates