Proceedings of the Japan Academy, Series B
Online ISSN : 1349-2896
Print ISSN : 0386-2208
ISSN-L : 0386-2208
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  • Hiroyuki YOSHIDA, Shintaro INOUE, Yasunori OKADA
    2025 Volume 101 Issue 6 Pages 317-338
    Published: June 11, 2025
    Released on J-STAGE: June 11, 2025
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    The biological activity of hyaluronan (HA), a major component of the extracellular matrix in vertebrate tissues, depends on its molecular weight, and thus its degradation is a critical process for HA biological functions. Here, we review the characteristics of newly discovered proteins essential for HA degradation, hyaluronan-binding protein involved in hyaluronan depolymerization (HYBID), also known as cell migration inducing hyaluronidase 1 (CEMIP) and KIAA1199, and transmembrane protein-2 (TMEM2; alias CEMIP2). Human and mouse forms of HYBID exert their HA-degrading activity in special microenvironments including recycling endosomes. Mouse TMEM2 functions as a cell-surface hyaluronidase for HA turnover in local tissues, lymph nodes, and the liver. In contrast, the role of human TMEM2 in HA degradation is the subject of much debate. HYBID expression is upregulated by proinflammatory factors such as histamine and interleukin-6 and downregulated by transforming growth factor-β. HYBID is involved in physiological HA turnover in human skin and joint tissues and plays an important role in their pathological destruction by accelerating HA degradation.

    Hypothesis of hyaluronan (HA) degradation mediated by HYBID and TMEM2 in mice and humans.HA degradation is carried out in two steps: The first step in local tissues and the second step in the lymphatic system and the liver. High molecular weight (HMW)-HA interacted with other extracellular matrix molecules such as proteoglycans is depolymerized into medium molecular weight (MMW)-HA fragments by mTMEM2 and mHYBID in mouse local tissues and by hHYBID and secreted hHYAL1 in human local tissues. In the second step, MMW-HA released from the local tissues flow into lymphatic vessels and regional lymph nodes, in which lymphatic endothelial cells are responsible for the degradation of MMW-HA into low molecular weight (LMW)-HA and oligosaccharides by mTMEM2, mHYAL1 and lysosomal glycosidases in mice and by hHYAL1 and lysosomal glycosidases in humans. HA fragments generated in the lymphatic system reach the general circulation and are further catabolized into monosaccharides by sinusoidal endothelial cells in the liver probably by following the similar pathways shown in the lymphatic system. Fullsize Image
  • Kazuhiro MAESHIMA
    2025 Volume 101 Issue 6 Pages 339-356
    Published: June 11, 2025
    Released on J-STAGE: June 11, 2025
    Advance online publication: April 25, 2025
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    Supplementary material

    The organization and dynamics of chromatin are critical for genome functions such as transcription and DNA replication/repair. Historically, chromatin was assumed to fold into the 30-nm fiber and progressively arrange into larger helical structures, as described in the textbook model. However, over the past 15 years, extensive evidence including our studies has dramatically transformed the view of chromatin from a static, regular structure to one that is more variable and dynamic. In higher eukaryotic cells, chromatin forms condensed yet liquid-like domains, which appear to be the basic unit of chromatin structure, replacing the 30-nm fiber. These domains maintain proper accessibility, ensuring the regulation of DNA reaction processes. During mitosis, these domains assemble to form more gel-like mitotic chromosomes, which are further constrained by condensins and other factors. Based on the available evidence, I discuss the physical properties of chromatin in live cells, emphasizing its viscoelastic nature—balancing local fluidity with global stability to support genome functions.

    From Regular Fibers to Fluid: The New View of Chromatin Organization Fullsize Image
Original Article
  • Yoshihiro KOYAMA, Mizuki NASU, Yoshihiro MATSUOKA
    2025 Volume 101 Issue 6 Pages 357-370
    Published: June 11, 2025
    Released on J-STAGE: June 11, 2025
    Advance online publication: May 21, 2025
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    Supplementary material

    Aegilops tauschii Coss., a progenitor of bread wheat, is an important wild genetic resource for breeding. The species comprises three genetically defined lineages (TauL1, TauL2, and TauL3), each displaying valuable phenotypes in agronomic traits, including spike shape. In the present work, we studied the relationship between population structure and spike shape variation patterns using a collection of 249 accessions. f4-statistics-based ancestry profiling confirmed the previously identified lineages and revealed a genetic component derived from TauL3 in the genomes of some southern Caspian and Transcaucasus TauL1 and TauL2 accessions. Spike shape variation patterns were analyzed using a convolutional neural network-based approach, trained on green and dry spike image datasets. This analysis showed that spike shape diversity is structured according to lineages and demonstrated the potential to distinguish the lineages based on spike shape. The implications of these findings for the origins of common wheat and the intraspecific taxonomy of Ae. tauschii are discussed.

    Tracing origins through spike shape using machine learning. Fullsize Image
    Wild wheat, Aegilops tauschii, harbors a vast reservoir of alleles that remain untapped in breeding programs. These alleles could contribute valuable traits, such as drought tolerance and disease resistance, when introduced into bread wheat. To fully harness this genetic diversity, it is crucial to rapidly identify strains carrying potentially beneficially alleles. In Ae. tauschii, which is composed of strain groups (lineages) with unique genetic makeups, this can be done by determining a strain’s lineage based on spike shape. In this work, we trained machine learning models to predict lineage based on spike morphology and found that spike shape diversity mirrors lineage structure. These models demonstrated potential for practical use in assigning strains to their respective lineages based on spike shape. This work opens new avenues for the application of machine learning in wheat improvement, as well as in the genetic and evolutionary studies of wheat morphology.
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