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The neutrophil–osteogenic cell axis promotes bone destruction in periodontitis

International Journal of Oral Science 2024年第1期16卷 154-162页

作者： Yutaro Ando Masayuki Tsukasaki Nam Cong-Nhat Huynh Shizao Zang Minglu Yan Ryunosuke Muro Kazutaka Nakamura Masatsugu Komagamine Noriko Komatsu Kazuo Okamoto kenta Nakano Tadashi Okamura Akira Yamaguchi Kazuyuki Ishihara Hiroshi Takayanagi Department of Immunology Graduate School of Medicine and Faculty of MedicineThe University of Tokyo7-3-1HongoBunkyo-kuTokyoJapan Department of Microbiology Tokyo Dental College2-1-14 Kanda-Misaki-choChiyoda-kuTokyoJapan Oral Health Science Center Tokyo Dental College2-9-18Kanda-Misaki-choChiyodakuTokyoJapan Department of Osteoimmunology Graduate School of Medicine and Faculty of MedicineThe University of Tokyo7-3-1HongoBunkyo-kuTokyoJapan Unit of Prosthodontics Laboratory of Oral-Maxillofacial Biology Faculty of Odonto-StomatologyUniversity of Medicine and Pharmacy at Ho Chi Minh CityHo Chi Minh CityVietnam Department of Oral and Maxillofacial Surgery Department of Sensory and Motor System MedicineGraduate School of MedicineThe University of TokyoTokyoJapan Division of Rheumatology Department of Internal MedicineKeio University School of MedicineTokyoJapan Department of Laboratory Animal Medicine Research InstituteNational Center for Global Health and MedicineTokyoJapan

The immune-stromal cell interactions play a key role in health and diseases. In periodontitis, the most prevalent infectious disease in humans, immune cells accumulate in the oral mucosa and promote bone destruction by inducing receptor activator of nuclear factor-κB ligand (RANKL) expression in osteogenic cells such as osteoblasts and periodontal ligament cells. However, the detailed mechanism underlying immune–bone cell interactions in periodontitis is not fully understood. Here, we performed single-cell RNAsequencing analysis on mouse periodontal lesions and showed that neutrophil–osteogenic cell crosstalk is involved in periodontitis-induced bone loss. The periodontal lesions displayed marked infiltration of neutrophils, and in silico analyses suggested that the neutrophils interacted with osteogenic cells through cytokine production. Among the cytokines expressed in the periodontal neutrophils, oncostatin M (OSM) potently induced RANKL expression in the primary osteoblasts, and deletion of the OSM receptor in osteogenic cells significantly ameliorated periodontitis-induced bone loss. Epigenomic data analyses identified the OSM-regulated RANKL enhancer region in osteogenic cells, and mice lacking this enhancer showed decreased periodontal bone loss while maintaining physiological bone metabolism. These findings shed light on the role of neutrophils in bone regulation during bacterial infection, highlighting the novel mechanism underlying osteoimmune crosstalk.

关键词： period neutrophil destruction

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Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

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npj Computational Materials 2019年第1期5卷 569-579页

作者： Stephen Wu Yukiko Kondo Masa-aki Kakimoto Bin Yang Hironao Yamada Isao Kuwajima Guillaume Lambard Kenta Hongo Yibin Xu Junichiro Shiomi Christoph Schick Junko Morikawa Ryo Yoshida The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan The Graduate University for Advanced Studies TachikawaTokyo 190-8562Japan Center for Materials research by Information Integration(CMI2) Research and Services Division of Materials Data and Integrated System(MaDIS)National Institute for Materials Science(NIMS)TsukubaIbaraki 305-0047Japan Institute of Physics and Competence Centre CALOR University of Rostock18059 RostockGermany Japan Advanced Institute of Science and Technology NomiIshikawa 923-1292Japan PRESTO JSTKawaguchiSaitama 332-0012Japan The University of Tokyo Bunkyo-kuTokyo 113-8656Japan Tokyo Tech World Research Hub Initiative(WRHI) Tokyo Institute of TechnologyTokyo 226-8503Japan Tokyo Institute of Technology Meguro-kuTokyo 152-8550Japan

The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.

关键词： conductivity thermal property

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