2024年1月23日 / 最終更新日 : 2024年1月23日 ad_y ニュース

2024年 新着論文 7　バイオインフォマティクス分野から論文が発表されました

Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information

Sci Rep. 2024 Jan 15;14(1):1315. doi: 10.1038/s41598-023-50905-8.

Authors

Mei Tomoto #  1 , Yohei Mineharu #  1   2 , Noriaki Sato  1   3 , Yoshinori Tamada  4 , Mari Nogami-Itoh  5 , Masataka Kuroda  5   6 , Jun Adachi  7 , Yoshito Takeda  8 , Kenji Mizuguchi  5   9 , Atsushi Kumanogoh  8 , Yayoi Natsume-Kitatani  10   11   12 , Yasushi Okuno  13   14   15

Affiliations

  • 1 Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
  • 2 Department of Artificial Intelligence in Healthcare and Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
  • 3 Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-Dai, Minato-Ku, Tokyo, 108-8639, Japan.
  • 4 Innovation Center for Health Promotion, Hirosaki University, 5 Zaifu-Cho Hirosaki City, Aomori, 036-8562, Japan.
  • 5 Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17, Senrioka-Shinmachi, Settsu City, Osaka, 566-0002, Japan.
  • 6 Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa, 227-0033, Japan.
  • 7 Laboratory of Proteomics for Drug Discovery, Center for Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka, 567-0085, Japan.
  • 8 Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, 2-2 Yamada-Oka, Suita City, Osaka, 565-0871, Japan.
  • 9 Institute for Protein Research, Osaka University, 3-2 Yamada-Oka, Suita City, Osaka, 565-0871, Japan.
  • 10 Innovation Center for Health Promotion, Hirosaki University, 5 Zaifu-Cho Hirosaki City, Aomori, 036-8562, Japan. natsume@nibiohn.go.jp.
  • 11 Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17, Senrioka-Shinmachi, Settsu City, Osaka, 566-0002, Japan. natsume@nibiohn.go.jp.
  • 12 Institute of Advanced Medical Sciences, Tokushima University, 3-18-15, Kuramoto-Cho, Tokushima City, Tokushima, 770-8503, Japan. natsume@nibiohn.go.jp.
  • 13 Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan. okuno.yasushi.4c@kyoto-u.ac.jp.
  • 14 Department of Artificial Intelligence in Healthcare and Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan. okuno.yasushi.4c@kyoto-u.ac.jp.
  • 15 Biomedical Computational Intelligence Unit, HPC- and AI-Driven Drug Development Platform Division, RIKEN Center for Computational Science, 7-1-26, Minatojima-Minami-Machi, Chuo-Ku, Kobe, Hyogo, 650-0047, Japan. okuno.yasushi.4c@kyoto-u.ac.jp.
# Contributed equally.

Free PMC article

Abstract

Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by severe lung fibrosis and a poor prognosis. Although the biomolecules related to IPF have been extensively studied, molecular mechanisms of the pathogenesis and their association with serum biomarkers and clinical findings have not been fully elucidated. We constructed a Bayesian network using multimodal data consisting of a proteome dataset from serum extracellular vesicles, laboratory examinations, and clinical findings from 206 patients with IPF and 36 controls. Differential protein expression analysis was also performed by edgeR and incorporated into the constructed network. We have successfully visualized the relationship between biomolecules and clinical findings with this approach. The IPF-specific network included modules associated with TGF-β signaling (TGFB1 and LRC32), fibrosis-related (A2MG and PZP), myofibroblast and inflammation (LRP1 and ITIH4), complement-related (SAA1 and SAA2), as well as serum markers, and clinical symptoms (KL-6, SP-D and fine crackles). Notably, it identified SAA2 associated with lymphocyte counts and PSPB connected with the serum markers KL-6 and SP-D, along with fine crackles as clinical manifestations. These results contribute to the elucidation of the pathogenesis of IPF and potential therapeutic targets.

© 2024. The Author(s).

Conflict of interest statement

The authors declare no competing interests.

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