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Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission

Author

Listed:
  • Shinya Tasaki

    (Takeda Pharmaceutical Company Limited
    Rush Alzheimer’s Disease Center)

  • Katsuya Suzuki

    (Keio University School of Medicine)

  • Yoshiaki Kassai

    (Takeda Pharmaceutical Company Limited)

  • Masaru Takeshita

    (Keio University School of Medicine)

  • Atsuko Murota

    (Keio University School of Medicine)

  • Yasushi Kondo

    (Keio University School of Medicine)

  • Tatsuya Ando

    (Takeda Pharmaceutical Company Limited)

  • Yusuke Nakayama

    (Takeda Pharmaceutical Company Limited)

  • Yuumi Okuzono

    (Takeda Pharmaceutical Company Limited)

  • Maiko Takiguchi

    (Takeda Pharmaceutical Company Limited)

  • Rina Kurisu

    (Takeda Pharmaceutical Company Limited)

  • Takahiro Miyazaki

    (Takeda Pharmaceutical Company Limited
    Nektar Therapeutics)

  • Keiko Yoshimoto

    (Keio University School of Medicine)

  • Hidekata Yasuoka

    (Keio University School of Medicine)

  • Kunihiro Yamaoka

    (Keio University School of Medicine)

  • Rimpei Morita

    (Keio University School of Medicine)

  • Akihiko Yoshimura

    (Keio University School of Medicine)

  • Hiroyoshi Toyoshiba

    (Takeda Pharmaceutical Company Limited)

  • Tsutomu Takeuchi

    (Keio University School of Medicine)

Abstract

Sustained clinical remission (CR) without drug treatment has not been achieved in patients with rheumatoid arthritis (RA). This implies a substantial difference between CR and the healthy state, but it has yet to be quantified. We report a longitudinal monitoring of the drug response at multi-omics levels in the peripheral blood of patients with RA. Our data reveal that drug treatments alter the molecular profile closer to that of HCs at the transcriptome, serum proteome, and immunophenotype level. Patient follow-up suggests that the molecular profile after drug treatments is associated with long-term stable CR. In addition, we identify molecular signatures that are resistant to drug treatments. These signatures are associated with RA independently of known disease severity indexes and are largely explained by the imbalance of neutrophils, monocytes, and lymphocytes. This high-dimensional phenotyping provides a quantitative measure of molecular remission and illustrates a multi-omics approach to understanding drug response.

Suggested Citation

  • Shinya Tasaki & Katsuya Suzuki & Yoshiaki Kassai & Masaru Takeshita & Atsuko Murota & Yasushi Kondo & Tatsuya Ando & Yusuke Nakayama & Yuumi Okuzono & Maiko Takiguchi & Rina Kurisu & Takahiro Miyazaki, 2018. "Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05044-4
    DOI: 10.1038/s41467-018-05044-4
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    Cited by:

    1. Alexander Platzer & Thomas Nussbaumer & Thomas Karonitsch & Josef S Smolen & Daniel Aletaha, 2019. "Analysis of gene expression in rheumatoid arthritis and related conditions offers insights into sex-bias, gene biotypes and co-expression patterns," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-23, July.
    2. Xia Qing & Thompson Jeffrey A. & Koestler Devin C., 2021. "Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 20(4-6), pages 101-119, December.
    3. Jia You & Yu Guo & Yi Zhang & Ju-Jiao Kang & Lin-Bo Wang & Jian-Feng Feng & Wei Cheng & Jin-Tai Yu, 2023. "Plasma proteomic profiles predict individual future health risk," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Shouguo Gao & Zhijie Wu & Bradley Arnold & Carrie Diamond & Sai Batchu & Valentina Giudice & Lemlem Alemu & Diego Quinones Raffo & Xingmin Feng & Sachiko Kajigaya & John Barrett & Sawa Ito & Neal S. Y, 2022. "Single-cell RNA sequencing coupled to TCR profiling of large granular lymphocyte leukemia T cells," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    5. Niki Karagianni & Ksanthi Kranidioti & Nikolaos Fikas & Maria Tsochatzidou & Panagiotis Chouvardas & Maria C Denis & George Kollias & Christoforos Nikolaou, 2019. "An integrative transcriptome analysis framework for drug efficacy and similarity reveals drug-specific signatures of anti-TNF treatment in a mouse model of inflammatory polyarthritis," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-25, May.

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