Regulome-based characterization of drug activity across the human diseasome

Michio Iwata, Keisuke Kosai, Yuya Ono, Shinya Oki, Koshi Mimori, Yoshihiro Yamanishi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Drugs are expected to recover the cell system away from the impaired state to normalcy through disease treatment. However, the understanding of gene regulatory machinery underlying drug activity or disease pathogenesis is far from complete. Here, we perform large-scale regulome analysis for various diseases in terms of gene regulatory machinery. Transcriptome signatures were converted into regulome signatures of transcription factors by integrating publicly available ChIP-seq data. Regulome-based correlations between diseases and their approved drugs were much clearer than the transcriptome-based correlations. For example, an inverse correlation was observed for cancers, whereas a positive correlation was observed for immune system diseases. After demonstrating the usefulness of the regulome-based drug discovery method in terms of accuracy and applicability, we predicted new drugs for nonsmall cell lung cancer and validated the anticancer activity in vitro. The proposed method is useful for understanding disease–disease relationships and drug discovery.

Original languageEnglish
Article number44
Journalnpj Systems Biology and Applications
Issue number1
Publication statusPublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • General Biochemistry,Genetics and Molecular Biology
  • Drug Discovery
  • Computer Science Applications
  • Applied Mathematics


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