Machine learning uncovers cell identity regulator by histone code.

Journal: Nature communications
Published Date: Jun 1, 2020

Abstract

Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and their expression regulation. Here, we develop CEFCIG, an artificial intelligent framework to uncover CIGs and further define their master regulators. On the basis of machine learning, CEFCIG reveals unique histone codes for transcriptional regulation of reported CIGs, and utilizes these codes to predict CIGs and their master regulators with high accuracy. Applying CEFCIG to 1,005 epigenetic profiles, our analysis uncovers the landscape of regulation network for identity genes in individual cell or tissue types. Together, this work provides insights into cell identity regulation, and delivers a powerful technique to facilitate regenerative medicine.

Authors

  • Bo Xia
    Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA.
  • Dongyu Zhao
    Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA.
  • Guangyu Wang
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Min Zhang
    Department of Infectious Disease, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Jie Lv
    Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA.
  • Alin S Tomoiaga
    Business Analytics, CIS & Law Department, The O'Malley School of Business Accounting, Manhattan College, Riverdale, NY, USA.
  • Yanqiang Li
    Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Shu Meng
    Center for Cardiovascular Regeneration, Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX, USA.
  • John P Cooke
    Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, Tex; Center for Cardiovascular Regeneration, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex.
  • Qi Cao
    Department of Urology, Robert H. Lurie Comprehensive Cancer Center, Chicago, IL, USA. qi.cao@northwestern.edu.
  • Lili Zhang
    Pharmaceutics Department, Institute of Medicinal Biotechnology, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100050, PR China.
  • Kaifu Chen
    Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA. kchen2@houstonmethodist.org.

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