A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS).

Journal: Nature communications
PMID: 31253799

Abstract

Cell state-specific promoters constitute essential tools for basic research and biotechnology because they activate gene expression only under certain biological conditions. Synthetic Promoters with Enhanced Cell-State Specificity (SPECS) can be superior to native ones, but the design of such promoters is challenging and frequently requires gene regulation or transcriptome knowledge that is not readily available. Here, to overcome this challenge, we use a next-generation sequencing approach combined with machine learning to screen a synthetic promoter library with 6107 designs for high-performance SPECS for potentially any cell state. We demonstrate the identification of multiple SPECS that exhibit distinct spatiotemporal activity during the programmed differentiation of induced pluripotent stem cells (iPSCs), as well as SPECS for breast cancer and glioblastoma stem-like cells. We anticipate that this approach could be used to create SPECS for gene therapies that are activated in specific cell states, as well as to study natural transcriptional regulatory networks.

Authors

  • Ming-Ru Wu
    Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Lior Nissim
    Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel.
  • Doron Stupp
    Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel.
  • Erez Pery
    Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Adina Binder-Nissim
    Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Karen Weisinger
    Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Casper Enghuus
    Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Sebastian R Palacios
    Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Melissa Humphrey
    Brain Tumor Research Center, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02144, USA.
  • Zhizhuo Zhang
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Eva Maria Novoa
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Manolis Kellis
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Ron Weiss
    Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Samuel D Rabkin
    Brain Tumor Research Center, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02144, USA.
  • Yuval Tabach
    Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel. yuvaltab@ekmd.huji.ac.il.
  • Timothy K Lu
    Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. timlu@mit.edu.

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