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Precise engineering of gene expression by editing plasticity.

Yang Qiu1,2, Lifen Liu2,3, Jiali Yan1,2

  • 1National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.

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|March 11, 2025
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Summary
This summary is machine-generated.

Scientists developed a computational framework to accurately predict and utilize cis-regulatory elements (CREs) for precise gene expression control. This advances knowledge-guided gene editing, enabling enhanced crop traits beyond natural variation.

Keywords:
CREDeep learningEditing plasticityInsilico editing schemePrecise regulationUMI-STARR-seq

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Area of Science:

  • Plant molecular biology
  • Genomics
  • Bioinformatics

Background:

  • Identifying cis-regulatory elements (CREs) is crucial for understanding and manipulating gene expression.
  • This knowledge is fundamental for genetic engineering and improving crop traits.

Purpose of the Study:

  • To develop a computational framework for accurate prediction and utilization of CREs.
  • To enable precise regulation of gene expression beyond natural variation for crop improvement.

Main Methods:

  • Developed two deep learning models to identify distal and proximal CREs.
  • Validated enhancer activity of numerous distal CREs using UMI-STARR-seq.
  • Introduced 'editing plasticity' concept to evaluate promoter editing potential.

Main Results:

  • Accurately predicted CREs and their effects on gene expression.
  • Demonstrated precise gene expression regulation beyond natural variation.
  • Successfully applied the framework to engineer optimal phenotypes, exemplified by ZmVTE4 in maize.

Conclusions:

  • Provided a robust computational framework for knowledge-guided gene editing.
  • Enabled reliable prediction and validation of CREs for targeted genetic modifications.
  • Offers a tool to enhance desirable crop traits through precise gene regulation.