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Plant Promoter Analysis: Identification and Characterization of Root Nodule Specific Promoter in the Common Bean
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Design and Testing of Root-Specific Synthetic Promoters by Machine Learning.

Chunhao Lu1,2,3, Yuepeng Song1,2,3, Deqiang Zhang1,2,3,4

  • 1State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.

International Journal of Molecular Sciences
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed a machine learning method to find DNA elements controlling gene expression in specific plant tissues. This approach successfully created a novel synthetic promoter for targeted gene delivery in poplar plants.

Keywords:
Populuscis-regulatory modulemachine learningsynthetic promoter

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Area of Science:

  • Plant Biotechnology
  • Molecular Biology
  • Bioinformatics

Background:

  • Designing synthetic promoters for precise gene expression in plants is challenging due to difficulties in identifying functional cis-regulatory elements (CREs).
  • Understanding tissue-specific gene regulation is key for advancing plant biotechnology and genetic engineering.

Purpose of the Study:

  • To develop a systematic approach for discovering tissue-specific cis-regulatory modules (CRMs) in poplar.
  • To generate and validate functional synthetic promoters using identified CRMs for targeted gene expression.

Main Methods:

  • Extensive transcriptomic analysis across diverse poplar tissues to generate expression profiles.
  • Motif detection in gene promoters using known transcription factor binding site (TFBS) position weight matrices.
  • Machine learning (random forest model) to predict informative, tissue-specific TFBSs and identify root-specific CRMs for the PopRTS1 gene.

Main Results:

  • Identification of putative root-specific CRMs associated with the PopRTS1 gene.
  • Construction and experimental validation of synthetic promoters using identified CRMs via rapid infiltration and GUS staining assays.
  • Successful identification and validation of a novel root-specific synthetic promoter, PRTS1.

Conclusions:

  • Machine learning effectively deciphers regulatory codes from omics data to predict functional CRMs.
  • The study provides a feasible and systematic method for screening and designing tissue-specific synthetic promoters.
  • This approach has significant potential for advancing targeted gene expression systems in plant biotechnology.