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<p>In vivo haploid induction has revolutionized crop breeding by shifting it from experience-driven selection to precision design methodologies. This review synthesizes three core mechanistic modules—male gamete defects (<i>MTL</i>/<i>DMP</i>/<i>KPL</i>), gametophyte reprogramming (<i>BBM-BAR1</i>), and centromere engineering (<i>CENH3</i>/<i>KNL2</i>)—into a cohesive framework and provides a critical assessment of their respective constraints. We emphasize emerging technological platforms (HIEdit, GEDH) that support transgene-free genome editing and outline advances in synthetic apomixis aimed at stabilizing hybrid vigor. Defining innovations include temperature-sensitive enhancement, synergistic mutation pyramiding, and AI-aided rational design. Persistent challenges include low efficiency in dicots, large-genome crops, and paternal haploid induction. Future research should integrate multiomics, synthetic biology, and machine learning to develop universal, cross-species haploid induction systems.</p>

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Diverse molecular mechanisms of in vivo haploid induction in plants: from fertilization interference to genome programming

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Wenjun Wu,
  • Yueshi Ding,
  • Yuhong Guan,
  • Zhu Luo,
  • Xiang Liu

摘要

In vivo haploid induction has revolutionized crop breeding by shifting it from experience-driven selection to precision design methodologies. This review synthesizes three core mechanistic modules—male gamete defects (MTL/DMP/KPL), gametophyte reprogramming (BBM-BAR1), and centromere engineering (CENH3/KNL2)—into a cohesive framework and provides a critical assessment of their respective constraints. We emphasize emerging technological platforms (HIEdit, GEDH) that support transgene-free genome editing and outline advances in synthetic apomixis aimed at stabilizing hybrid vigor. Defining innovations include temperature-sensitive enhancement, synergistic mutation pyramiding, and AI-aided rational design. Persistent challenges include low efficiency in dicots, large-genome crops, and paternal haploid induction. Future research should integrate multiomics, synthetic biology, and machine learning to develop universal, cross-species haploid induction systems.