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<p>Rice yield is a complex multigenic trait influenced by the interplay of multiple genes, understanding the underlying mechanisms of yield-related genes, such as <i>OsSPL14</i>, is essential for effective crop improvement strategies. <i>OsSPL14 (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE14)/IDEAL PLANT ARCHITECTURE1 (IPA1)</i> is a pleiotropic gene that plays a crucial role in determining plant architecture, contributing to increased grain yield in rice. As a transcription factor, <i>OsSPL14</i> regulates not only genes related to yield but also those that influence plant growth, development, and immune responses. In the present study, an attempt has been made to understand the functional and structural roles of <i>OsSPL14</i> in relation to rice yield and other related traits. Phylogenetic analysis and conserved motif identification established the evolutionary conservation of <i>OsSPL14</i> across multiple crop species, identifying two critical motif regions essential for its transcriptional activity. Allelic diversity analysis identified GGC (H1) as the superior allelic combination, strongly associated with trait, spikelets per panicle for rice genotypes ARC 11768 and VANKALI, from subset of the 3&#xa0;K RG panel. Since the structural information for <i>OsSPL14</i> was very limited, computational analysis was employed with advanced bioinformatics software tools. Molecular docking of <i>OsSPL14</i> and phytohormones revealed strong interactions within them, characterized by strong binding affinities, suggesting a role in mediating hormone-driven plant responses. The study provides a computational framework for understanding the structural and functional roles of <i>OsSPL14</i>. Building on these insights, future investigations integrating CRISPR/Cas-mediated gene editing, functional genomics, and structural biology will be important to experimentally validate the predictions and translate them into practical breeding applications for rice yield improvement.</p>

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Exploring the evolutionary and functional complexity of OsSPL14 in enhancing rice improvement

  • 期刊论文

  • 发表日期：

    2026年1月7日

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  • Archana Thiruppathi,
  • Shubham Rajaram Salunkhe,
  • Williams Mohanavel,
  • Veera Ranjani Rajagopalan,
  • Sakthi Ambothi Rathnasamy,
  • Sudha Manickam,
  • Raveendran Muthurajan

摘要

Rice yield is a complex multigenic trait influenced by the interplay of multiple genes, understanding the underlying mechanisms of yield-related genes, such as OsSPL14, is essential for effective crop improvement strategies. OsSPL14 (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE14)/IDEAL PLANT ARCHITECTURE1 (IPA1) is a pleiotropic gene that plays a crucial role in determining plant architecture, contributing to increased grain yield in rice. As a transcription factor, OsSPL14 regulates not only genes related to yield but also those that influence plant growth, development, and immune responses. In the present study, an attempt has been made to understand the functional and structural roles of OsSPL14 in relation to rice yield and other related traits. Phylogenetic analysis and conserved motif identification established the evolutionary conservation of OsSPL14 across multiple crop species, identifying two critical motif regions essential for its transcriptional activity. Allelic diversity analysis identified GGC (H1) as the superior allelic combination, strongly associated with trait, spikelets per panicle for rice genotypes ARC 11768 and VANKALI, from subset of the 3 K RG panel. Since the structural information for OsSPL14 was very limited, computational analysis was employed with advanced bioinformatics software tools. Molecular docking of OsSPL14 and phytohormones revealed strong interactions within them, characterized by strong binding affinities, suggesting a role in mediating hormone-driven plant responses. The study provides a computational framework for understanding the structural and functional roles of OsSPL14. Building on these insights, future investigations integrating CRISPR/Cas-mediated gene editing, functional genomics, and structural biology will be important to experimentally validate the predictions and translate them into practical breeding applications for rice yield improvement.