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Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits.

Tao ZhuChunjiao XiaRanran YuXinkai ZhouXingbing XuLin WangZhanxiang ZongJunjiao YangYinmeng LiuLuchang MingYuxin YouDijun ChenWeibo Xie
Published in: Nature communications (2024)
Unraveling the regulatory mechanisms that govern complex traits is pivotal for advancing crop improvement. Here we present a comprehensive regulome atlas for rice (Oryza sativa), charting the chromatin accessibility across 23 distinct tissues from three representative varieties. Our study uncovers 117,176 unique open chromatin regions (OCRs), accounting for ~15% of the rice genome, a notably higher proportion compared to previous reports in plants. Integrating RNA-seq data from matched tissues, we confidently predict 59,075 OCR-to-gene links, with enhancers constituting 69.54% of these associations, including many known enhancer-to-gene links. Leveraging this resource, we re-evaluate genome-wide association study results and discover a previously unknown function of OsbZIP06 in seed germination, which we subsequently confirm through experimental validation. We optimize deep learning models to decode regulatory grammar, achieving robust modeling of tissue-specific chromatin accessibility. This approach allows to predict cross-variety regulatory dynamics from genomic sequences, shedding light on the genetic underpinnings of cis-regulatory divergence and morphological disparities between varieties. Overall, our study establishes a foundational resource for rice functional genomics and precision molecular breeding, providing valuable insights into regulatory mechanisms governing complex traits.
Keyphrases
  • genome wide
  • transcription factor
  • single cell
  • rna seq
  • gene expression
  • copy number
  • dna methylation
  • deep learning
  • genome wide association study
  • electronic health record
  • high resolution
  • minimally invasive
  • healthcare