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

Tao Zhu, Chunjiao Xia, Ranran Yu, Xinkai Zhou, Xingbing Xu, Lin Wang, Zhanxiang Zong, Junjiao Yang, Yinmeng Liu, Luchang Ming, Y. Nancy You, Dijun Chen, Weibo Xie

2024Nature Communications25 citationsDOIOpen Access PDF

Abstract

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. The authors present a comprehensive regulome atlas for rice by mapping chromatin accessibility across 23 tissues from three rice varieties. By integrating matched RNA-seq data, they accurately predict numerous associations between non-coding regulatory elements and target genes. This study provides a foundational resource for gene editing and breeding strategies targeting non-coding regions in rice, potentially advancing crop improvement efforts.

Topics & Concepts

Computational biologyArchitectureComputer scienceBiologyGeographyArchaeologyGenetic Mapping and Diversity in Plants and AnimalsRice Cultivation and Yield ImprovementGABA and Rice Research