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

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.

Transcriptional regulation of transcription factor genes WRI1 and LAFL during Brassica napus seed development.

The WRINKLED1 (WRI1) and LAFL [LEAFY COTYLEDON1 (LEC1), ABSCISIC ACID INSENSITIVE3 (ABI3), FUSCA3 (FUS3), and LEC2] transcription factors play essential roles in governing seed development and oil biosynthesis. To gain a comprehensive understanding of the transcriptional regulation of WRI1 and LAFL, we conducted genome-wide association studies for the expression profiles of WRI1 and LAFL in developing seeds at 20 and 40 days after flowering (DAF) using 302 rapeseed (Brassica napus) accessions. We identified a total of 237 expression quantitative trait nucleotides (eQTNs) and 51 expression QTN-by-environment interactions (eQEIs) associated with WRI1 and LAFL. Around these eQTNs and eQEIs, we pinpointed 41 and 8 candidate genes with known transcriptional regulations or protein interactions with their expression traits, respectively. Based on RNA-seq and ATAC-seq data, we employed the XGBoost and Basenji models which predicted 15 candidate genes potentially regulating the expression of WRI1 and LAFL. We further validated the predictions via tissue expression profile, haplotype analysis, and expression correlation analysis, and verified the transcriptional activation activity of BnaC03.MYB56 (R2R3-MYB transcription factor 56) on the expression of BnaA09.LEC1 by dual-luciferase reporter and yeast one-hybrid assays. BnaA10.AGL15 (AGAMOUS-LIKE 15), BnaC04.VAL1 (VIVIPAROUS1/ABSCISIC ACID INSENSITIVE3-LIKE 1), BnaC03.MYB56, and BnaA10.MYB56 were co-expressed with WRI1 and LAFL at 20 DAF in M35, a key module for seed development and oil biosynthesis. We further validated the positive regulation of MYB56 on seed oil accumulation using Arabidopsis (Arabidopsis thaliana) mutants. This study not only delivers a framework for future eQEI identification but also offers insights into the developmental regulation of seed oil accumulation.

Limited conservation in cross-species comparison of GLK transcription factor binding suggested wide-spread cistrome divergence.

Non-coding cis-regulatory variants in animal genomes are an important driving force in the evolution of transcription regulation and phenotype diversity. However, cistrome dynamics in plants remain largely underexplored. Here, we compare the binding of GOLDEN2-LIKE (GLK) transcription factors in tomato, tobacco, Arabidopsis, maize and rice. Although the function of GLKs is conserved, most of their binding sites are species-specific. Conserved binding sites are often found near photosynthetic genes dependent on GLK for expression, but sites near non-differentially expressed genes in the glk mutant are nevertheless under purifying selection. The binding sites' regulatory potential can be predicted by machine learning model using quantitative genome features and TF co-binding information. Our study show that genome cis-variation caused wide-spread TF binding divergence, and most of the TF binding sites are genetically redundant. This poses a major challenge for interpreting the effect of individual sites and highlights the importance of quantitatively measuring TF occupancy.

Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus.

BACKGROUND: Regulation of gene expression plays an essential role in controlling the phenotypes of plants. Brassica napus (B. napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B. napus. RESULTS: We perform a comprehensive analysis of the transcriptional variability in the seeds of B. napus at two developmental stages, 20 and 40 days after flowering (DAF). We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively. Among them, the local eQTLs are mapped to the adjacent genes more frequently. The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking. Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage. We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content. To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets. Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content. CONCLUSIONS: We comprehensively characterize the gene regulatory features in the seeds of B. napus and reveal the gene networks regulating the seed oil content of B. napus.

PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants.

Characterizing regulatory effects of genomic variants in plants remains a challenge. Although several tools based on deep-learning models and large-scale chromatin-profiling data have been available to predict regulatory elements and variant effects, no dedicated tools or web services have been reported in plants. Here, we present PlantDeepSEA as a deep learning-based web service to predict regulatory effects of genomic variants in multiple tissues of six plant species (including four crops). PlantDeepSEA provides two main functions. One is called Variant Effector, which aims to predict the effects of sequence variants on chromatin accessibility. Another is Sequence Profiler, a utility that performs 'in silico saturated mutagenesis' analysis to discover high-impact sites (e.g., cis-regulatory elements) within a sequence. When validated on independent test sets, the area under receiver operating characteristic curve of deep learning models in PlantDeepSEA ranges from 0.93 to 0.99. We demonstrate the usability of the web service with two examples. PlantDeepSEA could help to prioritize regulatory causal variants and might improve our understanding of their mechanisms of action in different tissues in plants. PlantDeepSEA is available at http://plantdeepsea.ncpgr.cn/.