Bract suppression regulated by the miR156/529-SPLs-NL1-PLA1 module is required for the transition from vegetative to reproductive branching in rice

Reproductive transition of grasses is characterized by switching the pattern of lateral branches,featuring the suppression of outgrowth of the subtending leaves(bracts)and rapid formation of higher-order branches in the inflorescence(panicle).However,the molecular mechanisms underlying such changes remain largely unknown.Here,we show that bract suppression is required for the reproductive branching in rice.We identified a pathway involving the intrinsic time ruler microRNAI56/529,their targets SQUAMOSA PROMOTER BINDING PROTEIN LIKE(SPL)genes,NECK LEAF1(NL1),and PLASTOCHRON1(PLA1),which regulates the bract outgrowth and thus affects the pattern switch between vegetative and reproductive branching.Suppression of the bract results in global reprogramming of transcriptome and chromatin accessibility following the reproductive transition,while these processes are largely dysregu-lated in the mutants of these genes.These discoveries contribute to our understanding of the dynamic plant architecture and provide novel insights for improving crop yields.

An inferred functional impact map of genetic variants in rice

Interpreting the functional impacts of genetic variants(GVs)is an important challenge for functional genomic studies in crops and next-generation breeding.Previous studies in rice(Oryza sativa)have focused mainly on the identification of GVs,whereas systematic functional annotation of GVs has not yet been performed.Here,we present a functional impact map of GVs in rice.We curated haplotype information for 17397026 GVs from sequencing data of 4726 rice accessions.We quantitatively evaluated the effects of missense mutations in coding regions in each haplotype based on the conservation of amino acid residues and obtained the effects of 918848 non-redundant missense GVs.Furthermore,we generated high-quality chromatin accessibility(CA)data from six representative rice tissues and used these data to train deep convolutional neural network models to predict the impacts of 5067405 GVs for CA in regulatory regions.We characterized the functional properties and tissue specificity of the GV effects and found that large-effect GVs in coding and regulatory regions may be subject to selection in different directions.Finally,we demonstrated how the functional impact map could be used to prioritize causal variants in mapping populations.This impact map will be a useful resource for accelerating gene cloning and functional studies in rice,and can be freely queried in RiceVarMap V2.0(http://ricevarmap.ncpgr.cn).