Rice yield is an important and complex agronomic trait controlled by multiple genes.In recent decades,dozens of yield-associated genes in rice have been cloned,many of which can increase production in the form of loss...Rice yield is an important and complex agronomic trait controlled by multiple genes.In recent decades,dozens of yield-associated genes in rice have been cloned,many of which can increase production in the form of loss or degeneration of function.However,mutations occurring randomly under natural conditions have provided very limited genetic resources for yield increases.In this study,potentially yield-increasing alleles of two genes closely associated with yield were edited artificially.The recently developed CRISPR/Cas9system was used to edit two yield genes:Grain number 1a(Gn1a)and DENSE AND ERECT PANICLE1(DEP1).Several mutants were identified by a target sequence analysis.Phenotypic analysis confirmed one mutant allele of Gn1a and three of DEP1 conferring yield superior to that conferred by other natural high-yield alleles.Our results demonstrate that favorable alleles of the Gnla and DEP1 genes,which are considered key factors in rice yield increases,could be developed by artificial mutagenesis using genome editing technology.展开更多
Significantly increasing crop yield is a major and worldwide challenge for food supply and security.It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide.Yet,the g...Significantly increasing crop yield is a major and worldwide challenge for food supply and security.It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide.Yet,the gene regulatory mechanism underpinning this ultrahigh yield has been a mystery.Here,we systematically collected the transcriptome data for seven key tissues at different developmental stages using rice cultivated both at Taoyuan as the case group and at another regular rice planting place Jinghong as the control group.We identified the top 24 candidate high-yield genes with their network modules from these well-designed datasets by developing a novel computational systems biology method,i.e.,dynamic cross-tissue(DCT)network analysis.We used one of the candidate genes,Os SPL4,whose function was previously unknown,for gene editing experimental validation of the high yield,and confirmed that Os SPL4 significantly affects panicle branching and increases the rice yield.This study,which included extensive field phenotyping,cross-tissue systems biology analyses,and functional validation,uncovered the key genes and gene regulatory networks underpinning the ultrahigh yield of rice.The DCT method could be applied to other plant or animal systems if different phenotypes under various environments with the common genome sequences of the examined sample.DCT can be downloaded from https://github.com/ztpub/DCT.展开更多
基金the Department of Sciences and Technology of Yunnan Province (2016BB001)the National Basic Research Program of China (2013CB835200)a Key Grant of Yunnan Provincial Science and Technology Department (2013GA004)
文摘Rice yield is an important and complex agronomic trait controlled by multiple genes.In recent decades,dozens of yield-associated genes in rice have been cloned,many of which can increase production in the form of loss or degeneration of function.However,mutations occurring randomly under natural conditions have provided very limited genetic resources for yield increases.In this study,potentially yield-increasing alleles of two genes closely associated with yield were edited artificially.The recently developed CRISPR/Cas9system was used to edit two yield genes:Grain number 1a(Gn1a)and DENSE AND ERECT PANICLE1(DEP1).Several mutants were identified by a target sequence analysis.Phenotypic analysis confirmed one mutant allele of Gn1a and three of DEP1 conferring yield superior to that conferred by other natural high-yield alleles.Our results demonstrate that favorable alleles of the Gnla and DEP1 genes,which are considered key factors in rice yield increases,could be developed by artificial mutagenesis using genome editing technology.
基金the National Basic Research Program of China(Grant No.2013CB835200)the National Key R&D Program of China(Grant No.2017YFA0505500)+4 种基金the Key Grant of Yunnan Provincial Science and Technology Department(Grant No.2013GA004)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB13040700)the National Natural Science Foundation of China(Grant Nos.11871456 and 31771476)the Shanghai Municipal Science and Technology Major Project(Grant No.2017SHZDZX01)the Open Research Fund of State Key Laboratory of Hybrid Rice(Wuhan University,Grant No.KF201806),China。
文摘Significantly increasing crop yield is a major and worldwide challenge for food supply and security.It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide.Yet,the gene regulatory mechanism underpinning this ultrahigh yield has been a mystery.Here,we systematically collected the transcriptome data for seven key tissues at different developmental stages using rice cultivated both at Taoyuan as the case group and at another regular rice planting place Jinghong as the control group.We identified the top 24 candidate high-yield genes with their network modules from these well-designed datasets by developing a novel computational systems biology method,i.e.,dynamic cross-tissue(DCT)network analysis.We used one of the candidate genes,Os SPL4,whose function was previously unknown,for gene editing experimental validation of the high yield,and confirmed that Os SPL4 significantly affects panicle branching and increases the rice yield.This study,which included extensive field phenotyping,cross-tissue systems biology analyses,and functional validation,uncovered the key genes and gene regulatory networks underpinning the ultrahigh yield of rice.The DCT method could be applied to other plant or animal systems if different phenotypes under various environments with the common genome sequences of the examined sample.DCT can be downloaded from https://github.com/ztpub/DCT.