The importance of microRNA （miRNA） at the post-transcriptional regulation level has recently been recognized in both animals and plants. In recent years, many studies focused on miRNA target identification and funct...The importance of microRNA （miRNA） at the post-transcriptional regulation level has recently been recognized in both animals and plants. In recent years, many studies focused on miRNA target identification and functional analysis. However, little is known about the transcription and regulation of miRNAs themselves. In this study, the transcription start sites （TSSs） for 11 miRNA primary transcripts of soybean from 11 miRNA loci （of 50 loci tested） were cloned by a 5＂ rapid amplification of cDNA ends （5＂ RACE） procedure using total RNA from 30-d-old seedlings. The features consistent with a RNA polymerase II mechanism of transcription were found among these miRNA loci. A position weight matrix algorithm was used to identify conserved motifs in miRNA core promoter regions. A canonical TATA box motif was identified upstream of the major start site at 8 （76%） of the mapped miRNA loci. Several cis-acting elements were predicted in the 2 kb 5＂ to the TSSs. Potential spatial and temporal expression patterns of the miRNAs were found. The target genes for these miRNAs were also predicted and further elucidated for the potential function of the miRNAs. This research provides a molecular basis to explore regulatory mechanisms of miRNA expression, and a way to understand miRNA-mediated regulatory pathways and networks in soybean.展开更多
Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome,which has an important impact on gene expression,transcriptional regulation,and phenotypic traits.To da...Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome,which has an important impact on gene expression,transcriptional regulation,and phenotypic traits.To date,several methods have been developed for predicting gene expression.However,existing methods do not take into consideration the effect of chromatin interactions on target gene expression,thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements.In this study,we developed a highly accurate deep learning-based gene expression prediction model(DeepCBA)based on maize chromatin interaction data.Compared with existing models,DeepCBA exhibits higher accuracy in expression classification and expression value prediction.The average Pearson correlation coefficients(PCCs)for predicting gene expression using gene promoter proximal interactions,proximaldistal interactions,and both proximal and distal interactions were 0.818,0.625,and 0.929,respectively,representing an increase of 0.357,0.16,and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences.Some important motifs were identified through DeepCBA;they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity.Importantly,experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression.Moreover,promoter editing and verification of two reported genes(ZmCLE7 and ZmVTE4)demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding.DeepCBA is available at http://www.deepcba.com/or http://124.220.197.196/.展开更多
基金supported by the National High-Tech R&D Program of China (2006AA10Z1F1)the National Core Soybean Genetic Engineering Project, China(2011ZX08004-002)+3 种基金the National Natural Science Foundation of China (60932008, 30971810)the National Basic Research Program of China (2009CB118400)the Ministry of Education Innovation Team of Soybean Molecular Design,Chinathe Innovation Team of the Education Bureau of Heilongjiang Province, China
文摘The importance of microRNA （miRNA） at the post-transcriptional regulation level has recently been recognized in both animals and plants. In recent years, many studies focused on miRNA target identification and functional analysis. However, little is known about the transcription and regulation of miRNAs themselves. In this study, the transcription start sites （TSSs） for 11 miRNA primary transcripts of soybean from 11 miRNA loci （of 50 loci tested） were cloned by a 5＂ rapid amplification of cDNA ends （5＂ RACE） procedure using total RNA from 30-d-old seedlings. The features consistent with a RNA polymerase II mechanism of transcription were found among these miRNA loci. A position weight matrix algorithm was used to identify conserved motifs in miRNA core promoter regions. A canonical TATA box motif was identified upstream of the major start site at 8 （76%） of the mapped miRNA loci. Several cis-acting elements were predicted in the 2 kb 5＂ to the TSSs. Potential spatial and temporal expression patterns of the miRNAs were found. The target genes for these miRNAs were also predicted and further elucidated for the potential function of the miRNAs. This research provides a molecular basis to explore regulatory mechanisms of miRNA expression, and a way to understand miRNA-mediated regulatory pathways and networks in soybean.
基金supported by the Biological Breeding-Major Projects(2023ZD04076)the National Key Research and Development Program of China(2022YFD1201504)+3 种基金the Fundamental Research Funds for the Central Universities(2662022YLYJ010,2021ZKPY018,2662021JC008,and SZYJY2021003)the Major Project of Hubei Hongshan Laboratory(2022HSZD031)the Major Science and Technology Project of Hubei Province(2021AFB002)the Yingzi Tech&Huazhong Agricultural University Intelligent Research Institute of Food Health(IRIFH202209).
文摘Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome,which has an important impact on gene expression,transcriptional regulation,and phenotypic traits.To date,several methods have been developed for predicting gene expression.However,existing methods do not take into consideration the effect of chromatin interactions on target gene expression,thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements.In this study,we developed a highly accurate deep learning-based gene expression prediction model(DeepCBA)based on maize chromatin interaction data.Compared with existing models,DeepCBA exhibits higher accuracy in expression classification and expression value prediction.The average Pearson correlation coefficients(PCCs)for predicting gene expression using gene promoter proximal interactions,proximaldistal interactions,and both proximal and distal interactions were 0.818,0.625,and 0.929,respectively,representing an increase of 0.357,0.16,and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences.Some important motifs were identified through DeepCBA;they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity.Importantly,experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression.Moreover,promoter editing and verification of two reported genes(ZmCLE7 and ZmVTE4)demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding.DeepCBA is available at http://www.deepcba.com/or http://124.220.197.196/.
