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The function and regulation of lipid metabolic genes are essential for plant male reproduction. However, expression regulation of lipid metabolic genic male sterility (GMS) genes by noncoding RNAs is largely unclear. Here, we systematically predicted the microRNA regulators of 34 maize white brown complex members in ATP-binding cassette transporter G subfamily (WBC/ABCG) genes using transcriptome analysis. Results indicate that the ZmABCG26 transcript was predicted to be targeted by zma-miR164h-5p, and their expression levels were negatively correlated in maize B73 and Oh43 genetic backgrounds based on both transcriptome data and qRT-PCR experiments. CRISPR/Cas9-induced gene mutagenesis was performed on ZmABCG26 and another lipid metabolic gene, ZmFAR1. DNA sequencing, phenotypic, and cytological observations demonstrated that both ZmABCG26 and ZmFAR1 are GMS genes in maize. Notably, ZmABCG26 proteins are localized in the endoplasmic reticulum (ER), chloroplast/plastid, and plasma membrane. Furthermore, ZmFAR1 shows catalytic activities to three CoA substrates in vitro with the activity order of C12:0-CoA > C16:0-CoA > C18:0-CoA, and its four key amino acid sites were critical to its catalytic activities. Lipidomics analysis revealed decreased cutin amounts and increased wax contents in anthers of both zmabcg26 and zmfar1 GMS mutants. A more detailed analysis exhibited differential changes in 54 monomer contents between wild type and mutants, as well as between zmabcg26 and zmfar1. These findings will promote a deeper understanding of miRNA-regulated lipid metabolic genes and the functional diversity of lipid metabolic genes, contributing to lipid biosynthesis in maize anthers. Additionally, cosegregating molecular markers for ZmABCG26 and ZmFAR1 were developed to facilitate the breeding of male sterile lines.
Pubmed ID: 34360681 RIS Download
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A curated, open-source, integrated data resource for comparative functional genomics in crops and model plant species to facilitate the study of cross-species comparisons using information generated from projects supported by public funds. It currently hosts annotated whole genomes in over two dozen plant species and partial assemblies for almost a dozen wild rice species in the Ensembl browser, genetic and physical maps with genes, ESTs and QTLs locations, genetic diversity data sets, structure-function analysis of proteins, plant pathways databases (BioCyc and Plant Reactome platforms), and descriptions of phenotypic traits and mutations. The web-based displays for phenotypes include the Genes and Quantitative Trait Loci (QTL) modules. Sequence based relationships are displayed in the Genomes module using the genome browser adapted from Ensembl, in the Maps module using the comparative map viewer (CMap) from GMOD, and in the Proteins module displays. BLAST is used to search for similar sequences. Literature supporting all the above data is organized in the Literature database. In addition, Gramene now hosts a variety of web services including a Distributed Annotation Server (DAS), BLAST and a public MySQL database. Twice a year, Gramene releases a major build of the database and makes interim releases to correct errors or to make important updates to software and/or data. Additionally you can access Gramene through an FTP site.
View all literature mentionsSoftware ultrafast memory efficient tool for aligning sequencing reads. Bowtie is short read aligner.
View all literature mentionsThe Rfam database is a collection of RNA families, each represented by multiple sequence alignments, consensus secondary structures and covariance models (CMs). The families in Rfam break down into three broad functional classes: Non-coding RNA genes, structured cis-regulatory elements and self-splicing RNAs. Typically these functional RNAs often have a conserved secondary structure which may be better preserved than the RNA sequence. The CMs used to describe each family are a slightly more complicated relative of the profile hidden Markov models (HMMs) used by Pfam. CMs can simultaneously model RNA sequence and the structure in an elegant and accurate fashion. Rfam is also available via FTP. You can find data in Rfam in various ways... * Analyze your RNA sequence for Rfam matches * View Rfam family annotation and alignments * View Rfam clan details * Query Rfam by keywords * Fetch families or sequences by NCBI taxonomy * Enter any type of accession or ID to jump to the page for a Rfam family, sequence or genome
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