Transcriptomic analysis of the maize inbred line Chang7-2 and a large-grain mutant tc19

Abstract

Backgrounds Grain size is a key factor in crop yield that gradually develops after pollination. However, few studies have reported gene expression patterns in maize grain development using large-grain mutants. To investigate the developmental mechanisms of grain size, we analyzed a large-grain mutant, named tc19, at the morphological and transcriptome level at five stages corresponding to days after pollination (DAP). Results After maturation, the grain length, width, and thickness in tc19 were greater than that in Chang7-2 (control) and increased by 3.57, 8.80, and 3.88%, respectively. Further analysis showed that grain width and 100-kernel weight in tc19 was lower than in Chang7-2 at 14 and 21 DAP, but greater than that in Chang7-2 at 28 DAP, indicating that 21 to 28 DAP was the critical stage for kernel width and weight development. For all five stages, the concentrations of auxin and brassinosteroids were significantly higher in tc19 than in Chang7-2. Gibberellin was higher at 7, 14, and 21 DAP, and cytokinin was higher at 21 and 35 DAP, in tc19 than in Chang7-2. Through transcriptome analysis at 14, 21, and 28 DAP, we identified 2987, 2647 and 3209 differentially expressed genes (DEGs) between tc19 and Chang7-2. By using KEGG analysis, 556, 500 and 633 DEGs at 14, 21 and 28 DAP were pathway annotated, respectively, 77 of them are related to plant hormone signal transduction pathway. ARF3, AO2, DWF4 and XTH are higher expressed in tc19 than that in Chang7-2. Conclusions We found some DEGs in maize grain development by using Chang7-2 and a large-grain mutant tc19. These DEGs have potential application value in improving maize performance.

QTL mapping identifies novel major loci for kernel row number-associated ear fasciation, ear prolificacy and tillering in maize (Zea mays L.)

“…However, as shown in other studies ( Salvi et al., 2007 ; Li et al., 2012 ; Salvi and Tuberosa, 2015 ), QTLs are often due to gene expression level variation rather than variation of coding sequences, therefore quantification of the expression of ra1 in the ear primordium of Lo1016 and Lo964 will enable to test ra1 involvement in ear fasciation driven by qFas7 . The genes ct2 and bgh1 were identified as candidate genes for qFas1.2 ( Supplementary Table 6 ) based on former observations that maize lines carrying mutations at ct2 produced fasciated ears ( Bommert et al., 2013a ), and that the overexpression of bgh1 resulted in increased ear kernel row number ( Zhang et al., 2022 ). Five common native bgh1 alleles exhibited little structural and expression variation compared to the large increased expression conferred by these ectopic alleles ( Simmons et al., 2020 ).…”

Multiomics comparative analysis of the maize large grain mutant tc19 identified pathways related to kernel development

“…We previously identifed a large grain mutant tc19 , with different grain developmental rates to Chang7-2. First, we repeated the previous phenotype at two years and environments [ 22 ]. At 14 DAP, the average grain length of Chang7-2 and tc19 were respectively 5.81 and 5.31 mm (Fig.…”

“…Plant materials and phenotyping were performed same as previously [ 22 ]. Maize inbred line Chang7-2 and its large grain mutant tc19 were selected from the Maize Molecular Breeding Laboratory of Qingdao Agricultural University.…”

“…We previously identifed a large grain mutant tc19 on the background of the Chinese maize elite breeding line Chang7-2. We observed that tc19 shows different grain size and grain growth rates with Chang7-2 and identified several genes related to hormone signal pathways using transcriptomic analysis [ 22 ]. Here, by combining transcriptomic, proteomic and metabolomic analysis, we aim to identify new pathways and provide insights for maize grain development in tc19 .…”

Somatic Mutations in Fruit Trees: Causes, Detection Methods, and Molecular Mechanisms

“…Microarray analysis can compare the levels of gene expression between somatic mutations and their original variety, enabling the identification of candidate genes for large-scale somatic mutation screening in a cost-effective and efficient manner [ 39 , 40 ]. RNA-Seq can also compare gene expression levels at the transcriptome level, which can be used to select candidate gene regions for somatic mutation [ 41 , 42 , 43 ]. If certain genes are not expressed in mutants, it is possible to conduct additional experiments, such as comparing the nucleotide sequences of the targeted genomic DNA regions.…”