A NAC-EXPANSIN module enhances maize kernel size by controlling nucellus elimination

Maize early endosperm development is initiated in coordination with elimination of maternal nucellar tissues. However, the underlying mechanisms are largely unknown. Here, we characterize a major quantitative trait locus for maize kernel size and weight that encodes an EXPANSIN gene, ZmEXPB15. The encoded β-expansin protein is expressed specifically in nucellus, and positively controls kernel size and weight by promoting nucellus elimination. We further show that two nucellus-enriched transcription factors (TFs), ZmNAC11 and ZmNAC29, activate ZmEXPB15 expression. Accordingly, these two TFs also promote kernel size and weight through nucellus elimination regulation, and genetic analyses support their interaction with ZmEXPB15. Importantly, hybrids derived from a ZmEXPB15 overexpression line have increased kernel weight, demonstrates its potential value in breeding. Together, we reveal a pathway modulating the cellular processes of maternal nucellus elimination and early endosperm development, and an approach to improve kernel weight

A Mitochondrial Transcription Termination Factor, ZmSmk3, Is Required for nad1 Intron4 and nad4 Intron1 Splicing and Kernel Development in Maize

The expression systems of the mitochondrial genes are derived from their bacterial ancestors, but have evolved many new features in their eukaryotic hosts. Mitochondrial RNA splicing is a complex process regulated by families of nucleus-encoded RNA-binding proteins, few of which have been characterized in maize (Zea mays L.). Here, we identified the Zea mays small kernel 3 (Zmsmk3) candidate gene, which encodes a mitochondrial transcription termination factor (mTERF) containing two mTERF motifs, which is conserved in monocotyledon; and the target introns were also quite conserved during evolution between monocotyledons and dicotyledons. The mutations of Zmsmk3 led to arrested embryo and endosperm development, resulting in small kernels. A transcriptome of 12 days after pollination endosperm analysis revealed that the starch biosynthetic pathway and the zein gene family were down-regulated in the Zmsmk3 mutant kernels. ZmSMK3 is localized in mitochondria. The reduced expression of ZmSmk3 in the mutant resulted in the splicing deficiency of mitochondrial nad4 intron1 and nad1 intron4, causing a reduction in complex I assembly and activity, impairing mitochondria structure and activating the alternative respiratory pathway. So, the results suggest that ZmSMK3 is required for the splicing of nad4 intron 1 and nad1 intron 4, complex I assembly and kernel development in maize

Transcriptome Analysis Reveals Genes of Flooding-Tolerant and Flooding-Sensitive Rapeseeds Differentially Respond to Flooding at the Germination Stage

Flooding results in significant crop yield losses due to exposure of plants to hypoxic stress. Various studies have reported the effect of flooding stress at seedling establishment or later stages. However, the molecular mechanism prevailing at the germination stage under flooding stress remains enigmatic. The present study highlights the comparative transcriptome analysis in two rapeseed lines, i.e., flooding-tolerant (Santana) and -sensitive (23651) lines under control and 6-h flooding treatments at the germination stage. A total of 1840 up-regulated and 1301 down-regulated genes were shared by both lines in response to flooding. There were 4410 differentially expressed genes (DEGs) with increased expression and 4271 DEGs with reduced expression shared in both control and flooding conditions. Gene ontology (GO) enrichment analysis revealed that “transcription regulation”, “structural constituent of cell wall”, “reactive oxygen species metabolic”, “peroxidase”, oxidoreductase”, and “antioxidant activity” were the common processes in rapeseed flooding response. In addition, the processes such as “hormone-mediated signaling pathway”, “response to organic substance response”, “motor activity”, and “microtubule-based process” are likely to confer rapeseed flooding resistance. Mclust analysis clustered DEGs into nine modules; genes in each module shared similar expression patterns and many of these genes overlapped with the top 20 DEGs in some groups. This work provides a comprehensive insight into gene responses and the regulatory network in rapeseed flooding stress and provides guidelines for probing the underlying molecular mechanisms in flooding resistance

Analysis of Transcriptome and Expression of <i>C4H</i> and <i>FLS</i> Genes on Four Flower Colors of <i>Impatiens uliginosa</i>

Flower color is a major feature of ornamental plants, and the rich flower color of plants is an important factor in determining their ornamental and economic values, so flower color is an important research target for gardening and horticulture breeders at home and abroad. Our research group collected four colors of Impatiens uliginosa (white, pink, red, and deep red) during the collection of germplasm resources in the field. In this study, we analyzed the transcriptomes of the four flower colors of I. uliginosa by using RNA-Seq technology. The transcriptomes were screened to identify candidate genes related to flower color, and the coloring mechanisms of four flower colors were revealed at the molecular level. The main findings were as follows: (1) The number of the four different transcripts ranged from 64,723 to 93,522 and contained a total of 100,705 unigenes. (2) The analysis of differentially expressed genes revealed structural genes including C4H, FLS, PAL, and ANS and transcription factors including MYB, MYB-related, AP2-EREBP, and bHLH. (3) Among the four flower colors of I. uliginosa, the C4H1 gene had the highest expression in pink flowers, and the C4H2 gene had the highest expression in red flowers. This indicated that C4H genes positively regulated the red flower color of I. uliginosa. However, FLS expression was the highest in white flowers, and with deepening flower color, FLS gene expression gradually weakened, acting as a negative regulator. The results of this study could lay the theoretical foundation for investigating the mechanism of coloration and flower color variation in I. uliginosa

Characterization of novel loci controlling seed oil content in Brassica napus by marker metabolite-based multi-omics analysis

Abstract Background Seed oil content is an important agronomic trait of Brassica napus (B. napus), and metabolites are considered as the bridge between genotype and phenotype for physical traits. Results Using a widely targeted metabolomics analysis in a natural population of 388 B. napus inbred lines, we quantify 2172 metabolites in mature seeds by liquid chromatography mass spectrometry, in which 131 marker metabolites are identified to be correlated with seed oil content. These metabolites are then selected for further metabolite genome-wide association study and metabolite transcriptome-wide association study. Combined with weighted correlation network analysis, we construct a triple relationship network, which includes 21,000 edges and 4384 nodes among metabolites, metabolite quantitative trait loci, genes, and co-expression modules. We validate the function of BnaA03.TT4, BnaC02.TT4, and BnaC05.UK, three candidate genes predicted by multi-omics analysis, which show significant impacts on seed oil content through regulating flavonoid metabolism in B. napus. Conclusions This study demonstrates the advantage of utilizing marker metabolites integrated with multi-omics analysis to dissect the genetic basis of agronomic traits in crops

Additional file 1 of Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus

Additional file 1: Fig. S1. Overview of experimental and research analysis methods. Fig. S2. Venn diagram of the distribution of genes regulated by different types of eQTLs (local eQTL and distant eQTL) at 20 DAF and 40 DAF. (a) Distribution number of genes which were regulated by different types of eQTLs at 20 DAF. (b) Distribution number of genes which were regulated by different types of eQTLs at 40 DAF. Fig. S3. Manhattan plot of BnaA05.FAD7 eGWAS at 40 DAF. Fig. S4. Study design on ATAC-seq of 6 representative accessions of B. napus. Fig. S5. Correlation analysis of 59 ATAC-seq samples. The samples are named according to “(22, 26, 34 or 40) DAF” + “accession_cellular ploidy (2C, 3C or 4C)” + “biological replicate” format naming. Fig. S6. Regional plot of ATAC-seq data and eGWAS results of BnaA08.TGD1. BnaA08.TGD1 is marked by a dashed line. The shaded area indicates the lead SNP of the local eQTL affecting BnaA08.TGD1. Fig. S7. Comparison of the explained variance (r2) of eQTLs for expression variation in or not in OCRs. Fig. S8. Expression correlation analysis of adjacent genes and randomly sampled gene pairs. Violin plot shows that the expression correlation of adjacent genes is significantly higher than that of randomly sampled gene pairs, *** indicates P 0.05, * indicates P < 0.05, ** indicates P < 0.01 and *** indicates P < 0.001 compared with WT in Student’s t test. Fig. S34. Transcriptional regulation of genes are activated by BnaA09.SCL31 or BnaA07.NAC13. a Schematic representation of the constructs used for the dual-luciferase assay. The effector constructs contain BnaA09.SCL31 and BnaA07.NAC13 driven by the CaMV35S promoter, respectively. The reporter construct contains the firefly luciferase driven by BnaA07.NAC13, BnaC07.LPAAT, BnaC03.SLP1 and BnaA07.SRO3 promoter, and the Renilla luciferase (REN) driven by the CaMV35S promoter. And the black square is the terminator. b Bar graph showing the relative LUC/REN ratio in the dual-luciferase assay. Values are means(SD) (n = 3 biological repeats). BnaA07.SRO3 (SIMILAR TO RCD ONE 3)

Additional file 3 of Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus

Additional file 3. Review history

Additional file 2 of Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus

Additional file 2: Table S1. Genome-wide eQTLs identified at 20 DAF. Table S2. Genome-wide eQTLs identified at 40 DAF. Table S3. Genome-wide eGene-eQTLs identified at 20 DAF. Table S4. Genome-wide eGene-eQTLs identified at 40 DAF. Table S5. Results of GO enrichment analysis of specifically identified eGenes at 20 DAF. Table S6. Results of GO enrichment analysis of specifically identified eGenes at 40 DAF. Table S7. Summary of ATAC-Seq data. Table S8. List of homoeologous genes among subgenomes. Table S9. List of homoeologous genes on gene expression trend and regulation at 20 DAF. Table S10. List of homoeologous genes on gene expression trend and regulation at 40 DAF. Table S11. The information of hotspots at 20 DAF. Table S12. The information of hotspots at 40 DAF. Table S13. The list of TWAS significant genes of SOC and SGC at 20 DAF. Table S14. The list of TWAS significant genes of SOC and SGC at 40 DAF. Table S15. Enrichment analysis of eQTL hotspot regulatory genes in TWAS significant genes of SOC at 20 DAF. Table S16. Enrichment analysis of eQTL hotspot regulatory genes in TWAS significant genes of SOC at 40 DAF. Table S17. The results of Tomtom analysis of key sequences identified by Basenji module. Table S18. Primers for gene cloning and PCR confirmation