Comparative global profiling of Perilla leaf and stem via transcriptomics and metabolomics.

Perilla (Perilla frutescens L.) is a time-honored herbal plant with widespread applications in both medicine and culinary practices around the world. Profiling the essential organs and tissues with medicinal significance on a global scale offers valuable insights for enhancing the yield of desirable compounds in Perilla and other medicinal plants. In the present study, genome-wide RNA-sequencing (RNA-seq) and assessing the global spectrum of metabolites were carried out in the two major organs/tissues of stem (PfST) and leaf (PfLE) in Perilla. The results showed a total of 18,490 transcripts as the DEGs (differentially expressed genes) and 144 metabolites as the DAMs (differentially accumulated metabolites) through the comparative profiling of PfST vs PfLE, and all the DEGs and DAMs exhibited tissue-specific trends. An association analysis between the transcriptomics and metabolomics revealed 14 significantly enriched pathways for both DEGs and DAMs, among which the pathways of Glycine, serine and threonine metabolism (ko00260), Glyoxylate and dicarboxylate metabolism (ko00630), and Glucagon signaling pathway (ko04922) involved relatively more DEGs and DAMs. The results of qRT-PCR assays of 18 selected DEGs confirmed the distinct tissue-specific characteristics of all identified DEGs between PfST and PfLE. Notably, all eight genes associated with the flavonoid biosynthesis/metabolism pathways exhibited significantly elevated expression levels in PfLE compared to PfST. This observation suggests a heightened accumulation of metabolites related to flavonoids in Perilla leaves. The findings of this study offer a comprehensive overview of the organs and tissues in Perilla that have medicinal significance.

Genome-Wide Identification of DOF Gene Family and the Mechanism Dissection of SbDof21 Regulating Starch Biosynthesis in Sorghum.

Starch is one of the main utilization products of sorghum (Sorghum bicolor L.), the fifth largest cereal crop in the world. Up to now, the regulation mechanism of starch biosynthesis is rarely documented in sorghum. In the present study, we identified 30 genes encoding the C2-C2 zinc finger domain (DOF), with one to three exons in the sorghum genome. The DOF proteins of sorghum were divided into two types according to the results of sequence alignment and evolutionary analysis. Based on gene expressions and co-expression analysis, we identified a regulatory factor, SbDof21, that was located on chromosome 5. SbDof21 contained two exons, encoding a 36.122 kD protein composed of 340 amino acids. SbDof21 co-expressed with 15 genes involved in the sorghum starch biosynthesis pathway, and the Pearson correlation coefficients (PCCs) with 11 genes were greater than 0.9. The results of qRT-PCR assays indicated that SbDof21 is highly expressed in sorghum grains, exhibiting low relative expression levels in the tissues of roots, stems and leaves. SbDOF21 presented as a typical DOF transcription factor (TF) that was localized to the nucleus and possessed transcriptional activation activity. Amino acids at positions 182-231 of SbDOF21 formed an important structure in its activation domain. The results of EMSA showed that SbDOF21 could bind to four tandem repeats of P-Box (TGTAAAG) motifs in vitro, such as its homologous proteins of ZmDOF36, OsPBF and TaPBF. Meanwhile, we also discovered that SbDOF21 could bind and transactivate SbGBSSI, a key gene in sorghum amylose biosynthesis. Collectively, the results of the present study suggest that SbDOF21 acts as an important regulator in sorghum starch biosynthesis, exhibiting potential values for the improvement of starch contents in sorghum.

Profiling of transcriptional regulators associated with starch biosynthesis in sorghum (Sorghum bicolor L.).

Starch presents as the major component of grain endosperm of sorghum (Sorghum bicolor L.) and other cereals, serving as the main energy supplier for both plants and animals, as well as important industrial raw materials of human beings, and was intensively concerned world widely. However, few documents focused on the pathway and transcriptional regulations of starch biosynthesis in sorghum. Here we presented the RNA-sequencing profiles of 20 sorghum tissues at different developmental stages to dissect key genes associated with sorghum starch biosynthesis and potential transcriptional regulations. A total of 1,708 highly expressed genes were detected, namely, 416 in grains, 736 in inflorescence, 73 in the stalk, 215 in the root, and 268 genes in the leaf. Besides, 27 genes encoded key enzymes associated with starch biosynthesis in sorghum were identified, namely, six for ADP-glucose pyrophosphorylase (AGPase), 10 for starch synthases (SSs), four for both starch-branching enzymes (SBE) and starch-debranching enzymes (DBEs), two for starch phosphorylases (SPs), and one for Brittle-1 (BT1). In addition, 65 transcription factors (TFs) that are highly expressed in endosperm were detected to co-express with 16 out of 27 genes, and 90 cis-elements were possessed by all 27 identified genes. Four NAC TFs were cloned, and the further assay results showed that three of them could in vitro bind to the CACGCAA motif within the promoters of SbBt1 and SbGBSSI, two key genes associated with starch biosynthesis in sorghum, functioning in similar ways that reported in other cereals. These results confirmed that sorghum starch biosynthesis might share the same or similar transcriptional regulations documented in other cereals, and provided informative references for further regulatory mechanism dissection of TFs involved in starch biosynthesis in sorghum.

Characterization and fine mapping of qkrnw4, a major QTL controlling kernel row number in maize.

KEY MESSAGE: A major QTL controlling kernel row number, qkrnw4, was identified by combining linkage analysis and association mapping. Within qkrnw4, on the basis of its expression and bioinformatics analysis, Zm00001d052910 was supposed to be the candidate gene for kernel row number. Kernel row number (KRN) is an important yield-related trait that affects kernel number in maize. Understanding the genetic basis of KRN is important for increasing maize yields. In the present study, by the use of a near-isogenic line (NIL) that has a B73 background and that consistently displays a low KRN across environments, qkrnw4, a major quantitative trait locus (QTL) associated with KRN within a yield trait-related QTL hotspot in bin 4.08, was finely mapped to an ~ 33-kb interval. Regional association analysis of a nested association mapping population comprising 5000 recombinant inbred lines revealed Zm00001d052910, which encodes a protein with an unknown function, as the important candidate gene responsible for qkrnw4. Different expression levels of this candidate gene in immature ears were detected between the NIL and its recurrent parent. Moreover, the expression of several auxin-related genes was consistent with that of the candidate gene. Furthermore, the potential associations of this candidate gene with well-known inflorescence-related genes were discussed. The results of this study provide important information for the genetic elucidation of KRN variation in maize.

Correlations and comparisons of quantitative trait loci with family per se and testcross performance for grain yield and related traits in maize.

Simultaneous improvement in grain yield and related traits in maize hybrids and their parents (inbred lines) requires a better knowledge of genotypic correlations between family per se performance (FP) and testcross performance (TP). Thus, to understand the genetic basis of yield-related traits in both inbred lines and their testcrosses, two F (2:3) populations (including 230 and 235 families, respectively) were evaluated for both FP and TP of eight yield-related traits in three diverse environments. Genotypic correlations between FP and TP, [Formula: see text] (FP, TP), were low (0-0.16) for grain yield per plant (GYPP) and kernel number per plant (KNPP) in the two populations, but relatively higher (0.32-0.69) for the other six traits with additive effects as the primary gene action. Similar results were demonstrated by the genotypic correlations between observed and predicted TP values based on quantitative trait loci positions and effects for FP, [Formula: see text] (M (FP), Y (TP)). A total of 88 and 35 QTL were detected with FP and TP, respectively, across all eight traits in the two populations. However, the genotypic variances explained by the QTL detected in the cross-validation analysis were much lower than those in the whole data set for all traits. Several common QTL between FP and TP that accounted for large phenotypic variances were clustered in four genomic regions (bin 1.10, 4.05-4.06, 9.02, and 10.04), which are promising candidate loci for further map-based cloning and improvement in grain yield in maize. Compared with publicly available QTL data, these QTL were also detected in a wide range of genetic backgrounds and environments in maize. These results imply that effective selection based on FP to improve TP could be achieved for traits with prevailing additive effects.

QTL analysis for yield components and kernel-related traits in maize across multi-environments.

Huangzaosi, Qi319, and Ye478 are foundation inbred lines widely used in maize breeding in China. To elucidate genetic base of yield components and kernel-related traits in these elite lines, two F(2:3) populations derived from crosses Qi319 × Huangzaosi (Q/H, 230 families) and Ye478 × Huangzaosi (Y/H, 235 families), as well as their parents were evaluated in six environments including Henan, Beijing, and Xinjiang in 2007 and 2008. Correlation and hypergeometric probability function analyses showed the dependence of yield components on kernel-related traits. Three mapping procedures were used to identify quantitative trait loci (QTL) for each population: (1) analysis for each of the six environments, (2) joint analysis for each of the three locations across 2 years, and (3) joint analysis across all environments. For the eight traits measured, 90, 89, and 58 QTL for Q/H, and 72, 76, and 51 QTL for Y/H were detected by the three QTL mapping procedures, respectively. About 70% of the QTL from Q/H and 90% of the QTL from Y/H did not show significant QTL × environment interactions in the joint analysis across all environments. Most of the QTL for kernel traits exhibited high stability across 2 years at the same location, even across different locations. Seven major QTL detected under at least four environments were identified on chromosomes 1, 4, 6, 7, 9, and 10 in the populations. Moreover, QTL on chr. 1, chr. 4, and chr. 9 were detected in both populations. These chromosomal regions could be targets for marker-assisted selection, fine mapping, and map-based cloning in maize.