An unconventional proanthocyanidin pathway in maize

Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize

BACKGROUND

Drought is a major abiotic stress in sub-Saharan Africa, impacting maize growth and development leading to severe yield loss. Drought tolerance is a complex trait regulated by multiple genes, making direct grain yield selection ineffective. To dissect the genetic architecture of grain yield and flowering traits under drought stress, a genome-wide association study (GWAS) was conducted on a panel of 236 maize lines testcrossed and evaluated under managed drought and optimal growing conditions in multiple environments using seven multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, ISIS EM-BLASSO, and FARMCPU) from mrMLM and GAPIT R packages. Genomic prediction with RR-BLUP model was applied on BLUEs across locations under optimum and drought conditions.

RESULTS

A total of 172 stable and reliable quantitative trait nucleotides (QTNs) were identified, of which 77 are associated with GY, AD, SD, ASI, PH, EH, EPO and EPP under drought and 95 are linked to GY, AD, SD, ASI, PH, EH, EPO and EPP under optimal conditions. Among these QTNs, 17 QTNs explained over 10% of the phenotypic variation (R2 ≥ 10%). Furthermore, 43 candidate genes were discovered and annotated. Two major candidate genes, Zm00001eb041070 closely associated with grain yield near peak QTN, qGY_DS1.1 (S1_216149215) and Zm00001eb364110 closely related to anthesis-silking interval near peak QTN, qASI_DS8.2 (S8_167256316) were identified, encoding AP2-EREBP transcription factor 60 and TCP-transcription factor 20, respectively under drought stress. Haplo-pheno analysis identified superior haplotypes for qGY_DS1.1 (S1_216149215) associated with the higher grain yield under drought stress. Genomic prediction revealed moderate to high prediction accuracies under optimum and drought conditions.

CONCLUSION

The lines carrying superior haplotypes can be used as potential donors in improving grain yield under drought stress. Integration of genomic selection with GWAS results leads not only to an increase in the prediction accuracy but also to validate the function of the identified candidate genes as well increase in the accumulation of favorable alleles with minor and major effects in elite breeding lines. This study provides valuable insight into the genetic architecture of grain yield and secondary traits under drought stress.

Evolutionary trajectory of transcription factors and selection of targets for metabolic engineering

Transcription factors (TFs) provide potentially powerful tools for plant metabolic engineering as they often control multiple genes in a metabolic pathway. However, selecting the best TF for a particular pathway has been challenging, and the selection often relies significantly on phylogenetic relationships. Here, we offer examples where evolutionary relationships have facilitated the selection of the suitable TFs, alongside situations where such relationships are misleading from the perspective of metabolic engineering. We argue that the evolutionary trajectory of a particular TF might be a better indicator than protein sequence homology alone in helping decide the best targets for plant metabolic engineering efforts. This article is part of the theme issue 'The evolution of plant metabolism'.

Integrated Metabolomics and Transcriptomics Provide Key Molecular Insights into Floral Stage-Driven Flavonoid Pathway in Safflower

Safflower (Carthamus tinctorius L.) is a traditional Chinese medicinal herb renowned for its high flavonoid content and significant medicinal value. However, the dynamic changes in safflower petal flavonoid profiles across different flowering phases present a challenge in optimizing harvest timing and medicinal use. To enhance the utilization of safflower, this study conducted an integrated transcriptomic and metabolomic analysis of safflower petals at different flowering stages. Our findings revealed that certain flavonoids were more abundant during the fading stage, while others peaked during full bloom. Specifically, seven metabolites, including p-coumaric acid, naringenin chalcone, naringenin, dihydrokaempferol, apigenin, kaempferol, and quercetin, accumulated significantly during the fading stage. In contrast, dihydromyricetin and delphinidin levels were notably reduced. Furthermore, key genes in the flavonoid biosynthesis pathway, such as 4CL, DFR, and ANR, exhibited up-regulated expression with safflower's flowering progression, whereas CHI, F3H, and FLS were down-regulated. Additionally, exposure to UV-B stress at full bloom led to an up-regulation of flavonoid content and altered the expression of key flavonoid biosynthetic genes over time. This study not only elucidates the regulatory mechanisms underlying flavonoid metabolism in safflower but also provides insights for maximizing its medicinal and industrial applications.

Beyond pathways: Accelerated flavonoids candidate identification and novel exploration of enzymatic properties using combined mapping populations of wheat

Although forward-genetics-metabolomics methods such as mGWAS and mQTL have proven effective in providing myriad loci affecting metabolite contents, they are somehow constrained by their respective constitutional flaws such as the hidden population structure for GWAS and insufficient recombinant rate for QTL. Here, the combination of mGWAS and mQTL was performed, conveying an improved statistical power to investigate the flavonoid pathways in common wheat. A total of 941 and 289 loci were, respectively, generated from mGWAS and mQTL, within which 13 of them were co-mapped using both approaches. Subsequently, the mGWAS or mQTL outputs alone and their combination were, respectively, utilized to delineate the metabolic routes. Using this approach, we identified two MYB transcription factor encoding genes and five structural genes, and the flavonoid pathway in wheat was accordingly updated. Moreover, we have discovered some rare-activity-exhibiting flavonoid glycosyl- and methyl-transferases, which may possess unique biological significance, and harnessing these novel catalytic capabilities provides potentially new breeding directions. Collectively, we propose our survey illustrates that the forward-genetics-metabolomics approaches including multiple populations with high density markers could be more frequently applied for delineating metabolic pathways in common wheat, which will ultimately contribute to metabolomics-assisted wheat crop improvement.

Revisiting decade-old questions in proanthocyanidin biosynthesis: current understanding and new challenges

Proanthocyanidins (PAs), one of the most abundant natural polymers found in plants, are gaining increasing attention because of their beneficial effects for agriculture and human health. The study of PA biosynthesis has been active for decades, and progress has been drastically accelerated since the discovery of key enzymes such as Anthocyanidin Reductase (ANR), Leucoanthocyanidin Reductase (LAR), and key transcription factors such as Transparent Testa 2 (TT2) and Transparent Testa 8 (TT8) in the early 2000s. Scientists raised some compelling questions regarding PA biosynthesis about two decades ago in the hope that addressing these questions would lead to an enhanced understanding of PA biosynthesis in plants. These questions focus on the nature of starter and extension units for PA biosynthesis, the stereochemistry of PA monomers and intermediates, and how and where the polymerization or condensation steps work subcellularly. Here, I revisit these long-standing questions and provide an update on progress made toward answering them. Because of advanced technologies in genomics, bioinformatics and metabolomics, we now have a much-improved understanding of functionalities of key enzymes and identities of key intermediates in the PA biosynthesis and polymerization pathway. Still, several questions, particularly the ones related to intracellular PA transportation and deposition, as well as enzyme subcellular localization, largely remain to be explored. Our increasing understanding of PA biosynthesis in various plant species has led to a new set of compelling open questions, suggesting future research directions to gain a more comprehensive understanding of PA biosynthesis.

Identification of Yellow Seed Color Genes Using Bulked Segregant RNA Sequencing in Brassica juncea L

Yellow seed breeding is an effective method to improve oil yield and quality in rapeseed (Brassica napus L.). However, naturally occurring yellow-seeded genotypes have not been identified in B. napus. Mustard (Brassica juncea L.) has some natural, yellow-seeded germplasms, yet the molecular mechanism underlying this trait remains unclear. In this study, a BC9 population derived from the cross of yellow seed mustard "Wuqi" and brown seed mustard "Wugong" was used to analyze the candidate genes controlling the yellow seed color of B. juncea. Subsequently, yellow-seeded (BY) and brown-seeded (BB) bulks were constructed in the BC9 population and subjected to bulked segregant RNA sequencing (BSR-Seq). A total of 511 differentially expressed genes (DEGs) were identified between the brown and yellow seed bulks. Enrichment analysis revealed that these DEGs were involved in the phenylpropanoid biosynthetic process and flavonoid biosynthetic process, including key genes such as 4CL, C4H, LDOX/TT18, PAL1, PAL2, PAL4, TT10, TT12, TT4, TT8, BAN, DFR/TT3, F3H/TT6, TT19, and CHI/TT5. In addition, 111,540 credible single-nucleotide polymorphisms (SNPs) and 86,319 INDELs were obtained and used for quantitative trait locus (QTL) identification. Subsequently, two significant QTLs on chromosome A09, namely, qSCA09-3 and qSCA09-7, were identified by G' analysis, and five DEGs (BjuA09PAL2, BjuA09TT5, BjuA09TT6, BjuA09TT4, BjuA09TT3) involved in the flavonoid pathway were identified as hub genes based on the protein-to-protein network. Among these five genes, only BjuA09PAL2 and BjuA09F3H had SNPs between BY and BB bulks. Interestingly, the majority of SNPs in BjuA09PAL2 were consistent with the SNPs identified between the high-quality assembled B. juncea reference genome "T84-66" (brown-seed) and "AU213" (yellow-seed). Therefore, BjuA09PAL2, which encodes phenylalanine lyase, was considered as the candidate gene associated with yellow seed color of B. juncea. The identification of a novel gene associated with the yellow seed coloration of B. juncea through this study may play a significant role in enhancing yellow seed breeding in rapeseed.