Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.)

Phenolic amides (avenanthramides) in oats - an update review

Oats (Avena sativa L.) are one of the worldwide cereal crops. Avenanthramides (AVNs), the unique plant alkaloids of secondary metabolites found in oats, are nutritionally important for humans and animals. Numerous bioactivities of AVNs have been investigated and demonstrated in vivo and in vitro. Despite all these, researchers from all over the world are taking efforts to learn more knowledge about AVNs. In this work, we highlighted the recent updated findings that have increased our understanding of AVNs bioactivity, distribution, and especially the AVNs biosynthesis. Since the limits content of AVNs in oats strictly hinders the demand, understanding the mechanisms underlying AVN biosynthesis is important not only for developing a renewable, sustainable, and environmentally friendly source in both plants and microorganisms but also for designing effective strategies for enhancing their production via induction and metabolic engineering. Future directions for improving AVN production in native producers and heterologous systems for food and feed use are also discussed. This summary will provide a broad view of these specific natural products from oats.

Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and burdensome. Multi-trait genomic selection (MTGS) combines information from genome-wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.

Phenotypic characterization and candidate gene analysis of a short kernel and brassinosteroid insensitive mutant from hexaploid oat (Avena sativa)

In an ethyl methanesulfonate oat (Avena sativa) mutant population we have found a mutant with striking differences to the wild-type (WT) cv. Belinda. We phenotyped the mutant and compared it to the WT. The mutant was crossed to the WT and mapping-by-sequencing was performed on a pool of F2 individuals sharing the mutant phenotype, and variants were called. The impacts of the variants on genes present in the reference genome annotation were estimated. The mutant allele frequency distribution was combined with expression data to identify which among the affected genes was likely to cause the observed phenotype. A brassinosteroid sensitivity assay was performed to validate one of the identified candidates. A literature search was performed to identify homologs of genes known to be involved in seed shape from other species. The mutant had short kernels, compact spikelets, altered plant architecture, and was found to be insensitive to brassinosteroids when compared to the WT. The segregation of WT and mutant phenotypes in the F2 population was indicative of a recessive mutation of a single locus. The causal mutation was found to be one of 123 single-nucleotide polymorphisms (SNPs) spanning the entire chromosome 3A, with further filtering narrowing this down to six candidate genes. In-depth analysis of these candidate genes and the brassinosteroid sensitivity assay suggest that a Pro303Leu substitution in AVESA.00010b.r2.3AG0419820.1 could be the causal mutation of the short kernel mutant phenotype. We identified 298 oat proteins belonging to orthogroups of previously published seed shape genes, with AVESA.00010b.r2.3AG0419820.1 being the only of these affected by a SNP in the mutant. The AVESA.00010b.r2.3AG0419820.1 candidate is functionally annotated as a GSK3/SHAGGY-like kinase with homologs in Arabidopsis, wheat, barley, rice, and maize, with several of these proteins having known mutants giving rise to brassinosteroid insensitivity and shorter seeds. The substitution in AVESA.00010b.r2.3AG0419820.1 affects a residue with a known gain-of function substitution in Arabidopsis BRASSINOSTEROID-INSENSITIVE2. We propose a gain-of-function mutation in AVESA.00010b.r2.3AG0419820.1 as the most likely cause of the observed phenotype, and name the gene AsGSK2.1. The findings presented here provide potential targets for oat breeders, and a step on the way towards understanding brassinosteroid signaling, seed shape and nutrition in oats.

Phenotypic and genetic characterization of an Avena sativa L. germplasm collection of diverse origin: implications for food-oat breeding in Chile

Oats are known for their nutritional value and also for their beneficial properties on human health, such as the reduction of cholesterol levels and risk of coronary heart disease; they are an important export product for Chile. During the last decade (2010-2022) over 90% of the oat cultivated area in Chile has been covered with Avena sativa L. cv. Supernova INIA. This lack of genetic diversity in a context of climate change could limit the long-term possibility of growing oats in Chile. The present study is a phenotypic and genetic analysis of 132 oat cultivars and pure lines of diverse origin that can be considered as potential breeding material. The germplasm was evaluated for 28 traits and analyzed with 14 SSR markers. The effects of genotypes on phenotype were significant over all traits (P ≤ 0.05). Most traits exhibited moderate to high broad-sense heritability with exceptions such as yield (H2 = 0.27) and hulls staining (H2 = 0.32). Significant undesirable correlations between traits were generally of small biological importance, which is auspicious for achieving breeding objectives. Some of the heritability data and correlations provided here have not been previously reported. The overall phenotypic diversity was high (H' = 0.68 ± 0.18). The germplasm was grouped into three phenotypic clusters, differing in their qualities for breeding. Twenty-six genotypes outperforming Supernova INIA were identified for breeding of conventional food-oats. The genetic diversity of the germplasm was moderate on average (He = 0.58 ± 0.03), varying between 0.32 (AM22) and 0.77 (AME178). Two genetic subpopulations supported by the Structure algorithm exhibited a genetic distance of 0.24, showing low divergence of the germplasm. The diversity and phenotypic values found in this collection of oat genotypes are promising with respect to obtaining genetic gain in the short term in breeding programs. However, the similar genetic diversity, higher phenotypic diversity, and better phenotypic performance of the germplasm created in Chile compared to foreign germplasm suggest that germplasm harboring new genetic diversity will be key to favor yield and quality in new oat cultivars in the long term.

Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics

Over the last decades, significant strides were made in understanding the biochemical factors influencing the nutritional content and flavor profile of fruits and vegetables. Product differentiation in the produce aisle is the natural consequence of increasing consumer power in the food industry. Cotton-candy grapes, specialty tomatoes, and pineapple-flavored white strawberries provide a few examples. Given the increased demand for flavorful varieties, and pressing need to reduce micronutrient malnutrition, we expect breeding to increase its prioritization toward these traits. Reaching this goal will, in part, necessitate knowledge of the genetic architecture controlling these traits, as well as the development of breeding methods that maximize their genetic gain. Can artificial intelligence (AI) help predict flavor preferences, and can such insights be leveraged by breeding programs? In this Perspective, we outline both the opportunities and challenges for the development of more flavorful and nutritious crops, and how AI can support these breeding initiatives.

Genetic dissection of morphological variation in rosette leaves and leafy heads in cabbage (Brassica oleracea var. capitata)

Correlations between morphological traits of cabbage rosette leaves and heads were found. Genome-wide association studies of these traits identified 50 robust quantitative trait loci in multiple years. Half of these loci affect both organs. Cabbage (Brassica oleracea var. capitata) is an economically important vegetable crop cultivated worldwide. Cabbage plants go through four vegetative stages: seedling, rosette, folding and heading. Rosette leaves are the largest leaves of cabbage plants and provide most of the energy needed to produce the leafy head. To understand the relationship and the genetic basis of leaf development and leafy head formation, 308 cabbage accessions were scored for rosette leaf and head traits in three-year field trials. Significant correlations were found between morphological traits of rosette leaves and heads, namely leaf area with the head area, height and width, and leaf width with the head area and head height, when heads were harvested at a fixed number of days after sowing. Fifty robust quantitative trait loci (QTLs) for rosette leaf and head traits distributed over all nine chromosomes were identified with genome-wide association studies. All these 50 loci were identified in multiple years and generally affect multiple traits. Twenty-five of the QTL were associated with both rosette leaf and leafy head traits. We discuss thirteen candidate genes identified in these QTL that are expressed in heading leaves, with an annotation related to auxin and other phytohormones, leaf development, and leaf polarity that likely play a role in leafy head development or rosette leaf expansion.

Archetypes of inflorescence: genome-wide association networks of panicle morphometric, growth, and disease variables in a multiparent oat population

There is limited information regarding the morphometric relationships of panicle traits in oat (Avena sativa) and their contribution to phenology and growth, physiology, and pathology traits important for yield. To model panicle growth and development and identify genomic regions associated with corresponding traits, 10 diverse spring oat mapping populations (n = 2,993) were evaluated in the field and 9 genotyped via genotyping-by-sequencing. Representative panicles from all progeny individuals, parents, and check lines were scanned, and images were analyzed using manual and automated techniques, resulting in over 60 unique panicle, rachis, and spikelet variables. Spatial modeling and days to heading were used to account for environmental and phenological variances, respectively. Panicle variables were intercorrelated, providing reproducible archetypal and growth models. Notably, adult plant resistance for oat crown rust was most prominent for taller, stiff stalked plants having a more open panicle structure. Within and among family variance for panicle traits reflected the moderate-to-high heritability and mutual genome-wide associations (hotspots) with numerous high-effect loci. Candidate genes and potential breeding applications are discussed. This work adds to the growing genetic resources for oat and provides a unique perspective on the genetic basis of panicle architecture in cereal crops.

Generalizable approaches for genomic prediction of metabolites in plants

Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.