Softening is a hallmark of ripening in fleshy fruits, and has both desirable and undesirable implications for texture and postharvest stability. Accordingly, the timing and extent of pre-harvest ripening and associated textural changes following harvest are key targets for improving fruit quality through breeding. Previously, we identified a large effect locus associated with harvest date and firmness in apple ( Malus domestica ) using genome-wide association studies (GWAS). Here, we present additional evidence that polymorphisms in or around a transcription factor gene, NAC18.1 , may cause variation in these traits. First, we confirmed our previous findings with new phenotype and genotype data from ∼800 apple accessions. In this population, we compared a genetic marker within NAC18.1 to markers targeting three other firmness-related genes currently used by breeders ( ACS1 , ACO1 , and PG1 ), and found that the NAC18.1 marker was the strongest predictor of both firmness at harvest and firmness after 3 months of cold storage. By sequencing NAC18.1 across 18 accessions, we revealed two predominant haplotypes containing the single nucleotide polymorphism (SNP) previously identified using GWAS, as well as dozens of additional SNPs and indels in both the coding and promoter sequences. NAC18.1 encodes a protein that is orthogolous to the NON-RIPENING (NOR) transcription factor, a regulator of ripening in tomato ( Solanum lycopersicum ). We introduced both NAC18.1 transgene haplotypes into the tomato nor mutant and showed that both haplotypes complement the nor ripening deficiency. Taken together, these results indicate that polymorphisms in NAC18.1 may underlie substantial variation in apple firmness through modulation of a conserved ripening program.

The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.

Abstract Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional “omics” data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.

Oat ( Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Despite these characteristics, little is known regarding the genetic controllers of variation for these compounds in oat seed. We sought to characterize natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leverage this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for eight of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional ‘omics’ data to a set of biologically-meaningful variables and translate inferences on these data into improved breeding decisions.

Key message Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly-related populations in addition to the single-environment prediction.Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly-related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17 and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly-related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

ABSTRACT Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly-related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G+T, G+M and G+T+M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17 and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly-related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Oat ranks sixth in world cereal production and has a higher content of health-promoting compounds compared with other cereals. However, there is neither a robust oat reference genome nor transcriptome. Using deeply sequenced full-length mRNA libraries of oat cultivar Ogle-C, a de novo high-quality and comprehensive oat seed transcriptome was assembled. With this reference transcriptome and QuantSeq 3' mRNA sequencing, gene expression was quantified during seed development from 22 diverse lines across six time points. Transcript expression showed higher correlations between adjacent time points. Based on differentially expressed genes, we identified 22 major temporal co-expression (TCoE) patterns of gene expression and revealed enriched gene ontology biological processes. Within each TCoE set, highly correlated transcripts, putatively commonly affected by genetic background, were clustered and termed genetic co-expression (GCoE) sets. Seventeen of the 22 TCoE sets had GCoE sets with median heritabilities higher than 0.50, and these heritability estimates were much higher than that estimated from permutation analysis, with no divergence observed in cluster sizes between permutation and non-permutation analyses. Linear regression between 634 metabolites from mature seeds and the PC1 score of each of the GCoE sets showed significantly lower p-values than permutation analysis. Temporal expression patterns of oat avenanthramides and lipid biosynthetic genes were concordant with previous studies of avenanthramide biosynthetic enzyme activity and lipid accumulation. This study expands our understanding of physiological processes that occur during oat seed maturation and provides plant breeders the means to change oat seed composition through targeted manipulation of key pathways.

Lipid synthesis is a ubiquitous but costly branch of primary metabolism in plants. All cells must make fatty acids as precursors for cellular membranes and signaling molecules, and some cells make large quantities of fatty acid-derived products, such as oils (triacylglycerol) or cuticles (wax and

Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.

Softening is a hallmark of ripening in fleshy fruits, and has both desirable and undesirable implications for texture and postharvest stability. Accordingly, the timing and extent of ripening and associated textural changes are key targets for improving fruit quality through breeding. Previously, we identified a large effect locus associated with harvest date and firmness in apple (Malus domestica) using genome-wide association studies (GWAS). Here, we present additional evidence that polymorphisms in or around a transcription factor gene, NAC18.1, cause variation in these traits. First, we confirmed our previous findings with new phenotype and genotype data from ~800 apple accessions. In this population, we compared NAC18.1 to three other ripening-related markers currently used by breeders (ACS1, ACO1, and PG1), and found that the effect of the NAC18.1 genotype on both traits greatly exceeded that observed for the other markers. By sequencing NAC18.1 across 18 accessions, we revealed two predominant haplotypes containing the SNP previously identified using GWAS, as well as dozens of additional SNPs and indels in both the coding and promoter sequences. NAC18.1 encodes a protein with high similarity to the NON-RIPENING (NOR) transcription factor, an early regulator of ripening in tomato (Solanum lycopersicum). To test whether these genes are functionally orthologous, we introduced NAC18.1 transgenes into the tomato nor mutant and showed that both haplotypes complement the nor ripening deficiency. Taken together, these results indicate that polymorphisms in NAC18.1 underlie substantial variation in apple firmness and harvest time through modulation of a conserved ripening program. Highlight NAC18.1 is a member of a family of conserved transcriptional regulators of ripening that underlies variation in fruit firmness and harvest date in diverse apple accessions.

Hydrogen cyanide (HCN) is a potent inhibitor of cytochrome c oxidase, a conserved component of the respiratory electron transport chain in all aerobic life. Thus, HCN is well suited to serve as a broad-spectrum chemical defense, and indeed it plays such a role in many interactions between plants and

Approximately 1% of plant proteins are predicted to be post-translationally modified with a glycosylphosphatidylinositol (GPI) anchor that tethers the polypeptide to the outer leaflet of the plasma membrane. Whereas the synthesis and structure of GPI anchors is largely conserved across eukaryotes, the repertoire of functional domains present in the GPI-anchored proteome has diverged substantially. In plants, this includes a large fraction of the GPI-anchored proteome being further modified with plant-specific arabinogalactan (AG) O-glycans. The importance of the GPI-anchored proteome to plant development is underscored by the fact that GPI biosynthetic null mutants exhibit embryo lethality. Mutations in genes encoding specific GPI-anchored proteins (GAPs) further supports their contribution to diverse biological processes, occurring at the interface of the plasma membrane and cell wall, including signaling, cell wall metabolism, cell wall polymer cross-linking, and plasmodesmatal transport. Here, we review the literature concerning plant GPI-anchored proteins, in the context of their potential to act as molecular hubs that mediate interactions between the plasma membrane and the cell wall, and their potential to transduce the signal into the protoplast and, thereby, activate signal transduction pathways.

Saffron is produced from the stigmas and styles of Crocus sativus flowers and is one of the most expensive spices. In C. sativus , both stigma and style are intensely crimson red in color due to the presence of three major classes of apocarotenoids: crocins, picrocrocin, and safranal. In addition to

Fruit softening, which is a major determinant of shelf life and commercial value, is the consequence of multiple cellular processes, including extensive remodeling of cell wall structure. Recently, it has been shown that pectate lyase (PL), an enzyme that degrades de-esterified pectin in the primary wall, is a major contributing factor to tomato fruit softening. Studies of pectin structure, distribution, and dynamics have indicated that pectins are more tightly integrated with cellulose microfibrils than previously thought and have novel structural features, including branches of the main polymer backbone. Moreover, recent studies of the significance of pectinases, such as PL and polygalacturonase, are consistent with a causal relationship between pectin degradation and a major effect on fruit softening.

The cell wall invertase gene (LIN5) was reported to be a key enzyme influencing sugar uptake of tomato (Solanum lycopersicum) fruit. It was additionally revealed to be a key regulator of total soluble solids content in fruit as well as for reproductive development, being mainly involved in flower development, early fruit and seed development but also in ripening. Here, we demonstrate that silencing of the LIN5 gene promotes changes affecting fruit cuticle development which has a direct effect on postharvest properties. Transformants were characterized by reduced transpirational water loss in mature fruits accompanied by several other changes in the cuticle. Quantitative chemical composition, coupled with microscopy of isolated cuticle fruits revealed that the cuticle of the transformants were characterized by an increase of the thickness as well as significant increase in the content of cuticle components (cutin, phenolic compounds, and waxes). Furthermore, detailed analysis of the waxes revealed that the transformants displayed changes in waxes composition, showing higher levels of n-alkanes and triterpenoids which can shift the proportion of crystalline and amorphous waxes and change the water flux through the cuticle. Expression of the genes involved in cuticle biosynthesis indicated that LIN5 influences the biosynthesis of components of the cuticle, indicating that this process is coupled to sugar uploading via a mechanism which links carbon supply with the capacity for fruit expansion.

In order to understand factors controlling the synthesis and deposition of cellulose, we have studied the Arabidopsis (Arabidopsis thaliana) double mutant shaven3 shaven3-like1 (shv3svl1), which was shown previously to exhibit a marked cellulose deficiency. We discovered that exogenous sucrose (Suc) in growth medium greatly enhances the reduction in hypocotyl elongation and cellulose content of shv3svl1 This effect was specific to Suc and was not observed with other sugars or osmoticum. Live-cell imaging of fluorescently labeled cellulose synthase complexes revealed a slowing of cellulose synthase complexes in shv3svl1 compared with the wild type that is enhanced in a Suc-conditional manner. Solid-state nuclear magnetic resonance confirmed a cellulose deficiency of shv3svl1 but indicated that cellulose crystallinity was unaffected in the mutant. A genetic suppressor screen identified mutants of the plasma membrane Suc/H(+) symporter SUC1, indicating that the accumulation of Suc underlies the Suc-dependent enhancement of shv3svl1 phenotypes. While other cellulose-deficient mutants were not specifically sensitive to exogenous Suc, the feronia (fer) receptor kinase mutant partially phenocopied shv3svl1 and exhibited a similar Suc-conditional cellulose defect. We demonstrate that shv3svl1, like fer, exhibits a hyperpolarized plasma membrane H(+) gradient that likely underlies the enhanced accumulation of Suc via Suc/H(+) symporters. Enhanced intracellular Suc abundance appears to favor the partitioning of carbon to starch rather than cellulose in both mutants. We conclude that SHV3-like proteins may be involved in signaling during cell expansion that coordinates proton pumping and cellulose synthesis.

SHAVEN3 (SHV3) and its homolog SHAVEN3-like 1 (SVL1) encode glycosylphosphatidylinositol (GPI)-anchored proteins (GAPs) that are involved in cellulose biosynthesis and hypocotyl elongation in Arabidopsis thaliana. In a recent report, we showed that the cellulose and hypocotyl elongation defects of the shv3svl1 double mutant are greatly enhanced by exogenous sucrose in the growth medium. Further investigation of this phenomenon showed that shv3svl1 exhibits a hyperpolarized plasma membrane (PM) proton gradient that is coupled with enhanced accumulation of sucrose via the PM sucrose/proton symporter SUC1. The resulting high intracellular sucrose concentration appears to favor starch synthesis at the expense of cellulose synthesis. Here, we describe our interpretation of these results in terms of 2 potential regulators of cellulose synthesis: intracellular sucrose concentration and a putative signaling pathway that involves SHV3-like proteins.

The plant cell wall is primarily composed of the polysaccharides cellulose, hemicellulose and pectin. The structural and compositional complexity of these components are important for determining cell wall function during plant growth. Moreover, cell wall structure defines a number of functional properties of plant-derived biomass, such as rheological properties of foods and feedstock suitability for the production of cellulosic biofuels. A typical characterization of cell wall chemistry in the molecular biology lab consists of a mild acid hydrolysis for the quantification of hemicellulose and pectin-derived monomers and a separate analysis of cellulose by the Updegraff method. We have adopted a streamlined ‘one-step two-step’ hydrolysis protocol that allows for the simultaneous determination of cellulose content, neutral sugars, and uronic acids by high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) of paired samples. In our work, this protocol has largely replaced Updegraff cellulose quantification and hydrolysis with 2 M TFA for the determination of matrix polysaccharide composition at the micro scale.

Cellulose is a main component of plant cell walls. Tools to analyze cellulose mainly rely on analytical chemistry, which yields information about cellulose amounts and structure, but cannot be applied to intact tissues. Moreover, these methods measure total cellulose and cannot be used to assay cellulose synthesis per se. Live cell imaging of the catalytic subunits of the cellulose synthesis complex (CSC) conjugated to fluorescent proteins is an important tool to understand the dynamics of the cellulose biosynthesis process (Paredez et al., 2006). This method can be used in various genetic backgrounds (Sorek et al., 2014) or with different chemical inhibitors (Brabham and Debolt, 2012). Here we describe in detail the procedure to visualize the movement of CSCs at the plasma membrane. As the movement of CSCs is likely caused by glucan synthesis and extrusion into the cell wall, live cell analysis of CSC velocity provides a method to directly measure cellulose synthesis in vivo.

The majority of terrestrial biomass accumulates as plant cell walls, the main structural component of leaves, stems, roots, fruits, and seeds. The main constituents of plant cell walls are lignin and polysaccharides, which can be transformed into liquid fuel molecules through chemical transformation or microbial fermentation. Because of the large scale of demand for fuel, it is essential that biomass-to-fuel conversion processes maximize conservation of energy in the products. Here, we summarize some of the challenges posed to these processes by the chemical complexity of plant cell walls.