Explore the vast range of titles available.

Soft winter wheat has been adapted to the north-central, north-western, and south-central United States over hundreds of years for optimal yield, height, heading date, and pathogen and pest resistance. Environmental factors like weather affect abiotic traits such as pre-harvest sprouting resistance. However, pre-harvest sprouting has rarely been a target for breeding. Owing to changing weather patterns from climate change, pre-harvest sprouting resistance is needed to prevent significant crop losses not only in the United States, but worldwide. Twenty-two traits including age of breeding line as well as agronomic, flour quality, and pre-harvest sprouting traits were studied in a population of 188 lines representing genetic diversity over 200 years of soft winter wheat breeding. Some traits were correlated with one another by principal components analysis and Pearson’s correlations. A genome-wide association study using 1,978 markers uncovered a total of 102 regions encompassing 226 quantitative trait nucleotides. Twenty-six regions overlapped multiple traits with common significant markers. Many of these traits were also found to be correlated by Pearson’s correlation and principal components analyses. Most pre-harvest sprouting regions were not co-located with agronomic traits and thus useful for crop improvement against climate change without affecting crop performance. Six different genome-wide association statistical models (GLM, MLM, MLMM, FarmCPU, BLINK, and SUPER) were utilized to search for reasonable models to analyze soft winter wheat populations with increased markers and/or breeding lines going forward. Some flour quality and agronomic traits seem to have been selected over time, but not pre-harvest sprouting. It appears possible to select for pre-harvest sprouting resistance without impacting flour quality or the agronomic value of soft winter wheat.

Wheat crops provide 20% of calories worldwide. Cell walls function in plant growth, are part of biotic and abiotic stress resistance, and provide plant mechanical strength and adaptability. These functions factor into the productivity of wheat. The genes that produce and maintain the plant cell wall are up to 10% of the genome in many varied families. Previously, curated cell wall gene families have been published for maize and rice, two other important crop grasses. Here, 81 cell wall-related wheat gene families curated via sequence similarity to maize and rice and unique family protein motif searches are presented. A total of 4086 wheat, 1118 maize, 1036 rice, and 955 Arabidopsis genes were aligned and placed into gene family trees to present homologs for all four species. Due to hexaploidy, many wheat cell wall gene families show expected triplication of genes per family over maize, rice, and Arabidopsis. However, several families contained more wheat genes than expected. The utility of this research is demonstrated with an example from a pre-harvest sprouting study to identify specific gene families rather than the less descriptive identification available with standard bioinformatic searches.

Grain and flour quality traits affect marketing potential and milling and baking properties. Trait means varied in fourteen wheat grain and flour quality traits for a population of 188 diverse soft winter wheat varieties harvested from 2020 to 2023 at the same location. Significant weather differences occurred yearly. This created a challenge for the detection of chromosome locations affecting these traits through genome-wide association studies (GWAS). Mean normalization using standard deviation to transform raw data to Z scores has been used successfully in other statistical analyses of biological systems with mean differences. Mean normalization was applied to a GWAS, improving detection power for thirteen grain and flour quality traits with high broad-sense heritability. It did not improve the lone trait with low heritability. Improvement was measured as the reduction in the p-value of mean normalized data compared with raw data for the same significant marker using the same GWAS model in the same trait. Improvement varied by trait and marker, but the average p-value of 135 common significant marker/GWAS model combinations was reduced 27 times with mean normalization over raw averaged data. Mean normalization reduced p-values ~1800 times when compared with a GWAS using best linear unbiased predictors. However, the best linear unbiased predictors led to only 15 common marker/GWAS model combinations with mean normalization, limiting the ability for direct marker comparison. Test weight, kernel protein, kernel weight, sodium carbonate solvent retention capacity, and sucrose solvent retention capacity showed the greatest increased detection power.

Grasses and related commelinid monocot species synthesize cell walls distinct in composition from other angiosperm species. With few exceptions, the genomes of all angiosperms contain the genes that encode the enzymes for synthesis of all cell-wall polysaccharide, phenylpropanoid, and protein constituents known in vascular plants. RNA-seq analysis of transcripts expressed during development of the upper and lower internodes of maize ( Zea mays ) stem captured the expression of cell-wall-related genes associated with primary or secondary wall formation. High levels of transcript abundances were not confined to genes associated with the distinct walls of grasses but also of those associated with xyloglucan and pectin synthesis. Combined with proteomics data to confirm that expressed genes are translated, we propose that the distinctive cell-wall composition of grasses results from sorting downstream from their sites of synthesis in the Golgi apparatus and hydrolysis of the uncharacteristic polysaccharides and not from differential expression of synthases of grass-specific polysaccharides.

Background Genome-Wide Association Studies (GWAS) are used to identify genes and alleles that contribute to quantitative traits in large and genetically diverse populations. However, traits with complex genetic architectures create an enormous computational load for discovery of candidate genes with acceptable statistical certainty. We developed a streamlined computational pipeline for GWAS (COMPILE) to accelerate identification and annotation of candidate maize genes associated with a quantitative trait, and then matches maize genes to their closest rice and Arabidopsis homologs by sequence similarity. Results COMPILE executed GWAS using a Mixed Linear Model that incorporated, without compression, recent advancements in population structure control, then linked significant Quantitative Trait Loci (QTL) to candidate genes and RNA regulatory elements contained in any genome. COMPILE was validated using published data to identify QTL associated with the traits of α-tocopherol biosynthesis and flowering time, and identified published candidate genes as well as additional genes and non-coding RNAs. We then applied COMPILE to 274 genotypes of the maize Goodman Association Panel to identify candidate loci contributing to resistance of maize stems to penetration by larvae of the European Corn Borer ( Ostrinia nubilalis ). Candidate genes included those that encode a gene of unknown function, WRKY and MYB-like transcriptional factors, receptor-kinase signaling, riboflavin synthesis, nucleotide-sugar interconversion, and prolyl hydroxylation. Expression of the gene of unknown function has been associated with pathogen stress in maize and in rice homologs closest in sequence identity. Conclusions The relative speed of data analysis using COMPILE allowed comparison of population size and compression. Limitations in population size and diversity are major constraints for a trait and are not overcome by increasing marker density. COMPILE is customizable and is readily adaptable for application to species with robust genomic and proteome databases.

Background The cellular machinery for cell wall synthesis and metabolism is encoded by members of large multi-gene families. Maize is both a genetic model for grass species and a potential source of lignocellulosic biomass from crop residues. Genetic improvement of maize for its utility as a bioenergy feedstock depends on identification of the specific gene family members expressed during secondary wall development in stems. Results High-throughput sequencing of transcripts expressed in developing rind tissues of stem internodes provided a comprehensive inventory of cell wall-related genes in maize ( Zea mays , cultivar B73). Of 1239 of these genes, 854 were expressed among the internodes at ≥95 reads per 20 M, and 693 of them at ≥500 reads per 20 M. Grasses have cell wall compositions distinct from non-commelinid species; only one-quarter of maize cell wall-related genes expressed in stems were putatively orthologous with those of the eudicot Arabidopsis. Using a slope-metric algorithm, five distinct patterns for sub-sets of co-expressed genes were defined across a time course of stem development. For the subset of genes associated with secondary wall formation, fifteen sequence motifs were found in promoter regions. The same members of gene families were often expressed in two maize inbreds, B73 and Mo17, but levels of gene expression between them varied, with 30% of all genes exhibiting at least a 5-fold difference at any stage. Although presence-absence and copy-number variation might account for much of these differences, fold-changes of expression of a CADa and a FLA11 gene were attributed to polymorphisms in promoter response elements. Conclusions Large genetic variation in maize as a species precludes the extrapolation of cell wall-related gene expression networks even from one common inbred line to another. Elucidation of genotype-specific expression patterns and their regulatory controls will be needed for association panels of inbreds and landraces to fully exploit genetic variation in maize and other bioenergy grass species.

Grass species represent a major source of food, feed, and fiber crops and potential feedstocks for biofuel production. Most of the biomass is contributed by cell walls that are distinct in composition from all other flowering plants. Identifying cell wall-related genes and their functions underpins a fundamental understanding of growth and development in these species. Toward this goal, we are building a knowledge base of the maize (Zea mays) genes involved in cell wall biology, their expression profiles, and the phenotypic consequences of mutation. Over 750 maize genes were annotated and assembled into gene families predicted to function in cell wall biogenesis. Comparative genomics of maize, rice (Oryza sativa), and Arabidopsis (Arabidopsis thaliana) sequences reveal differences in gene family structure between grass species and a reference eudicot species. Analysis of transcript profile data for cell wall genes in developing maize ovaries revealed that expression within families differed by up to 100-fold. When transcriptional analyses of developing ovaries before pollination from Arabidopsis, rice, and maize were contrasted, distinct sets of cell wall genes were expressed in grasses. These differences in gene family structure and expression between Arabidopsis and the grasses underscore the requirement for a grass-specific genetic model for functional analyses. A UniformMu population proved to be an important resource in both forward- and reverse-genetics approaches to identify hundreds of mutants in cell wall genes. A forward screen of field-grown lines by near-infrared spectroscopic screen of mature leaves yielded several dozen lines with heritable spectroscopic phenotypes. Pyrolysis-molecular beam mass spectrometry confirmed that several nir mutants had altered carbohydrate-lignin compositions.

The plant endoplasmic reticulum-Golgi apparatus is the site of synthesis, assembly, and trafficking of all noncellulosic polysaccharides, proteoglycans, and proteins destined for the cell wall. As grass species make cell walls distinct from those of dicots and noncommelinid monocots, it has been assumed that the differences in cell-wall composition stem from differences in biosynthetic capacities of their respective Golgi. However, immunosorbence-based screens and carbohydrate linkage analysis of polysaccharides in Golgi membranes, enriched by flotation centrifugation from etiolated coleoptiles of maize (Zea mays) and leaves of Arabidopsis (Arabidopsis thaliana), showed that arabinogalactan-proteins and arabinans represent substantial portions of the Golgi-resident polysaccharides not typically found in high abundance in cell walls of either species. Further, hemicelluloses accumulated in Golgi at levels that contrasted with those found in their respective cell walls, with xyloglucans enriched in maize Golgi, and xylans enriched in Arabidopsis. Consistent with this finding, maize Golgi membranes isolated by flotation centrifugation and enriched further by free-flow electrophoresis, yielded >200 proteins known to function in the biosynthesis and metabolism of cell-wall polysaccharides common to all angiosperms, and not just those specific to cell-wall type. We propose that the distinctive compositions of grass primary cell walls compared with other angiosperms result from differential gating or metabolism of secreted polysaccharides post-Golgi by an as-yet unknown mechanism, and not necessarily by differential expression of genes encoding specific synthase complexes.

A modified TEMPO-catalyzed oxidation of the solvent-exposed glucosyl units of cellulose to uronic acids, followed by carboxyl reduction with NaBD4 to 6-deutero- and 6,6-dideuteroglucosyl units, provided a robust method for determining relative proportions of disordered amorphous, ordered surface chains, and anhydrous core-crystalline residues of cellulose microfibrils inaccessible to TEMPO. Both glucosyl residues of cellobiose units, digested from amorphous chains of cellulose with a combination of cellulase and cellobiohydrolase, were deuterated, whereas those from anhydrous chains were undeuterated. By contrast, solvent-exposed and anhydrous residues alternate in surface chains, so only one of the two residues of cellobiosyl units was labeled. Although current estimates indicate that each cellulose microfibril comprises only 18 to 24 (1 → 4)-β-d-glucan chains, we show here that microfibrils of walls of Arabidopsis leaves and maize coleoptiles, and those of secondary wall cellulose of cotton fibers and poplar wood, bundle into much larger macrofibrils, with 67 to 86% of the glucan chains in the anhydrous domain. These results indicate extensive bundling of microfibrils into macrofibrils occurs during both primary and secondary wall formation. We discuss how, beyond lignin, the degree of bundling into macrofibrils contributes an additional recalcitrance factor to lignocellulosic biomass for enzymatic or chemical catalytic conversion to biofuel substrates.Graphic abstract

Biotechnological approaches to reduce or modify lignin in biomass crops are predicated on the assumption that it is the principal determinant of the recalcitrance of biomass to enzymatic digestion for biofuels production. We defined quantitative trait loci (QTL) in the Intermated B73 × Mo17 recombinant inbred maize (Zea mays) population using pyrolysis molecular-beam mass spectrometry to establish stem lignin content and an enzymatic hydrolysis assay to measure glucose and xylose yield. Among five multiyear QTL for lignin abundance, two for 4-vinylphenol abundance, and four for glucose and/or xylose yield, not a single QTL for aromatic abundance and sugar yield was shared. A genome-wide association study for lignin abundance and sugar yield of the 282-member maize association panel provided candidate genes in the 11 QTL of the B73 and Mo17 parents but showed that many other alíeles impacting these traits exist among this broader pool of maize genetic diversity. B73 and Mo17 genotypes exhibited large differences in gene expression in developing stem tissues independent of allelic variation. Combining these complementary genetic approaches provides a narrowed list of candidate genes. A cluster of SCARECROW-LIKE9 and SCAKECROW-LIKE14 transcription factor genes provides exceptionally strong candidate genes emerging from the genome-wide association study. In addition to these and genes associated with cell wall metabolism, candidates include several other transcription factors associated with vascularization and fiber formation and components of cellular signaling pathways. These results provide new insights and strategies beyond the modification of lignin to enhance yields of biofuels from genetically modified biomass.