Evaluating maize phenotypic variance, heritability, and yield relationships at multiple biological scales across agronomically relevant environments.

A challenge to improve an integrative phenotype, like yield, is the interaction between the broad range of possible molecular and physiological traits that contribute to yield and the multitude of potential environmental conditions in which they are expressed. This study collected data on 31 phenotypic traits, 83 annotated metabolites, and nearly 22,000 transcripts from a set of 57 diverse, commercially relevant maize hybrids across three years in central U.S. Corn Belt environments. Although variability in characteristics created a complex picture of how traits interact produce yield, phenotypic traits and gene expression were more consistent across environments, while metabolite levels showed low repeatability. Phenology traits, such as green leaf number and grain moisture and whole plant nitrogen content showed the most consistent correlation with yield. A machine learning predictive analysis of phenotypic traits revealed that ear traits, phenology, and root traits were most important to predicting yield. Analysis suggested little correlation between biomass traits and yield, suggesting there is more of a sink limitation to yield under the conditions studied here. This work suggests that continued improvement of maize yields requires a strong understanding of baseline variation of plant characteristics across commercially-relevant germplasm to drive strategies for consistently improving yield.

Comparative analysis of maize (Zea mays) crop performance: natural variation, incremental improvements and economic impacts.

Grain yield from maize hybrids continues to improve through advances in breeding and biotechnology. Despite genetic improvements to hybrid maize, grain yield from distinct maize hybrids is expected to vary across growing locations due to numerous environmental factors. In this study, we examine across-location variation in grain yield among maize hybrids in three case studies. The three case studies examine hybrid improvement through breeding, introduction of an insect protection trait or introduction of a transcription factor trait associated with increased yield. In all cases, grain yield from each hybrid population had a Gaussian distribution. Across-location distributions of grain yield from each hybrid partially overlapped. The hybrid with a higher mean grain yield typically outperformed its comparator at most, but not all, of the growing locations (a 'win rate'). These results suggest that a broad set of environmental factors similarly impacts grain yields from both conventional- and biotechnology-derived maize hybrids and that grain yields among two or more hybrids should be compared with consideration given to both mean yield performance and the frequency of locations at which each hybrid 'wins' against its comparators. From an economic standpoint, growers recognize the value of genetically improved maize hybrids that outperform comparators in the majority of locations. Grower adoption of improved maize hybrids drives increases in average U.S. maize grain yields and contributes significant value to the economy.

Expression of a truncated ATHB17 protein in maize increases ear weight at silking.

ATHB17 (AT2G01430) is an Arabidopsis gene encoding a member of the α-subclass of the homeodomain leucine zipper class II (HD-Zip II) family of transcription factors. The ATHB17 monomer contains four domains common to all class II HD-Zip proteins: a putative repression domain adjacent to a homeodomain, leucine zipper, and carboxy terminal domain. However, it also possesses a unique N-terminus not present in other members of the family. In this study we demonstrate that the unique 73 amino acid N-terminus is involved in regulation of cellular localization of ATHB17. The ATHB17 protein is shown to function as a transcriptional repressor and an EAR-like motif is identified within the putative repression domain of ATHB17. Transformation of maize with an ATHB17 expression construct leads to the expression of ATHB17Δ113, a truncated protein lacking the first 113 amino acids which encodes a significant portion of the repression domain. Because ATHB17Δ113 lacks the repression domain, the protein cannot directly affect the transcription of its target genes. ATHB17Δ113 can homodimerize, form heterodimers with maize endogenous HD-Zip II proteins, and bind to target DNA sequences; thus, ATHB17Δ113 may interfere with HD-Zip II mediated transcriptional activity via a dominant negative mechanism. We provide evidence that maize HD-Zip II proteins function as transcriptional repressors and that ATHB17Δ113 relieves this HD-Zip II mediated transcriptional repression activity. Expression of ATHB17Δ113 in maize leads to increased ear size at silking and, therefore, may enhance sink potential. We hypothesize that this phenotype could be a result of modulation of endogenous HD-Zip II pathways in maize.

Characterization and crystal structure of lysine insensitive Corynebacterium glutamicum dihydrodipicolinate synthase (cDHDPS) protein.

The lysine insensitive Corynebacterium glutamicum dihydrodipicolinate synthase enzyme (cDHDPS) was recently successfully introduced into maize plants to enhance the level of lysine in the grain. To better understand lysine insensitivity of the cDHDPS, we expressed, purified, kinetically characterized the protein, and solved its X-ray crystal structure. The cDHDPS enzyme has a fold and overall structure that is highly similar to other DHDPS proteins. A noteworthy feature of the active site is the evidence that the catalytic lysine residue forms a Schiff base adduct with pyruvate. Analyses of the cDHDPS structure in the vicinity of the putative binding site for S-lysine revealed that the allosteric binding site in the Escherichia coli DHDPS protein does not exist in cDHDPS due to three non-conservative amino acids substitutions, and this is likely why cDHDPS is not feedback inhibited by lysine.

Bioinformatic methods for allergenicity assessment using a comprehensive allergen database.

BACKGROUND: A principal aim of the safety assessment of genetically modified crops is to prevent the introduction of known or clinically cross-reactive allergens. Current bioinformatic tools and a database of allergens and gliadins were tested for the ability to identify potential allergens by analyzing 6 Bacillus thuringiensis insecticidal proteins, 3 common non-allergenic food proteins and 50 randomly selected corn (Zea mays) proteins. METHODS: Protein sequences were compared to allergens using the FASTA algorithm and by searching for matches of 6, 7 or 8 contiguous identical amino acids. RESULTS: No significant sequence similarities or matches of 8 contiguous amino acids were found with the B. thuringiensis or food proteins. Surprisingly, 41 of 50 corn proteins matched at least one allergen with 6 contiguous identical amino acids. Only 7 of 50 corn proteins matched an allergen with 8 contiguous identical amino acids. When assessed for overall structural similarity to allergens, these 7 plus 2 additional corn proteins shared >or=35% identity in an overlap of >or=80 amino acids, but only 6 of the 7 were similar across the length of the protein, or shared >50% identity to an allergen. CONCLUSIONS: An evaluation of a protein by the FASTA algorithm is the most predictive of a clinically relevant cross-reactive allergen. An additional search for matches of 8 amino acids may provide an added margin of safety when assessing the potential allergenicity of a protein, but a search with a 6-amino-acid window produces many random, irrelevant matches.