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.
View Article and Find Full Text PDFGrain 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.
View Article and Find Full Text PDFATHB17 (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.
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFBackground: 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.