Lauren K. Dedow

The β‐glucuronidase gene, uidA (GUS), has remained a favorite reporter gene in plants since its introduction in 1987 for its stability and versatility in a variety of fluorometric, spectrophotometric, and histochemical techniques. One of the most popular uses is as a reporter gene for visualizing endogenous promoter activities within plant tissues. Despite this popularity, specific protocols for minimizing nonrepresentative staining patterns, including false negatives, in challenging tissue types are not common. This became a large issue during our work on dark‐grown Arabidopsis hypocotyls, and we set out to develop a protocol that would ensure accurate staining in a tissue that is biologically resistant to reagent penetration. Through extensive testing using a variety of constitutive and endogenous promoter::GUS fusion lines, we have developed an optimized GUS staining protocol that combines the use of acetone as a fixative, deliberate physical damage, and proper positive and negative controls to help ensure accurate staining along the hypocotyl while minimizing false negatives. Hopefully, our recommendations will allow for improved staining that more accurately reflects the true activity of cloned endogenous promoters and thus facilitate a more accurate understanding of promoter activity in Arabidopsis hypocotyls and other hard‐to‐stain tissues.

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C₄ metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.