|Zhang, Nengyi -|
|Gibon, Yves -|
|Gur, Amit -|
|Chen, Charles -|
|Hohne, Melanie -|
|Zhang, Zhiwu -|
|Kroon, Dallas -|
|Tschoep, Hendrik -|
|Sitt, Mark -|
Submitted to: Plant Physiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 20, 2010
Publication Date: October 22, 2010
Citation: Zhang, N., Gibon, Y., Gur, A., Chen, C., Lepak, N.K., Hohne, M., Zhang, Z., Kroon, D., Tschoep, H., Sitt, M., Buckler IV, E.S. 2010. Fine quantitative trait loci mapping of carbon and nitrogen metabolism enzyme activities and seedling biomass in the intermated maize IBM mapping population. Plant Physiology. 154:1753-1765. Interpretive Summary: Plant growth and development are largely dependent on nitrogen and carbon metabolism. Understanding the genetic basis of nitrogen and carbon metabolism will accelerate development of plant varieties with high yield and improved nitrogen use efficiency. In this study, we measured the activities of ten enzymes from carbon and nitrogen metabolism in a maize genetic mapping population. We found that the natural variation at numerous other genes appear to regulate each enzyme, and that the protein sequence and DNA context of the gene encoding the enzyme were relatively unimportant. Overall, this study identifies more quantitative trait loci (QTL) at a higher resolution than previous studies of genetic variation in metabolism, which in the long-term enable the design of maize plants with optimized carbon and nitrogen metabolism for each environment.
Technical Abstract: Understanding the genetic basis of nitrogen and carbon metabolism will accelerate development of plant varieties with high yield and improved nitrogen use efficiency. In this study, we measured the activities of ten enzymes from carbon and nitrogen metabolism and seedling/juvenile biomass in the maize (Zea mays L.) intermated B73×Mo17 (IBM) mapping population, which provides almost a four-fold increase in genetic map distance compared to conventional mapping populations. We found that all ten enzymes are heritable in activity and are positively correlated, indicating they are co-regulated. We also detected negative correlations between biomass and the activity of six enzymes. In total, we found 73 QTL, 8 of which were significant, that influence activity of these ten enzymes and biomass, respectively. While some QTL were shared by different enzymes or biomass, we critically evaluated the probability that this may be fortuitous. All enzyme activity QTL were in trans to the known genomic locations of structural genes, except for single cis-QTL for nitrate reductase, glutamate dehydrogenase, and shikimate dehydrogenase; the low frequency and low additive magnitude compared to trans-QTL indicates that cis-regulation is relatively unimportant versus trans-regulation. We identified two-gene epistatic interactions for eight enzymes and biomass, with three epistatic QTL being shared by two other traits; however, epistasis explained on average only 2.8% of the genetic variance. Overall, this study identifies more QTL at a higher resolution than previous studies of genetic variation in metabolism.