Gene expression biomarkers provide sensitive indicators of in planta nitrogen status in maize

基因表达生物标志物为玉米植物体内氮状况提供敏感指标

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作者:Xiaofeng S Yang, Jingrui Wu, Todd E Ziegler, Xiao Yang, Adel Zayed, M S Rajani, Dafeng Zhou, Amarjit S Basra, Daniel P Schachtman, Mingsheng Peng, Charles L Armstrong, Rico A Caldo, James A Morrell, Michelle Lacy, Jeffrey M Staub

Abstract

Over the last several decades, increased agricultural production has been driven by improved agronomic practices and a dramatic increase in the use of nitrogen-containing fertilizers to maximize the yield potential of crops. To reduce input costs and to minimize the potential environmental impacts of nitrogen fertilizer that has been used to optimize yield, an increased understanding of the molecular responses to nitrogen under field conditions is critical for our ability to further improve agricultural sustainability. Using maize (Zea mays) as a model, we have characterized the transcriptional response of plants grown under limiting and sufficient nitrogen conditions and during the recovery of nitrogen-starved plants. We show that a large percentage (approximately 7%) of the maize transcriptome is nitrogen responsive, similar to previous observations in other plant species. Furthermore, we have used statistical approaches to identify a small set of genes whose expression profiles can quantitatively assess the response of plants to varying nitrogen conditions. Using a composite gene expression scoring system, this single set of biomarker genes can accurately assess nitrogen responses independently of genotype, developmental stage, tissue type, or environment, including in plants grown under controlled environments or in the field. Importantly, the biomarker composite expression response is much more rapid and quantitative than phenotypic observations. Consequently, we have successfully used these biomarkers to monitor nitrogen status in real-time assays of field-grown maize plants under typical production conditions. Our results suggest that biomarkers have the potential to be used as agronomic tools to monitor and optimize nitrogen fertilizer usage to help achieve maximal crop yields.

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