The challenge of remobilisation in plant nitrogen economy.: A survey of physio-agronomic and molecular approaches

In this article, we discuss the ways in which our understanding of the controls of nitrogen remobilisation in model species and crop plants have beers increased through classical physiological studies and the use of transgenic plants or mutants with modified capacities for nitrogen or carbon assimilation and recycling. An improved understanding of the transition between nitrogen assimilation and nitrogen recycling will be vital, if improvements in crop nitrogen use efficiency are to reduce the need for excessive input of fertilisers and improve or stabilise yield. In this review, we present an overall view of past work and more recent studies on this topic, using different plants systems and models depicting the biochemical and molecular events occurring during the transition between sink leaves and source leaves. These models may provide a way to identify the nature of the metabolic or developmental signals triggering in a coordinate manner nitrogen and carbon recycling during leaf senescence. Another way of developing crop varieties with improved nitrogen use efficiency, and identifying key elements controlling the process of nitrogen remobilisation, is the use of quantitative genetics. We present and discuss recent findings on the genetic variability and basis of nitrogen use efficiency in crops in general and in maize in particular. A genetic approach using maize recombinant inbred lines was undertaken allowing the detection of Quantitative Trait Loci (QTLs) for morphological traits, grain yield and its components under high nitrogen or low nitrogen input. Co-mapping was observed between genes encoding enzymes involved in nitrogen assimilation (nitrate reductase, glutamine synthetase) and these Quantitative Trait Loci. All coincidences were consistent with the expected physiological function of the corresponding enzyme activities. This work strongly suggests that in maize, nitrogen use efficiency can be improved both by marker-assisted selection and genetic engineering.