Search

The phytotoxic effects of aluminum (Al) on root systems of crop plants constitute a major agricultural problem in many areas of the world. Root exudation of Al-chelating molecules such as low-molecular-weight organic acids has been shown to be an important mechanism of plant Al tolerance/resistance. Differences observed in the physiology and electrophysiology of root function for two maize genotypes with contrasting Al tolerance revealed an association between rates of Al-activated root organic acid release and Al tolerance. Using these genotypes, we cloned ZmALMT1, a maize gene homologous to the wheat ALMT1 and Arabidopsis AtALMT1 genes that have recently been described as encoding functional, Al-activated transporters that play a role in tolerance by mediating Al-activated organic acid exudation in roots. The ZmALMT1 cDNA encodes a 451 amino acid protein containing six transmembrane helices. Transient expression of a ZmALMT1::GFP chimera confirmed that the protein is targeted to the plant cell plasma membrane. We addressed whether ZmALMT1 might underlie the Al-resistance response (i.e. Al-activated citrate exudation) observed in the roots of the Al-tolerant genotype. The physiological, gene expression and functional data from this study confirm that ZmALMT1 is a plasma membrane transporter that is capable of mediating elective anion efflux and influx. However, gene expression data as well as biophysical transport characteristics obtained from Xenopus oocytes expressing ZmALMT1 indicate that this transporter is implicated in the selective transport of anions involved in mineral nutrition and ion homeostasis processes, rather than mediating a specific Al-activated citrate exudation response at the rhizosphere of maize roots.

Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the multitude of admissible perspectives for data analysis of gene expression require additional computational resources, such as hierarchical structures and dynamic allocation of resources. We present an immune-inspired hierarchical clustering device, called hierarchical artificial immune network (HaiNet), especially devoted to the analysis of gene expression data. This technique was applied to a newly generated data set, involving maize plants exposed to different aluminum concentrations. The performance of the algorithm was compared with that of a self-organizing map, which is commonly adopted to deal with gene expression data sets. More consistent and informative results were obtained with HaiNet.