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Sean T May

Publications and source records attributed to Sean T May.

3 recordsLinked to original sources

Changes in gene expression in Arabidopsis shoots during phosphate starvation and the potential for developing smart plants.

Our aim was to generate and prove the concept of "smart" plants to monitor plant phosphorus (P) status in Arabidopsis. Smart plants can be genetically engineered by transformation with a construct containing the promoter of a gene up-regulated specifically by P starvation in an accessible tissue upstream of a marker gene such as beta-glucuronidase (GUS). First, using microarrays, we identified genes whose expression changed more than 2.5-fold in shoots of plants growing hydroponically when P, but not N or K, was withheld from the nutrient solution. The transient changes in gene expression occurring immediately (4 h) after P withdrawal were highly variable, and many nonspecific, shock-induced genes were up-regulated during this period. However, two common putative cis-regulatory elements (a PHO-like element and a TATA box-like element) were present significantly more often in the promoters of genes whose expression increased 4 h after the withdrawal of P compared with their general occurrence in the promoters of all genes represented on the microarray. Surprisingly, the expression of only four genes differed between shoots of P-starved and -replete plants 28 h after P was withdrawn. This lull in differential gene expression preceded the differential expression of a new group of 61 genes 100 h after withdrawing P. A literature survey indicated that the expression of many of these "late" genes responded specifically to P starvation. Shoots had reduced P after 100 h, but growth was unaffected. The expression of SQD1, a gene involved in the synthesis of sulfolipids, responded specifically to P starvation and was increased 100 h after withdrawing P. Leaves of Arabidopsis bearing a SQD1::GUS construct showed increased GUS activity after P withdrawal, which was detectable before P starvation limited growth. Hence, smart plants can monitor plant P status. Transferring this technology to crops would allow precision management of P fertilization, thereby maintaining yields while reducing costs, conserving natural resources, and preventing pollution.

Arabidopsis↗

Loss of the AKT2/3 potassium channel affects sugar loading into the phloem of Arabidopsis.

Members of the AKT2/3 family have been identified as photosynthate-induced phloem K(+) channels. Here we describe the isolation and characterisation of an AKT2/3 loss-of-function mutant (akt2/3-1) from Arabidopsis thaliana (L.) Heynh. Microautoradiography following (14)CO(2) incubation in the light revealed that a major fraction of (14)CO(2)-derived photosynthates leaking out of sieve tubes appears not to be effectively reloaded (retrieval) into the phloem of the mutant. Using the aphid stylectomy technique we showed that the phloem sap of the mutant, lacking the phloem channels of the AKT2/3 type, contained only half the sucrose content of the wild type. Furthermore, the akt2/3-1 mutant exhibited a reduced K(+) dependence of the phloem potential. Xenopus oocytes expressing the phloem sucrose/proton symporter depolarise upon sucrose application. When, however, the phloem channel was co-expressed - mimicking the situation in the sieve tube/companion cell complex - depolarisation was prevented. From our studies we thus conclude that AKT2/3 regulates the sucrose/H(+) symporters via the phloem potential.

Animals↗

Finding your knockout: reverse genetics techniques for plants.

The process of finding a mutant plant for your gene of interest has recently become far more straightforward and painless than has ever been possible before. This has come about through the production of large-scale insertional mutagenesis populations. These can now be readily screened in bulk for insertional mutants through a mixture of molecular (PCR/hybridization) and bioinformation techniques. Here we describe a step-by-step guide to the molecular protocols and a description of the bioinformatics approach.

Algorithms↗