Abstract
Understanding the mechanisms underlying plants’ adaptation to their environment will require knowledge of the genes and alleles underlying elemental composition. Modern genetics is capable of quickly, and cheaply indicating which regions of DNA are associated with particular phenotypes in question, but most genes remain poorly annotated, hindering the identification of candidate genes. To help identify candidate genes underlying elemental accumulations, we have created the known ionome gene (KIG) list: a curated collection of genes experimentally shown to change uptake, accumulation, and distribution of elements. We have also created an automated computational pipeline to generate lists of KIG orthologs in other plant species using the PhytoMine database. The current version of KIG consists of 176 known genes covering 5 species, 23 elements, and their 1588 orthologs in 10 species. Analysis of the known genes demonstrated that most were identified in the model plant Arabidopsis thaliana, and that transporter coding genes and genes altering the accumulation of iron and zinc are overrepresented in the current list.
Highlights
Understanding the complex relationships that determine plant adaptation will require detailed knowledge of the action of individual genes, the environment and their interactions
The current primary list (v1.0) consists of 176 genes from A. thaliana, O. sativa, Medicago truncatula, Triticum aestivum and Zea mays with the majority coming from A. thaliana and O. sativa (Table 1)(Figure 1)
We found 618 A.thaliana genes predicted to encode elemental transport, and only 40 of these PANTHER genes are listed in the known ionome gene (KIG) list
Summary
Understanding the complex relationships that determine plant adaptation will require detailed knowledge of the action of individual genes, the environment and their interactions. Chemical, biochemical and cell biology processes are involved in moving elements, implicating thousands of genes in every plant species. Modern genetic techniques have made it easy and inexpensive to identify hundreds to thousands of loci for traits, such as, the elemental composition (or ionome) of plant tissues. Moving from loci to genes is still difficult as the number of possible candidates is often extremely large and the ability of researchers to identify a candidate gene from its functional annotations is limited by our current knowledge and inherent biases about what is worth studying
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