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Preuss C.P.,University of Adelaide | Preuss C.P.,The Australian Center for Plant Functional Genomics | Huang C.Y.,University of Adelaide | Huang C.Y.,The Australian Center for Plant Functional Genomics | Tyerman S.D.,University of Adelaide
Plant, Cell and Environment | Year: 2011

High-affinity phosphate transporters mediate uptake of inorganic phosphate (P i) from soil solution under low P i conditions. The electrophysiological properties of any plant high-affinity P i transporter have not been described yet. Here, we report the detailed characterization of electrophysiological properties of the barley P i transporter, HvPHT1;1 in Xenopus laevis oocytes. A very low K m value (1.9μm) for phosphate transport was observed in HvPHT1;1, which falls within the concentration range observed for barley roots. Inward currents at negative membrane potentials were identified as nH +:P i - (n>1) co-transport based on simultaneous P i radiotracer uptake, oocyte voltage clamping and pH dependence. HvPHT1;1 showed preferential selectivity for P i and arsenate, but no transport of the other oxyanions SO 4 2- and NO 3 -. In addition, HvPHT1;1 locates to the plasma membrane when expressed in onion (Allium cepa L.) epidermal cells, and is highly expressed in root segments with dense hairs. The electrophysiological properties, plasma membrane localization and cell-specific expression pattern of HvPHT1;1 support its role in the uptake of P i under low P i conditions. © 2011 Blackwell Publishing Ltd.

Kumar P.,University of South Australia | Kumar P.,The Australian Center for Plant Functional Genomics | Huang C.,The Australian Center for Plant Functional Genomics | Cai J.,University of South Australia | And 3 more authors.
Plant and Soil | Year: 2014

Aims: Root branching is a fundamental phenotypic property of a root system. In this paper we present a system for the fully automated detection and classification of root tips in root images obtained either by 2D flat bed scanning or by 3D digital camera imaging. With our system we aim to provide a robust, efficient and accurate means of phenotyping of roots. Methods: Structural information derived from image features such as root ends and root branches is utilised for the detection and classification processes. A statistical analysis based on training data sets of root tips and non-root tips is used to assign image features to one of three different classes: non-root tips, primary root tips and lateral root tips. The automated procedure is optimised to ensure as high true detection rate and low false detection rate as possible. Results: We apply the method to images of barley, rice, and corn roots taken either by 2D scanning of washed and cut roots or digital camera images of plant roots growing in a transparent medium. The results of our detection and classification procedure are validated by a comparison with manually labelled images for all three species. Our results are also compared to two established platforms, EZ-Rhizo and WinRHIZO. Finally, we demonstrate the utility of the statistical learning approach by quantifying root phenotypic properties of barley double haploid lines. Conclusions: The method of statistical learning of characteristic features is an accurate means of not only counting root numbers, but also discriminating between primary and lateral roots. The fully-automated procedure presented in this paper can be used reliably in high throughput situations to characterise quantitative phenotypic variation. © 2014 Springer International Publishing Switzerland.

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