TY  - JOUR
A1  - Zhang, Youjun
A1  - Chen, Moxian
A1  - Siemiatkowska, Beata
A1  - Toleco, Mitchell Rey
A1  - Jing, Yue
A1  - Strotmann, Vivien
A1  - Zhang, Jianghua
A1  - Stahl, Yvonne
A1  - Fernie, Alisdair R.
T1  - A highly efficient agrobacterium-mediated method for transient gene expression and functional studies in multiple plant species
JF  - Plant Communications
N2  - Although the use of stable transformation technology has led to great insight into gene function, its application in high-throughput studies remains arduous. Agro-infiltration have been widely used in species such as Nicotiana benthamiana for the rapid detection of gene expression and protein interaction analysis, but this technique does not work efficiently in other plant species, including Arabidopsis thaliana. As an efficient high-throughput transient expression system is currently lacking in the model plant species A. thaliana, we developed a method that is characterized by high efficiency, reproducibility, and suitability for transient expression of a variety of functional proteins in A. thaliana and 7 other plant species, including Brassica oleracea, Capsella rubella, Thellungiella salsuginea, Thellungiella halophila, Solanum tuberosum, Capsicum annuum, and N. benthamiana. Efficiency of this method was independently verified in three independent research facilities, pointing to the robustness of this technique. Furthermore, in addition to demonstrating the utility of this technique in a range of species, we also present a case study employing this method to assess protein-protein interactions in the sucrose biosynthesis pathway in Arabidopsis.
KW  - transient expression
KW  - agro-infiltration
KW  - subcellular localization
KW  - protein-protein interaction
Y1  - 2019
SN  - 2590-3462
VL  - 1
IS  - 5
PB  - Science Direct
CY  - New York
ER  - 
TY  - JOUR
A1  - Ryngajllo, Malgorzata
A1  - Childs, Liam H.
A1  - Lohse, Marc
A1  - Giorgi, Federico M.
A1  - Lude, Anja
A1  - Selbig, Joachim
A1  - Usadel, Björn
T1  - SLocX  predicting subcellular localization of Arabidopsis proteins leveraging gene expression data
JF  - Frontiers in plant science
N2  - Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mito-chondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.
KW  - subcellular localization
KW  - support vector machine
KW  - prediction
KW  - gene expression
Y1  - 2011
U6  - https://doi.org/10.3389/fpls.2011.00043
SN  - 1664-462X
VL  - 2
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  -