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SLocX predicting subcellular localization of Arabidopsis proteins leveraging gene expression data
(2011)
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.
The crucial role of carbohydrate in plant growth and morphogenesis is widely recognized. In this study, we describe the characterization of nana, a dwarf Arabidopsis (Arabidopsis thaliana) mutant impaired in carbohydrate metabolism. We show that the nana dwarf phenotype was accompanied by altered leaf morphology and a delayed flowering time. Our genetic and molecular data indicate that the mutation in nana is due to a transfer DNA insertion in the promoter region of a gene encoding a chloroplast-located aspartyl protease that alters its pattern of expression. Overexpression of the gene (oxNANA) phenocopies the mutation. Both nana and oxNANA display alterations in carbohydrate content, and the extent of these changes varies depending on growth light intensity. In particular, in low light, soluble sugar levels are lower and do not show the daily fluctuations observed in wild-type plants. Moreover, nana and oxNANA are defective in the expression of some genes implicated in sugar metabolism and photosynthetic light harvesting. Interestingly, some chloroplast-encoded genes as well as genes whose products seem to be involved in retrograde signaling appear to be down-regulated. These findings suggest that the NANA aspartic protease has an important regulatory function in chloroplasts that not only influences photosynthetic carbon metabolism but also plastid and nuclear gene expression.