Functional Dissection of Sugar Signals Affecting Gene Expression in Arabidopsis thaliana

Background: Sugars modulate expression of hundreds of genes in plants. Previous studies on sugar signaling, using intact plants or plant tissues, were hampered by tissue heterogeneity, uneven sugar transport and/or inter-conversions of the applied sugars. This, in turn, could obscure the identity of a specific sugar that acts as a signal affecting expression of given gene in a given tissue or cell-type. Methodology/Principal Findings: To bypass those biases, we have developed a novel biological system, based on stem-cell-like Arabidopsis suspension culture. The cells were grown in a hormone-free medium and were sustained on xylose as the only carbon source. Using functional genomics we have identified 290 sugar responsive genes, responding rapidly (within 1 h) and specifically to low concentration (1 mM) of glucose, fructose and/or sucrose. For selected genes, the true nature of the signaling sugar molecules and sites of sugar perception were further clarified using non-metabolizable sugar analogues. Using both transgenic and wild-type A. thaliana seedlings, it was shown that the expression of selected sugar-responsive genes was not restricted to a specific tissue or cell type and responded to photoperiod-related changes in sugar availability. This suggested that sugar-responsiveness of genes identified in the cell culture system was not biased toward heterotrophic background and resembled that in whole plants. Conclusions: Altogether, our research strategy, using a combination of cell culture and whole plants, has provided an unequivocal evidence for the identity of sugar-responsive genes and the identity of the sugar signaling molecules, independently from their inter-conversions or use for energy metabolism

Adaptive lifting scheme with sparse criteria for image coding

International audienceLifting schemes (LS) were found to be efficient tools for image coding purposes. Since LS-based decompositions depend on the choice of the prediction/update operators, many research efforts have been devoted to the design of adaptive structures. The most commonly used approaches optimize the prediction filters by minimizing the variance of the detail coefficients. In this article, we investigate techniques for optimizing sparsity criteria by focusing on the use of an a"" (1) criterion instead of an a"" (2) one. Since the output of a prediction filter may be used as an input for the other prediction filters, we then propose to optimize such a filter by minimizing a weighted a"" (1) criterion related to the global rate-distortion performance. More specifically, it will be shown that the optimization of the diagonal prediction filter depends on the optimization of the other prediction filters and vice-versa. Related to this fact, we propose to jointly optimize the prediction filters by using an algorithm that alternates between the optimization of the filters and the computation of the weights. Experimental results show the benefits which can be drawn from the proposed optimization of the lifting operators