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Limited availability of phosphate ion (Pi) reduces plant growth in natural ecosystems. Here, we report the functional effects of overexpressing an Arabidopsis thaliana purple acid phosphatase encoding gene, AtPAP18, in Nicotiana tabbacum as a crop model plant. Transgenic tobacco plants exhibited significant increases in acid phosphatase activity, total P and Pi contents leading to improved biomass production in both Pi-deficient and Pi-sufficient conditions. Transient expression of AtPAP18::green fluorescent fusion protein in onion epidermal cells indicated that AtPAP18 is a dual-targeted protein, which is detected mainly in the apoplast of the cells after 24 h and in the vacuole after 72 h. Possibly, AtPAP18 protein confers efficient retrieval of Pi from bonded extracellular compounds as well as expendable intracellular Pi-monoesters and anhydrides. These data clearly indicate that overexpression of AtPAP18 gene offers an effective approach for reducing the consumption of chemical Pi fertilizer through increased acquisition of soil Pi and mobilization of internal resources.

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Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.

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