Generation of Marker- and Backbone-Free Transgenic Potatoes by Site-Specific Recombination and a Bi-Functional Marker Gene in a Non-Regular One-Border Agrobacterium Transformation Vector

A binary vector, designated PROGMO, was constructed to assess the potential of the Zygosaccharomyces rouxii R/Rs recombination system for generating marker- and backbone-free transgenic potato (Solanum tuberosum) plants with high transgene expression and low copy number insertion. The PROGMO vector utilises a constitutively expressed plant-adapted R recombinase and a codA-nptII bi-functional, positive/negative selectable marker gene. It carries only the right border (RB) of T-DNA and consequently the whole plasmid will be inserted as one long T-DNA into the plant genome. The recognition sites (Rs) are located at such positions that recombinase enzyme activity will recombine and delete both the bi-functional marker genes as well as the backbone of the binary vector, leaving only the gene of interest flanked by a copy of Rs¿and RB. Efficiency of PROGMO transformation was tested by introduction of the GUS reporter gene into potato. It was shown that after 21 days of positive selection and using 300 mgl¿1 5-fluorocytosine for negative selection, 29% of regenerated shoots carried only the GUS gene flanked by a copy of Rs and RB. The PROGMO vector approach is simple and might be widely applicable for the production of marker- and backbone-free transgenic plants of many crop species

Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell