The story of the network of genes that controls fruit patterning in Arabidopsis is still unfolding, and here we have seen familiar players take on different parts and we have identified new regulators that may play important roles. Arabidopsis fruit are patterned into three major regions: the valves, the replum, and the valve margin. Previous studies have shown that the FRUITFULL (FUL) and REPLUMLESS (RPL) genes are responsible for patterning the valve and replum, respectively. To identify new regulators, we screened for mutants that showed rescued replum development in the sensitized rpl ful double mutant background. We identified one suppressor (m33) that likely identifies a new fruit patterning gene as well as another suppressor (m413) that corresponds to the well-known floral homeotic gene APETALA2 (AP2). Although previous studies have characterized the role of AP2 during flower development and its regulation by miR172, little is known about its function during fruit morphogenesis. Here we used promoter-GUS fusions to analyze the expression patterns of the five miR172 genes in reproductive and vegetative tissues and found that miR172 may be controlling AP2 activity in these tissues. In addition, we found that FUL may be an upstream regulator of miR172 in the fruit. These studies have allowed us to incorporate the role for miR172 into current models of fruit patterning. Although the final chapter to this story has yet to be written, our studies have added new insights that will undoubtedly contribute to the long-term goal of understanding the complex network of gene interactions that underlies fruit morphogenesis

The reliable forward engineering of genetic systems remains limited by the ad hoc reuse of many types of basic genetic elements. Although a few intrinsic prokaryotic transcription terminators are used routinely, termination efficiencies have not been studied systematically. Here, we developed and validated a genetic architecture that enables reliable measurement of termination efficiencies. We then assembled a collection of 61 natural and synthetic terminators that collectively encode termination efficiencies across an ∼800-fold dynamic range within Escherichia coli. We simulated co-transcriptional RNA folding dynamics to identify competing secondary structures that might interfere with terminator folding kinetics or impact termination activity. We found that structures extending beyond the core terminator stem are likely to increase terminator activity. By excluding terminators encoding such context-confounding elements, we were able to develop a linear sequence-function model that can be used to estimate termination efficiencies (r = 0.9, n = 31) better than models trained on all terminators (r = 0.67, n = 54). The resulting systematically measured collection of terminators should improve the engineering of synthetic genetic systems and also advance quantitative modeling of transcription termination.