Cyanobacteria are photosynthetic bacteria that are currently being developed as biological production platforms. They derive energy from light and carbon from atmospheric carbon dioxide, and some species can fix atmospheric nitrogen. One advantage of developing cyanobacteria for renewable production of biofuels and other biological products is that they are amenable to genetic manipulation, facilitating bioengineering and synthetic biology. To expand the currently available genetic toolkit, we have demonstrated the utility of synthetic theophylline-responsive riboswitches for effective regulation of gene expression in four diverse species of cyanobacteria, including two recent isolates. We evaluated a set of six riboswitches driving the expression of a yellow fluorescent protein reporter in Synechococcus elongatus PCC 7942, Leptolyngbya sp. strain BL0902, Anabaena sp. strain PCC 7120, and Synechocystis sp. strain WHSyn. We demonstrated that riboswitches can offer regulation of gene expression superior to that of the commonly used isopropyl-␤-D-thiogalactopyranoside induction of a lacI q -P trc promoter system. We also showed that expression of the toxic protein SacB can be effectively regulated, demonstrating utility for riboswitch regulation of proteins that are detrimental to biomass accumulation. Taken together, the results of this work demonstrate the utility and ease of use of riboswitches in the context of genetic engineering and synthetic biology in diverse cyanobacteria, which will facilitate the development of algal biotechnology.

In addition to coding for protein sequences, RNA molecules encode a diverse set of generegulatory elements. RNA switches are one class of gene-regulatory elements that control protein expression in a manner that is dependent on the concentration of specific ligand molecules. These allosteric gene-regulatory elements have been shown as useful tools in engineering diverse cell types to display novel function. In particular, RNA switches have been used as genetically encoded biosensors and conditional controllers to direct cellular decisions based on the system's changing environment. A significant focus in the field has been the generation of novel RNA switches that are tailored for different biological systems. We review approaches that have been used to generate RNA switches, which leverage the unique physical properties of RNA and the myriad ways in which RNA can modulate gene expression.Outline 1 Naturally occurring riboswitches can serve as a starting point for designing engineered RNA switches 2 RNA switches can be rationally designed using rules-based approaches 3 Computational tools enable a more efficient RNA switch design process 4 High-throughput screening of RNA switch libraries allows for generation of highly functional switch sequences 5 Conclusions ReferencesEditors:

Plasmid-based genetic systems in Escherichia coli are a staple of synthetic biology. However, the use of plasmids imposes limitations on the size of synthetic gene circuits and the ease with which they can be placed into bacterial hosts. For instance, unique selective markers must be used for each plasmid to ensure their maintenance in the host. These selective markers are most often genes encoding resistance to antibiotics such as ampicillin or kanamycin. However, the simultaneous use of multiple antibiotics to retain different plasmids can place undue stress on the host and increase the cost of growth media. To address this problem, we have developed a method for stably transforming three different plasmids in E. coli using a single antibiotic selective marker. To do this, we first examined two different systems with which two plasmids may be maintained. These systems make use of either T7 RNA polymerase-specific regulation of the resistance gene or split antibiotic resistance enzymes encoded on separate plasmids. Finally, we combined the two methods to create a system with which three plasmids can be transformed and stably maintained using a single selective marker. This work shows that large-scale plasmid-based synthetic gene circuits need not be limited by the use of multiple antibiotic resistance genes.

Ribozyme switches are a class of RNA-encoded genetic switch that support conditional regulation of gene expression across diverse organisms. An improved elucidation of the relationships between sequence, structure, and activity can improve our capacity for de novo rational design of ribozyme switches. Here, we generated data on the activity of hundreds of thousands of ribozyme sequences. Using automated structural analysis and machine learning, we leveraged these large datasets to develop predictive models that estimate the in vivo gene-regulatory activity of a ribozyme sequence. These models supported the de novo design of ribozyme libraries with low mean basal gene-regulatory activities and new ribozyme switches that exhibit changes in gene-regulatory activity in the presence of a target ligand, producing functional switches for four out of five aptamers. Our work examines how biases in the model and the dataset that affect prediction accuracy can arise and demonstrates that machine learning can be applied to RNA sequences to predict gene-regulatory activity, providing the basis for design tools for functional RNAs.