The Henry M. Jackson Foundation

Riboswitches are RNA-based regulatory elements that utilize ligand-induced structural changes in the 5'-untranslated region of mRNA to regulate the expression of associated genes. The majority of synthetic riboswitches have been selected and tested in cell-based systems. Cell-free protein expression systems (CFPS) have several advantages for the development and testing of synthetic riboswitches, including eliminating interactions with complex cellular networks, and the decoupling of transcription and translation processes. To gain a better understanding of the riboswitch regulatory mechanism, to allow for more efficient riboswitch optimization and use for biosensing applications, we studied the performance of a theophylline-responsive synthetic riboswitch coupled with the superfolder green fluorescent protein (sfGFP) reporter gene in E. coli cellular extract and PURE cell-free systems. To monitor the mRNA dynamics, a malachite green aptamer sequence was added to the 3'-untranslated region of sfGFP mRNA. Performance of the theophylline riboswitch was compared with a constitutively expressed sfGFP (control). Transcription dynamics of the riboswitch mRNA was very similar to the transcription of the control mRNA for all theophylline concentrations tested in both E. coli extract and PURE CFPS. However, sfGFP expression in the riboswitch construct was one order of magnitude lower, even at the highest concentration of theophylline. A mathematical model of riboswitch activation governed by the kinetic trapping mechanism was developed. Two factors - a reduced fraction of mRNA in the 'ON' state and a considerably lower translation initiation rate in the riboswitch - contribute to the much lower level of protein expression in the theophylline riboswitch compared to the control construct.

Liposomes are potent adjuvant constituents for licensed vaccines and vaccine candidates and carriers for drug delivery. Depending on the method of preparation, liposomes vary in size distribution, either forming uniform small size vesicles or a heterogeneous mixture of small to large vesicles. Importantly, differences in liposomal size have been demonstrated to induce differential immune responses. Determination of particle size distribution could therefore be crucial for the efficacy and stability of vaccine formulations. We compared the techniques of dynamic light scattering, laser diffraction, and conventional nanoparticle tracking analysis with a novel multispectral advanced nanoparticle tracking analysis (MANTA) for particle size determination of mono- and polydisperse liposomes. MANTA reported an average 146 nm size of monodisperse liposomes but showed a multimodal distribution of polydisperse liposomes with continuous sizes from 50 to 2000 nm. However, approximately 95% of particles were in the size range of 50-1500 nm and only few particles were identified in the 1500-2000 nm range for the investigated volume. Based on our results, we conclude that MANTA is the most suitable approach and can serve as stand-alone technique for particle size characterization of heterogeneous liposome samples in the 50-2000 nm size range.

There are many mechanisms of neurotoxicity that are initiated by the interaction of chemicals with different neurological targets. Under the U.S. Environmental Protection Agency's ToxCast program, the biological activity of thousands of chemicals was screened in biochemical and cell-based assays in a high-throughput manner. Two hundred sixteen assays in the ToxCast screening database were identified as targeting a total of 123 proteins having neurological functions according to the Gene Ontology database. Data from these assays were imported into the Organization for Economic Co-operation and Development QSAR Toolbox and used to predict neurological targets for chemical neurotoxins. Two sets of data were generated: one set was used to classify compounds as active or inactive and another set, composed of AC₅₀s for only active compounds, was used to predict AC₅₀ values for unknown chemicals. Chemical grouping and read-across within the QSAR Toolbox were used to identify neurologic targets and predict interactions for pyrethroids, a class of compounds known to elicit neurotoxic effects in humans. The classification prediction results showed 79% accuracy while AC₅₀ predictions demonstrated mixed accuracy compared with the ToxCast screening data.