Biology subjects

Robson, J. M.

Publications and source records attributed to Robson, J. M..

5 recordsLinked to original sources

bioRxiv2026

Crowdsourced riboregulators reveal design principles for programmable RNA switching

RNA-based sensors offer powerful and programmable control of gene expression, yet our understanding of the structural principles that govern their potential design space remains incomplete. Here, we challenged a community of designers to generate novel riboregulators capable of activating translation in response to specific RNA targets. Participants submitted diverse sequence architectures, which were synthesized and evaluated in a cell-free transcription-translation system. Across 100 designs, community-generated riboregulators displayed wide variability in activation dynamics, fold change, and structural features, outperforming some canonical toehold-switch designs and achieving up to 80-fold activation. Structural ensemble analyses identified accessibility patterns near the ribosome binding site that distinguish high- from low-performing regulators, highlighting the central role of RBS sequestration and release in modulating expression. Together, we demonstrate community-driven design can expand the accessible structural space of riboregulators and uncover mechanistic features governing translational activation. Our findings establish quantitative links between RNA folding energetics and gene expression output, providing design principles for next-generation programmable RNA sensors.

synthetic biology↗

bioRxiv2025

Automated Assembly of Programmable RNA-Based Sensors

Engineered programmable RNA sensors have been applied in low-cost diagnostics, endogenous RNA detection, and multi-input genetic circuits. However, designing, producing, and screening high-performance RNA sensors remains time consuming and labor intensive. Here, we present an automated plasmid assembly pipeline using liquid handling robotics to enable high-throughput construction of plasmids with arbitrary sequences. We compare automated and manual assembly methods using the NGS Hamilton Microlab STAR across two plasmid backbones to evaluate efficiency and reliability. As a proof of concept, we use this modular platform to construct 144 total plasmids encoding riboregulators targeting diverse viral targets along with their cognate trigger sequences. We further demonstrate that the assembled plasmids are functional in both bacterial and cell-free expression systems.

synthetic biology↗

bioRxiv2025

Toehold-VISTA: A machine learning approach to decipher programmable RNA sensor-target interactions

RNA-based biosensors have emerged as essential tools in synthetic biology and diagnostics, enabling precise and programmable responses to diverse RNA inputs. However, the time to design, produce, and screen high-performance RNA sensors remains a critical challenge. The fundamental rules governing RNA-RNA interactions--specifically the structure-function relationships that determine sensor performance--remain poorly understood. Here, we present a method enabling versatile in-silico RNA-targeting analysis (VISTA), a machine learning-guided framework for the rapid design of RNA sensors. VISTA integrates biophysical modeling of both sensor and target RNAs with a partial least squares discriminant analysis (PLS-DA) machine learning framework. Using high-throughput experimental measurements with sequence-structure feature extraction to train predictive models, we capture the key determinants of RNA sensor performance. We find that by using toehold switches as a model RNA sensor, Toehold-VISTA successfully designs RNA sensors with improved function against SARS-CoV-2 RNA. These findings establish a broadly applicable, target-aware design strategy for accelerating RNA sensor engineering across biotechnology and diagnostic applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/669990v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1f33be7org.highwire.dtl.DTLVardef@1e1f523org.highwire.dtl.DTLVardef@1a4d914org.highwire.dtl.DTLVardef@1a7eb5c_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Generative and predictive neural networks for the design of functional RNA molecules

RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence and structural properties of an RNA molecule and its ability to perform specific functions often necessitates extensive experimental screening of candidate sequences. Here we present a generalized neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions. We demonstrate that this approach achieves state-of-the-art performance across several distinct RNA prediction tasks, while learning interpretable abstractions of RNA secondary structure. We paired these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of novel mRNA 5 untranslated regions and toehold switch riboregulators exhibiting a predetermined fitness. This approach enabled the design of novel toehold switches with a 43-fold increase in experimentally characterized dynamic range compared to those designed using classic thermodynamic algorithms. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of diagnostic and therapeutic RNA molecules with improved function.

synthetic biology↗

bioRxiv2017

Raising the stakes: Loss of efflux-pump regulation decreases meropenem susceptibility in Burkholderia pseudomallei

Burkholderia pseudomallei, the causative agent of the high-mortality disease melioidosis, is a Gram-negative bacterium that is naturally resistant to many antibiotics. There is no vaccine for melioidosis, and effective eradication is reliant on biphasic and prolonged antibiotic administration. The carbapenem drug, meropenem, is the current gold-standard option for treating severe melioidosis. Intrinsic B. pseudomallei resistance towards meropenem has not yet been documented; however, resistance could conceivably develop over the course of infection, leading to prolonged sepsis and treatment failure. Here, we document 11 melioidosis cases in which B. pseudomallei isolates developed decreased susceptibility towards meropenem during treatment, including two cases not treated with this antibiotic. Meropenem minimum inhibitory concentrations increased over time from 0.5-0.75 to 3-8 g/mL. Using comparative genomics, we identified multiple mutations affecting multidrug resistance-nodulation-division (RND) efflux pump regulators, leading to over-expression of their corresponding pumps. The most commonly affected pump was AmrAB-OprA, although alterations in the local regulators of BpeEF-OprC or BpeAB-OprB were observed in three cases. This study confirms the role of RND efflux pumps in decreased meropenem susceptibility in B. pseudomallei. Further, we document two concerning examples of severe melioidosis where the reduced treatment efficacy of meropenem was associated with a fatal outcome.\n\nSignificance StatementThe bacterium Burkholderia pseudomallei, which causes the often-fatal tropical disease melioidosis, is difficult to eradicate. Due to high levels of intrinsic antibiotic resistance, only a handful of antibiotics are effective against this pathogen. One of these, meropenem, is commonly used in the treatment of melioidosis patients who are unresponsive to other treatments or are critically ill. Here, we describe 11 melioidosis cases whereby patients exhibited prolonged or repeated infections that were associated with the development of decreased meropenem susceptibility. We identified the molecular basis for this decreased susceptibility in latter B. pseudomallei isolates obtained from these patients, and functionally confirmed the mechanism conferring this phenotype. Our findings have important ramifications for the diagnosis, treatment and management of life-threatening melioidosis cases.

microbiology↗