Kilby, P. | bioRxiv Science

bioRxiv2024

MMINT: a Metabolic Model Interactive Network Tool for the exploration and comparative visualisation of metabolic networks

Genome-scale metabolic models (GEMs) are essential tools in systems and synthetic biology, enabling the mathematical simulation of metabolic pathways encoded in genomes to predict phenotypes. The complexity of GEMs, however, can often limit the interpretation and comparison of their outputs. Here, we present MMINT (Metabolic Modelling Interactive Network Tool), designed to facilitate the exploration and comparison of metabolic networks. MMINT employs GEM networks and flux solutions derived from Constraint Based Analysis (e.g. Flux Balance Analysis) to create interactive visualizations. This tool allows for seamless toggling of source and target metabolites, network decluttering, enabling exploration and comparison of flux solutions by highlighting similarities and differences between metabolic states, which enhances the identification of mechanistic drivers of phenotypes. We demonstrate MMINTs capabilities using the Pyrococcus furiosus GEM, showcasing its application in distinguishing the metabolic drivers of acetate- and ethanol-producing phenotypes. By providing an intuitive and responsive model-exploration experience, MMINT addresses the need for a tool that simplifies the interpretation of GEM outputs and supports the discovery of novel metabolic engineering strategies. MMINT is available at https://doi.org/10.6084/m9.figshare.26409328 Graphical abstractMMINT functionalities provide an intuitive and responsive model-exploration experience, enabling flux solution comparison and the identification of metabolic drivers of phenotypes O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/606923v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@163d42eorg.highwire.dtl.DTLVardef@ff2922org.highwire.dtl.DTLVardef@1e5881aorg.highwire.dtl.DTLVardef@4a8bf0_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

Optimising the production of dsRNA biocontrols in microbial systems using multiple transcriptional terminators.

Crop pests and pathogens annually cause over $100 billion in global crop damage, with insects consuming 5-20% of major grain crops. Current crop pest and disease control strategies rely on insecticidal and fungicidal sprays, plant genetic resistance, transgenes and agricultural practices. dsRNA is emerging as a novel sustainable method of plant protection as an alternative to traditional chemical pesticides. Successful commercialisation of dsRNA based biocontrols requires the economical production of large quantities of dsRNA combined with suitable delivery methods to ensure RNAi efficacy against the target pest. In this study, we have optimised the design of plasmid DNA constructs to produce dsRNA biocontrols in E. coli, by employing a wide range of alternative synthetic transcriptional terminators prior to measurement of dsRNA yield. We demonstrate that a 7.8-fold increase of dsRNA was achieved using triple synthetic transcriptional terminators within a dual T7 dsRNA production system compared to the absence of transcriptional terminators. Moreover, our data demonstrates that batch fermentation production dsRNA using multiple transcriptional terminators is scalable and generates significantly higher yields of dsRNA generated in the absence of transcriptional terminators at both small-scale batch culture and large-scale fermentation. In addition, we show that application of these dsRNA biocontrols expressed in E. coli cells results in increased insect mortality. Finally, novel mass spectrometry analysis was performed to determine the precise sites of transcriptional termination at the different transcriptional terminators providing important further mechanistic insight.

synthetic biology↗