Methods for Embedding Cell-Free Protein Synthesis Reactions in Macro-Scale Hydrogels

Synthetic gene networks provide a platform for scientists and engineers to design and build novel systems with functionality encoded at a genetic level. ...

Newcastle University

European common ash, Fraxinus excelsior, is currently threatened by Ash dieback (ADB) caused by the fungus, Hymenoscyphus fraxineus. To detect and identify metabolites that may be products of pathways important in contributing to resistance against H. fraxineus, we performed untargeted metabolomic profiling on leaves from five high-susceptibility and five low-susceptibility F. excelsior individuals identified during Danish field trials. We describe in this study, two datasets. The first is untargeted LC-MS metabolomics raw data from ash leaves with high-susceptibility and low-susceptibility to ADB in positive and negative mode. These data allow the application of peak picking, alignment, gap-filling and retention-time correlation analyses to be performed in alternative ways. The second, a processed dataset containing abundances of aligned features across all samples enables further mining of the data. Here we illustrate the utility of this dataset which has previously been used to identify putative iridoid glycosides, well known anti-herbivory terpenoid derivatives, and show differential abundance in tolerant and susceptible ash samples.

Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease.

Multifactorial approaches can quickly and efficiently model complex, interacting natural or engineered biological systems in a way that traditional one-factor-at-a-time experimentation can fail to do. We applied a Design of Experiments (DOE) approach to model ethanol biosynthesis in yeast, which is well-understood and genetically tractable, yet complex. Six alcohol dehydrogenase (ADH) isozymes catalyze ethanol synthesis, differing in their transcriptional and post-translational regulation, subcellular localization, and enzyme kinetics. We generated a combinatorial library of all ADH gene deletions and measured the impact of gene deletion(s) and environmental context on ethanol production of a subset of this library. The data were used to build a statistical model that described known behaviors of ADH isozymes and identified novel interactions. Importantly, the model described features of ADH metabolic behavior without explicit a priori knowledge. The method is therefore highly suited to understanding and optimizing metabolic pathways in less well-understood systems.

Design of Experiments Methodology to Build a Multifactorial Statistical Model Describing the Metabolic Interactions of Alcohol Dehydrogenase Isozymes in the Ethanol Biosynthetic Pathway of the Yeast Saccharomyces cerevisiae.

Well-characterized promoter collections for synthetic biology applications are not always available in industrially relevant hosts. We developed a broadly applicable method for promoter identification in atypical microbial hosts that requires no a priori understanding of cis-regulatory element structure. This novel approach combines bioinformatic filtering with rapid empirical characterization to expand the promoter toolkit and uses machine learning to improve the understanding of the relationship between DNA sequence and function. Here, we apply the method in Geobacillus thermoglucosidasius, a thermophilic organism with high potential as a synthetic biology chassis for industrial applications. Bioinformatic screening of G. kaustophilus, G. stearothermophilus, G. thermodenitrificans, and G. thermoglucosidasius resulted in the identification of 636 100 bp putative promoters, encompassing the genome-wide design space and lacking known transcription factor binding sites. Eighty of these sequences were characterized in vivo, and activities covered a 2-log range of predictable expression levels. Seven sequences were shown to function consistently regardless of the downstream coding sequence. Partition modeling identified sequence positions upstream of the canonical -35 and -10 consensus motifs that were predicted to strongly influence regulatory activity in Geobacillus, and artificial neural network and partial least squares regression models were derived to assess if there were a simple, forward, quantitative method for in silico prediction of promoter function. However, the models were insufficiently general to predict pre hoc promoter activity in vivo, most probably as a result of the relatively small size of the training data set compared to the size of the modeled design space.

Rapid, Heuristic Discovery and Design of Promoter Collections in Non-Model Microbes for Industrial Applications.

We report a method for embedding cell-free protein synthesis reactions in macro-scale hydrogel materials without a free liquid phase. This paper focuses on methods of preparation for a variety of hydrogels and an investigation of the impact that the hydrogel material has on cell-free protein synthesis.

Cell-free protein synthesis in hydrogel materials.

 H16 is gaining significant attention as a microbial chassis for range of biotechnological applications. While the bacterium is a major producer of bioplastics, its lithoautotrophic and versatile metabolic capabilities make the bacterium a promising microbial chassis for biofuels and chemicals using renewable resources. It remains necessary to develop appropriate experimental resources to permit controlled bioengineering and system optimization of this microbe. In this study, we employed statistical design of experiments to gain understanding of the impact of components of defined media on  growth and built a model that can predict the bacterium's cell density based on medium components. This highlighted medium components, and interaction between components, having the most effect on growth: fructose, amino acids, trace elements, CaCl, and NaHPO contributed significantly to growth ( values of <-1.65 or >1.65); copper and histidine were found to interact and must be balanced for robust growth. Our model was experimentally validated and found to correlate well ( = 0.85). Model validation at large culture scales showed correlations between our model-predicted growth ranks and experimentally determined ranks at 100 ml in shake flasks (ρ = 0.87) and 1 liter in a bioreactor (ρ = 0.90). Our approach provides valuable and quantifiable insights on the impact of medium components on cell growth and can be applied to model other  responses that are crucial for its deployment as a microbial chassis. This approach can be extended to other nonmodel microbes of medical and industrial biotechnological importance. Chemically defined media (CDM) for cultivation of  vary in components and compositions. This lack of consensus makes it difficult to optimize new processes for the bacterium. This study employed statistical design of experiments (DOE) to understand how basic components of defined media affect  growth. Our growth model predicts that  can be cultivated to high cell density with components held at low concentrations, arguing that CDM for large-scale cultivation of the bacterium for industrial purposes will be economically competitive. Although existing CDM for the bacterium are without amino acids, addition of a few amino acids to growth medium shortened lag phase of growth. The interactions highlighted by our growth model show how factors can interact with each other during a process to positively or negatively affect process output. This approach is efficient, relying on few well-structured experimental runs to gain maximum information on a biological process, growth.

Applying Statistical Design of Experiments To Understanding the Effect of Growth Medium Components on Cupriavidus necator H16 Growth.

Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment.

Plant pest surveillance: from satellites to molecules.

Cupriavidus necator H16 is a chemolithoautotroph with a range of industrial biotechnological applications. Advanced metabolic engineering in the bacterium, however, is impeded by low transformation efficiency, making it difficult to introduce and screen new genetic functions rapidly. This study systematically characterized the broad host range plasmids pBHR1, pBBR1MCS-2 and pKT230 used frequently for C. necator engineering. Kanamycin resistance cassette (KanR) and a truncated sequence of the replication origin (Rep) are contributing factors to C. necator low electroporation transformation efficiency. Consequently, a series of modular minimal plasmids, named pCAT, were constructed. pCAT vectors transform C. necator H16 with a > 3000-fold higher efficiency (up to 10 CFU/μg DNA) compared to control plasmids. Further, pCAT vectors are highly stable, expressing reporter proteins over several days of serial cultivation in the absence of selection pressure. Finally, they can be assembled rapidly from PCR or synthesized DNA fragments, and restriction-ligation reactions can be efficiently electroporated directly into C. necator, circumventing the requirement to use Escherichia coli for plasmid maintenance or propagation. This study demonstrates that an understanding of the behaviour of the constituent parts of plasmids in a host is key to efficient propagation of genetic information, while offering new methods for engineering a bacterium with desirable industrial biotechnological features.

pCAT vectors overcome inefficient electroporation of Cupriavidus necator H16.

Cell-free protein synthesis (CFPS) reactions have grown in popularity with particular interest in applications such as gene construct prototyping, biosensor technologies and the production of proteins with novel chemistry. Work has frequently focussed on optimising CFPS protocols for improving protein yield, reducing cost, or developing streamlined production protocols. Here we describe a statistical Design of Experiments analysis of 20 components of a popular CFPS reaction buffer. We simultaneously identify factors and factor interactions that impact on protein yield, rate of reaction, lag time and reaction longevity. This systematic experimental approach enables the creation of a statistical model capturing multiple behaviours of CFPS reactions in response to components and their interactions. We show that a novel reaction buffer outperforms the reference reaction by 400% and importantly reduces failures in CFPS across batches of cell lysates, strains of , and in the synthesis of different proteins. Detailed and quantitative understanding of how reaction components affect kinetic responses and robustness is imperative for future deployment of cell-free technologies.

Thomas P. Howard

School of Natural and Environmental Sciences

Newcastle University

Methods for Embedding Cell-Free Protein Synthesis Reactions in Macro-Scale Hydrogels

Thomas P. Howard

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}School of Natural and Environmental Sciences

Newcastle University

Methods for Embedding Cell-Free Protein Synthesis Reactions in Macro-Scale Hydrogels

School of Natural and Environmental Sciences