Plant-associated endophytes offer promising agricultural, environmental, and biotechnological applications. Despite their potential utility, difficulties in culturing these microorganisms under laboratory conditions have limited both their isolation and a comprehensive understanding of their biology, function, and ecological role. Against this background, Pseudomonas granadensis strain CT364—isolated from the olive tree rhizosphere—emerged as a potential endophyte of interest due to its cultivability and its ability to promote rooting across diverse species, including olive trees, rapeseed, mung bean and cowpea. Genome Annotation and in silico predictions identified 564 genes linked to rhizosphere competence, plant colonisation and plant growth-promoting traits. Experimental findings confirmed the strain’s motility, capacity for biofilm formation, and ability to sense and respond to plant-derived signals. P. granadensis CT364 effectively colonises the rhizosphere, rhizoplane, and internal tissues of Arabidopsis, confirming its endophytic nature without exhibiting any pathogenic traits. Inoculation experiments demonstrated significant effects on root architecture and increases in plant biomass and rosette area. Notably, these benefits were retained under salinity and osmotic stress, underscoring its plant growth-promoting ability. Finally, both genome analysis and experimental tests confirmed its resistance to osmotic stress and heavy metal toxicity, highlighting the strain’s ability to survive in difficult environments. The integration of genomic insights and experimental validation supports the conclusion that P. granadensis CT364 is a plant growth-promoting endophytic bacterium. Its ability to enhance plant development under both optimal and stressful conditions, combined with its ability to colonise Arabidopsis and non-pathogenic nature, positions this strain as a potential bioinoculant for sustainable agriculture. Furthermore, the identification of specific genes related to plant sensing and colonisation, and its genetic tractability, open avenues for exploring underlying mechanisms of plant–microbe interactions. In summary, P. granadensis CT364 therefore not only holds potential for improving crop performance under challenging environmental conditions but also offers a valuable model for the study of beneficial plant–bacterial symbiosis.

Pseudomonas sp. CT364 was isolated from olive tree rhizosphere in Seville (Spain). We report its complete genome sequence, acquired by co-assembling Illumina and Nanopore reads. The genome comprises a circular chromosome of 6.2 Mbp and a G + C content of 60.0%. Taxonomic analyses confirmed it to be Pseudomonas granadensis.

Synthetic gene networks provide a platform for scientists and engineers to design and build novel systems with functionality encoded at a genetic level. While the dominant paradigm for the deployment of gene networks is within a cellular chassis, synthetic gene networks may also be deployed in cell-free environments. Promising applications of cell-free gene networks include biosensors, as these devices have been demonstrated against biotic (Ebola, Zika, and SARS-CoV-2 viruses) and abiotic (heavy metals, sulfides, pesticides, and other organic contaminants) targets. Cell-free systems are typically deployed in liquid form within a reaction vessel. Being able to embed such reactions in a physical matrix, however, may facilitate their broader application in a wider set of environments. To this end, methods for embedding cell-free protein synthesis (CFPS) reactions in a variety of hydrogel matrices have been developed. One of the key properties of hydrogels conducive to this work is the high-water reconstitution capacity of hydrogel materials. Additionally, hydrogels possess physical and chemical characteristics that are functionally beneficial. Hydrogels can be freeze-dried for storage and rehydrated for use later. Two step-by-step protocols for the inclusion and assay of CFPS reactions in hydrogels are presented. First, a CFPS system can be incorporated into a hydrogel via rehydration with a cell lysate. The system within the hydrogel can then be induced or expressed constitutively for complete protein expression through the hydrogel. Second, cell lysate can be introduced to a hydrogel at the point of polymerization, and the entire system can be freeze-dried and rehydrated at a later point with an aqueous solution containing the inducer for the expression system encoded within the hydrogel. These methods have the potential to allow for cell-free gene networks that confer sensory capabilities to hydrogel materials, with the potential for deployment beyond the laboratory.

Wastewater-based epidemiology (WBE) permits the sustainable surveillance of pathogens in large populations and does not discriminate between symptomatic and asymptomatic groups. WBE allows health authorities and policymakers to make swift decisions to limit the impact of local and regional disease outbreaks, minimise the spread of infection and mitigate the effects of pathogen importation. Biosensors are an exciting addition to conventional WBE analytical approaches. Combined with sentinel surveillance programs, biosensors can be reactive to novel variants of a virus in the community. However, progress developing biosensors for wastewater surveillance is severely limited compared to advances in clinical diagnostics, with a lack of well-developed biosensors currently being available. Whilst the field of biosensors is vast, this review focuses on trends in monitoring SARS-CoV-2 in wastewater over a key period (2020–2021). We explore the complexities involved in sampling within wastewater networks, the options for target selection, and reflect on the ethical considerations and limitations of this approach by highlighting the complex transdisciplinary connections needed. The outlook for WBE biosensors is assessed to be on a positive trajectory as current technical challenges are overcome. Finally, we outline the current status and where further development is needed to have a systematic feedback mechanism which would allow wastewater biosensors to be kept current and relevant to emergent pathogens.

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 E. coli, 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.

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 107 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.

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.

The variability of applied load and the integrity of lubrication are crucial factors dictating the durability of wind turbine gearbox bearings. In this work, new ultrasonic reflectometry techniques have been implemented to measure both load and lubrication in the high-speed shaft bearing of a field operational Vestas V42 wind turbine gearbox. Miniature piezoelectric transducers were bonded onto the bearing outer raceway. The reflected pulses were used to infer bearing load and lubrication. Roller load measurements were seen to vary by 33% across the roller complement, with similar trend observed for reflection coefficient. Inspection of the reflection coefficient patterns were able to show whether the roller inlet region was fully flooded or partially starved, capturing the stochastic behaviour of bearing lubrication.

Chemically defined media (CDM) for cultivation of C. necator 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 C. necator growth. Our growth model predicts that C. necator 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.

Cupriavidus necator is a Gram-negative soil bacterium of major biotechnological interest. It is a producer of the bioplastic 3-polyhydroxybutyrate, has been exploited in bioremediation processes, and it's lithoautotrophic capabilities suggest it may function as a microbial cell factory upgrading renewable resources to fuels and chemicals. It remains necessary however to develop appropriate experimental resources to permit controlled bioengineering and system optimisation of this microbial chassis. A key resource for physiological, biochemical and metabolic studies of any microorganism is a chemically defined growth medium. Here we use 1 mL micro-well cell cultures, automated liquid handling and a statistical engineering approach to develop a model that describes the effect of key media components and their interactions on C. necator culture cell density. The model is predictive and was experimentally validated against novel media compositions. Moreover, the model was further validated against larger culture volumes at 100 mL and 1 L volumes and found to correlate well. This approach provides valuable and quantifiable insights into the impact of media components on cell growth as well as providing predictions to guide culture scale-up.

Smart materials are able to alter one or more of their properties in response to defined stimuli. Our ability to design and create such materials, however, does not match the diversity and specificity of responses seen within the biological domain. We propose that relocation of molecular phenomena from living cells into hydrogels can be used to confer smart functionality to materials. We establish that cell-free protein synthesis can be conducted in agarose hydrogels, that gene expression occurs throughout the material and that co-expression of genes is possible. We demonstrate that gene expression can be controlled transcriptionally (using in gel gene interactions) and translationally in response to small molecule and nucleic acid triggers. We use this system to design and build a genetic device that can alter the structural property of its chassis material in response to exogenous stimuli. Importantly, we establish that a wide range of hydrogels are appropriate chassis for cell-free synthetic biology, meaning a designer may alter both the genetic and hydrogel components according to the requirements of a given application. We probe the relationship between the physical structure of the gel and in gel protein synthesis and reveal that the material itself may act as a macromolecular crowder enhancing protein synthesis. Given the extensive range of genetically encoded information processing networks in the living kingdom and the structural and chemical diversity of hydrogels, this work establishes a model by which cell-free synthetic biology can be used to create autonomic and adaptive materials. Significance statement Smart materials have the ability to change one or more of their properties (e.g. structure, shape or function) in response to specific triggers. They have applications ranging from light-sensitive sunglasses and drug delivery systems to shape-memory alloys and self-healing coatings. The ability to programme such materials, however, is basic compared to the ability of a living organism to observe, understand and respond to its environment. Here we demonstrate the relocation of biological information processing systems from cells to materials. We achieved this by operating small, programmable genetic devices outside the confines of a living cell and inside hydrogel matrices. These results establish a method for developing materials functionally enhanced with molecular machinery from biological systems.

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.

Biofuels are a commercial reality with ethanol comprising approximately 10% of the US retail fuel market, and biodiesels contributing a little under 5% to the EU retail fuel market. These biofuels are derived from the fermentation of sugars by yeast (ethanol) and from the chemical modification of animal fats and plant oils (biodiesel). However, these biofuel molecules are chemically distinct from the petroleum fuels that they are blended with. Petroleum-based fuels are predominantly composed of alkane and alkene hydrocarbons. These differences impact on fuel properties and infrastructure compatibility resulting in a “blend wall” that – without significant infrastructure realignment and associated costs – limits the use of biofuels. For this reason, there is great interest in biosynthetic routes for alkane and alkene production. Here we will review the known biological routes to alkane/alkene biosynthesis with a focus on bacterial alkane and alkene biosynthetic pathways. Specifically, we will review pathways for which the underlying genetic components have been identified. We will also investigate the development of engineered metabolic pathways that permit the production of alkanes and alkenes that are not naturally synthesized in bacteria (heterologous production) but are suitable for industrial commercial application. Finally, we will highlight some of the challenges facing this research area as it moves from proof-of-principle studies toward industrialization.

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.

Our PCR Simulator is a web-based application designed to introduce concepts of multi-factorial experimental design and support teaching of the polymerase chain reaction. Learners select experimental settings and receive results of their simulated reactions quickly, allowing rapid iteration between data generation and analysis. This enables the student to perform complex iterative experimental design strategies within a short teaching session. Here we provide a short overview of the user interface and underpinning model, and describe our experience using this tool in a teaching environment.

While first-generation biofuels are a market reality, research into new, drop-in biofuels is advancing rapidly. Metabolic engineering and synthetic biology are enabling the production of compounds that are more suited to the existing infrastructure and engines. This chapter explores these new biological tools and how microbial platforms and products are being designed to produce the biofuels of the future. The use of the feedstock to generate biofuels is perhaps the most contentious issue surrounding biofuel production. Metabolic engineering can, through the rational design of artificial metabolic pathways, produce biofuel chemicals to the standards and with the properties that both industry and legislation require. The production of higher alcohols is an attempt to overcome some of the limitations of ethanol. Fatty acids (FAs) phosphoglycerides and triacylclycerides (TAGs) are the primary components of cell membranes. These fats and oils, both from plant and animal sources are currently exploited as biofuels.

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 retentiontime 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.

Small piezoelectric transducers bonded to the outer raceway of a rolling element bearing can be used to send ultrasonic pulses to the element-raceway contact and receive reflections. Interpretation of the magnitude and time of flight (ToF) of the reflections can be used to deduce amount of load imparted between roller and raceway. The approach is most successful when the sensor is smaller than the area of contact. Then the sound field falls directly on the contact region. It is thus suited to large bearings, such as those found in wind turbines. In this work, we have used the approach on the rotor side high speed shaft bearing in a Vestas V42 600 kW wind turbine operating in the Barnesmore windfarm in Northern Ireland. The bearing is a SKF 32222 tapered roller bearing. The nature of the contact geometry meant that careful location of the transducer was required to ensure direct pulseecho reflection. The load directly imparted by the roller onto the raceway can be deduced from the change in time of flight of the reflected signal. This measurement is challenging because the change in time of flight is only a few nanoseconds. Nevertheless loads on each roller were determined and results were consistent with expectation. Functional over a period of 2 years, this validated method allows confident application in other sites such as pitch and main bearings to measure key inputs to prognostic algorithms derived from rig-based sensitivity studies, rather than optimistic ISO 281 calculations.

Sex determination in maize involves the production of staminate and pistillate florets from an initially bisexual floral meristem. Pistil elimination in staminate florets requires jasmonic acid signaling, and functional pistils are protected by the action of the silkless 1 (sk1) gene. The sk1 gene was identified and found to encode a previously uncharacterized family 1 uridine diphosphate glycosyltransferase that localized to the plant peroxisomes. Constitutive expression of an sk1 transgene protected all pistils in the plant, causing complete feminization, a gain-of-function phenotype that operates by blocking the accumulation of jasmonates. The segregation of an sk1 transgene was used to effectively control the production of pistillate and staminate inflorescences in maize plants.