Microorganisms live in close association with plants, forming ecological interaction webs and providing beneficial traits such as nutrition, growth, and tolerance to biotic and abiotic stresses. Via the rhizosphere, plants recruit bacteria which colonize internal plant tissues, creating a spatial gradient between the rhizosphere and the endosphere. This study presents a high throughput in planta endophyte enrichment scheme designed for the identification of 'super'-endophytic bacteria which can serially colonise the rice root endosphere. Oryza sativa (rice) plants were grown in bulk soil, and endophytes were then recovered from roots. The recovered endophyte mixture was used as inoculum for the first generation of rice plantlets, which were then grown under no stress or nitrogen (N) depletion. The total endophytic community was then purified and used as a second inoculum for a new set of plants; this procedure was repeated for four generations. Enrichment patterns of root bacterial endophytes were observed, such as Kosakonia in the non-stressed plants and Ferrovibrio in plants grown under nitrogen starvation. This enrichment method proved to be suitable for the identification of endophytes which can efficiently colonise the root endosphere.

Phenazines are bioactive secondary metabolites with antifungal, anticancer, and insecticidal properties, while hydroxylated derivatives often exhibit enhanced bioactivity. 2-hydroxyphenazine (2-OH-PHZ), which is synthesised by the flavin-dependent monooxygenase PhzO from phenazine-1-carboxylic acid (PCA), shows better bioactivity against the pathogenic fungus Gaeumannomyces graminis vars. tritici. However, the low catalytic efficiency (10%–20% conversion) of PhzO limited 2-OH-PHZ production. To boost PhzO activity, engineering flavin reductase (Fre)-mediated FADH2 regeneration was applied to Pseudomonas chlororaphis LX24AE. Remarkably, this approach improved catalytic efficiency from 25% to 40% and increased the production of a novel dihydroxylated derivative. Then, it was first characterised by UPLC-MS and NMR, and identified as 3,4-dihydroxyphenazine-1-carboxylic acid (3,4-OH-PCA). Next, the Fre-PhzO module through heterologous co-expression in P. putida KT2440 demonstrated a 4.5-fold enhancement in hydroxylation efficiency relative to the PhzO mono-component system, which also confirmed that PhzO and flavin reductase are essential for 3,4-OH-PCA biosynthesis. Moreover, in vitro assays further verified that PhzO exhibits FAD-dependent catalytic promiscuity, simultaneously generating 2-OH-PCA and 3,4-OH-PCA. Furthermore, in vitro and in vivo assays demonstrated that substrate concentration affected the distribution of products. Finally, cytotoxicity evaluation of the isolated 3,4-OH-PCA was performed, and it showed substantial inhibition against oesophageal cancer TE-1 cells and human cervical cancer HeLa cells with an IC50 value of 8.55 μM and 17.69 μM, respectively. This work redefines PhzO as a promiscuous biocatalyst capable of dual hydroxylation, offering a modular platform for engineering bioactive phenazine derivatives.

Escherichia coli, a key workhorse in industrial biotechnology, is commonly grown on glucose, which supports rapid growth but leads to acetate overflow, which instead inhibits growth, diverts carbon from the production pathway, and reduces productivity. Recent studies suggest that acetate may also have beneficial effects in glucose-grown E. coli, but its potential in bioprocesses remains unexplored. In this study, we systematically investigated acetate's impact on bioproduction using a kinetic model of glucose and acetate metabolism in E. coli. The model predicts that acetate can enhance bioproduction in glucose-grown E. coli through three mechanisms: (i) by minimizing acetate overflow, thereby reducing carbon loss, (ii) by increasing acetyl-CoA levels, thereby boosting the biosynthetic flux of acetyl-CoA-derived compounds, and (iii) by promoting biomass accumulation, thus improving overall productivity. We experimentally validated the predictions of the model for mevalonate and 3-hydroxypropionate production, where acetate supplementation increased productivity by 117% and 34%, respectively. Our findings provide a valuable framework for optimizing E. coli-based bioprocesses and highlight acetate's underutilized potential in biotechnology. By leveraging acetate from waste streams as a metabolic booster, this approach could contribute to more sustainable and environmentally friendly bioprocesses.

Protein-protein conjugation systems are a powerful way of creating fusion proteins and enable the dynamic combination of protein domains with diverse functionalities. However, the insertion of these systems into enzymes is often performed with little consideration of the structural impact they might have. This is particularly relevant when modifying complex molecular machines that transition between numerous conformational states. Here, we address this issue by developing SIMPLIFE, a computational workflow that supports the design of optimal insertion sites for conjugation tags based on the structure of the proteins involved and performs localised residue redesign where needed. We demonstrate how SIMPLIFE can be used to effectively augment the function of T7 RNA polymerase using the DogCatcher-DogTag system, enabling diverse and dynamically varying mutations within a targeted region of DNA. This work demonstrates the power of combining biophysical and machine learning based approaches for protein structure prediction to efficiently augment the function of molecular machines, accelerating our ability to combine complex biochemical functionalities in new ways.

Nitrification significantly contributes to N2O emissions in wastewater treatment, typically enhanced at low dissolved oxygen (DO). The present study revealed that low DO (∼0.2 mg/L) enhanced the N2O emission factor (EF) from nitrification by 4.5 times in canonical ammonia-oxidizing bacteria (AOB)-dominated sludge, while it reduced N2O EF by 73% in comammox Nitrospira-dominated sludge. During nitrification, the accumulation of intermediate NH2OH in AOB-dominant sludge was much higher and increased more significantly by low DO (from 0.018 to 0.067 mg-N/L) compared to comammox Nitrospira-dominant sludge (from 0.004 to 0.009 mg-N/L). In AOB-dominant sludge, the increased NH2OH and upregulated AOB-NOR gene at low DO promoted NO reduction, thus increasing N2O EF while simultaneously decreasing NO emission. In the comammox Nitrospira-dominant sludge, N2O was primarily produced via abiotic pathways. Further investigation found that N2O production decreased significantly under low DO in inactivated sludge and pure water with the sole addition of NH2OH, suggesting that low DO inhibited N2O formation via abiotic NH2OH oxidation. Therefore, in comammox Nitrospira-dominant sludge, low DO decreased N2O production primarily due to the inhibition of low DO to N2O formation via abiotic NH2OH oxidation and the low NH2OH accumulation. These results imply that enriching comammox Nitrospira in the wastewater treatment process can ensure stably low N2O emissions regardless of the variations in DO.

Microbial growth is often described in terms of resource uptake rates, making the understanding and parameterisation of these rate-limiting processes critical for microbial modelling. In phototrophic plankton, theoretical studies suggest that nutrient uptake is limited by mechanistic processes involving membrane transporters, and it has been observed that the cell-specific maximum resource uptake rate (Vmax) follows a power-law relationship with cell size, as well as a trade-off between Vmax and the half-saturation constant (Km). These constraints may also apply to chemotrophic microorganisms; however, many datasets lack direct cell-size measurements. We therefore leveraged the assumption that prokaryotic cell sizes, Vmax, and Km each follow log-normal distributions, drawing parallels with established phytoplankton scaling laws. Our analysis suggests that chemotrophic organisms generally exhibit higher maximum uptake rate per dry weight (VmaxDW) and Km values than phototrophs, and that VmaxDW and Km are not strongly correlated when all chemotroph data are combined. Furthermore, the Bayesian-derived exponents for VmaxDW and Km exceeded those expected from allometric scaling relationships based on the membrane-transport capacity observed for phototrophs, implying that a range of additional factors likely affect observed kinetic parameters.

Microbial bioproduction is an important approach to realizing green biomanufacturing. However, poor bioproduction stability caused by genetic heterogeneity is one of the important factors limiting its industrial-scale applications. Here, two methods have been developed to reduce genetic heterogeneity in Bacillus subtilis. SiteMuB (the site-dependent mutation bias) was proposed to enable stable genome integration expression by analyzing the spontaneous mutation rate of the same DNA sequences integrated at different genome sites. Additionally, robustly growing chassis with low mutation rates (ChassisLMR) were developed by deleting unstable elements and enhancing DNA repair. These methods were then employed to improve the production stability of small molecule metabolites and proteins. In N-acetylneuraminic acid production, after 76 generations of cell division, corresponding to the number of cell generations required for > 200-m3 industrial-scale production, strains with SiteMuB and ChassisLMR achieved 15.9-fold and 11.1-fold higher titres than that of the starting strain, respectively. Moreover, by improving the genetic stability of burdensome T7RNAP, combining SiteMuB with ChassisLMR stably maintained the T7 expression system for up to 74 generations, representing a 2.1-fold improvement. Furthermore, ChassisLMR improved the production stability of GFP on the plasmids by 1.38-fold. Overall, SiteMuB and ChassisLMR provide broadly applicable and highly efficient ways to achieve stable bioproduction by reducing genetic heterogeneity.

Corynebacterium glutamicum serves as a pivotal industrial chassis for biomanufacturing and an ideal model for studying the phylogenetically related pathogen Mycobacterium tuberculosis. Oxidative stress poses a critical challenge to microorganisms during aerobic industrial processes and immune cell-mediated antibacterial killing by perturbing cellular redox homeostasis, affecting central metabolism, and damaging the integrity of biomacromolecules. However, the intricate mechanisms underlying the dynamic defence of C. glutamicum, despite previous transcriptomic studies on acute and adaptive responses to oxidative stresses, remain largely unclear, hindering strain engineering for industrial applications and the development of effective antimicrobial treatments. In this study, the susceptibility of C. glutamicum to hydrogen peroxide (H2O2) was evaluated, and the inhibitory dynamics of H2O2 were characterized through viable cell counting. RNA sequencing (RNA-seq) was employed to analyse gene expression changes after exposure to 720 mM H2O2. The treatment induced differential expression of 966 and 787 genes at 2 and 6 h, respectively, reflecting perturbations across a broad array of pathways, including (i) enhanced H2O2 and peroxide scavenging, mycothiol biosynthesis, and iron chelation; (ii) repressed central metabolism and enhanced anaplerosis; (iii) elevated sulphur assimilation; (iv) altered amino acid biosynthesis; and (v) altered transcriptional regulation in response to oxidative stress. Further validation by overexpression of ahpD, cysN, and exogenous supplementation with l-methionine and l-cysteine significantly enhanced bacterial tolerance to H2O2. Overall, this study provides the most comprehensive analysis to date of temporal cellular adaptation to H2O2 stress in C. glutamicum, establishing a foundation for future applications in both biomanufacturing and antimicrobial research.

Intercellular communication is essential for distributed genetic circuits operating across cells in multicellular consortia. While diverse signalling molecules have been employed--ranging from quorum sensing signals, secondary metabolites, and pheromones to peptides, and nucleic acids--phage-packaged DNA offers a highly programmable method for communicating information between cells. Here, we present a library of five M13 phagemid variants with distinct replication origins, including those based on the Standard European Vector Architecture (SEVA) family, designed to tune the growth and secretion dynamics of sender strains. We systematically characterize how intracellular phagemid copy number varies with cellular growth physiology and how this, in turn, affects phage secretion rates. In co-cultures, these dynamics influence resource competition and modulate communication outcomes between sender and receiver cells. Leveraging the intercellular CRISPR interference (i-CRISPRi) system, we quantify phagemid transfer frequencies and identify rapid-transfer variants that enable efficient, low-burden communication. The phagemid toolbox developed here expands the repertoire of available phagemids for DNA-payload delivery applications and for implementing intercellular communication in multicellular circuits.

Enhancements to crop morphology, such as the semi-dwarfing that helped drive the green revolution, are often driven by changes in gene dosage. These changes are challenging to translate across varieties and species, which slows the pace of crop improvement. Synthetic transcription factors (SynTFs) offer a rapid alternative to generate targeted alterations to gene dosage. However, the complexity of developmental pathways makes it unclear how to best apply them to predictably engineer morphology. In this work, we explore if mathematical modeling can guide SynTF-based expression modulation of genes in the signaling pathway of the phytohormone, gibberellin (GA), which is a central regulator of cell expansion, to elucidate the design principles for engineering organ size. We demonstrate that modulation of GA signaling gene expression can generate consistent dwarfing across tissues in both controlled and variable environments in the model plant Arabidopsis thaliana, and that the degree of dwarfing is dependent on the strength of regulation as predicted by modeling. We further validate the predictive power of the model by demonstrating its capacity to accurately predict the qualitative impacts of different regulatory architectures for both increasing and decreasing organ size. Finally, we show that these insights can be generalized for engineering organ size in the crop Solanum lycopersicum (tomato). This work creates a framework for predictable engineering of an agriculturally important trait, organ size, across tissues and plant species. It also serves as a proof-of-concept for how mathematical models can guide SynTF-based alterations in gene dosage to enable bottom-up design of plant phenotypes.