RMH

p-Coumaric acid (p-CA) is a high-value phenolic acid with applications in food preservation, pharmaceuticals, cosmetics, and the production of plant-derived aromatic compounds. Microbial cell factories provide a sustainable alternative to plant extraction and chemical synthesis. In this review, recent advances in synthetic biology strategies for microbial p-CA biosynthesis are systematically summarized. The natural biosynthetic pathways and major heterologous routes are first introduced, followed by a review of key enzyme engineering strategies. Furthermore, metabolic engineering approaches for increasing precursor availability, enhancing metabolic flux, attenuating competing branches, blocking degradation pathways, and optimizing cofactor and transporter systems are discussed. Emerging model-guided and machine learning-assisted strategies for rational strain optimization are also highlighted. Finally, progress in chassis reinforcement, substrate expansion, and fermentation process optimization is summarized, and future opportunities for constructing robust, efficient, and industrially applicable p-CA-producing microbial cell factories are discussed.

Type III polyketide synthases (PKSs) are homodimeric enzymes that catalyze starter-substrate loading, iterative condensation, and cyclization to produce diverse natural products, including chalcones, stilbenes, cannabinoid precursors, and curcuminoids. Despite their conserved catalytic architecture, type III PKSs exhibit remarkable plasticity in starter-substrate utilization, chain extension, and cyclization chemistry. This versatility makes them attractive but challenging targets for enzyme engineering. This review examines three major strategies for improving type III PKSs: screening and genome mining, structure-guided engineering, and directed evolution coupled with high-throughput screening or selection. We discuss how these approaches can be used to improve catalytic activity, substrate specificity, and product selectivity, both in vitro and in heterologous production hosts. We also examine recent applications of artificial intelligence (AI) and machine learning (ML) to enzyme discovery, functional prediction, and variant prioritization, as well as the limitations imposed by small and heterogeneous datasets. Finally, we provide a practical framework for selecting engineering strategies and discuss future directions toward more predictable engineering of type III PKSs.

Genetic logic gates, such as inverting NOT functions, are routinely designed and optimised for a narrow set of laboratory-standard chemical inputs. This reliance limits their utility in real-world applications, where engineered chassis must navigate complex, niche-specific signals. Focusing on environmental synthetic biology, we engineered four biosensors responsive to ecologically relevant physical and chemical inputs (iron, oxygen, temperature, and light) and systematically coupled them to three repressor-based NOT logic gates (QacR, SrpR, and LitR) to generate 12 composite sensor-gate circuits. To evaluate cross-host robustness, all constructs were benchmarked in two non-model soil bacteria: Pseudomonas putida KT2440 and Pseudomonas protegens Pf-5. Our findings demonstrate that overall circuit performance is non-additive and cannot be predicted simply by summing individual sensor and gate dynamics. While standalone biosensors reliably detected their cognate inputs, concatenation across regulatory junctions introduced non-linearities, emergent dynamics, and host-dependent effects that caused several pairings to deviate unexpectedly from their predicted behavior. Furthermore, host context directly governed logic execution: P. protegens Pf-5 exhibited superior overall circuit-processing capacity, whereas specific configurations, such as iron-responsive gates, performed better in P. putida KT2440. All 12 constructs are modularly integrated using the Standard European Vector Architecture (SEVA) format, providing a benchmarked toolkit to program complex information processing in real-world environments.

Living systems have been argued to operate near critical points, but their relevance at the cellular level is poorly understood. Critical states allow for a high sensitivity to external inputs and maximal information transfer and display fluctuations at all scales. Critical points are very special, and require the fine tuning of a control parameter, which is hard to reconcile with the robustness of living cells. Self-organized criticality (SOC) proposes that feedback, rather than tuning, brings a system to its critical point. Here we show that RNA interference, in which messenger RNAs compete for shared silencing machinery and so queue for service, together with negative feedback gives rise to SOC in mammalian cells. In a synthetic circuit, a protein repressor whose mRNA competes with the mRNA of a fluorescent reporter that it represses senses the congestion of the queue, closing the feedback loop. Theory and stochastic simulations show that the loop holds the queue near its critical point despite changes in its parameters, and that expression becomes intermittent, with scale-free bursts. Measurements in human cell lines show the predicted power-law distributions of single-cell fluorescence. RNA interference could thus support engineered SOC in animal cells and may also contribute to SOC in natural systems.

Here, we report engineered bacterial spores for versatile covalent enzyme immobilization through a biomolecular click reaction (Click-TIED). This approach decouples crust protein folding and assembly during sporulation from the production of target enzymes, enabling a versatile immobilization platform. We identified a truncated CgeA protein as an effective fusion partner for successful covalent bond formation on the Bacillus subtilis spore surface. The resulting immobilized enzymes remained stable on the spore surface and showed robust, improved activity for long-term reuse for a week. This platform also enables enzyme co-immobilization for tandem catalysis and the direct harvesting of target enzymes from cell lysates. This work presents a straightforward method for covalently immobilizing enzymes on the bacterial spore, a recyclable and renewable biological bead, advancing the development of a deployable biocatalysis platform.

Bacteria deploy specialised secretion systems to transfer proteins and DNA into other cells, driving microbial evolution, virulence and the spread of antibiotic resistance. Among these, type IV secretion systems (T4SS) are unique in mediating both effector delivery and conjugative DNA transfer via extracellular pili that establish contact with recipient cells. How these molecular machines couple recipient-cell engagement to the opening of a secretion channel is a central unresolved question across direct-contact secretion systems. Here we present the first high-resolution structure of a conjugative pilus tip from the R388 T4SS. The tip comprises the major pilin VirB2 and the minor pilin VirB5 together with two unexpected components: VirB6, repositioned from the inner-membrane stalk to the pilus apex where it forms a dynamic gate, and KikA, a modular distal adhesin likely involved in recipient-cell engagement. These findings unify pilus biogenesis, entry exclusion and recipient-cell engagement into a single structural mechanism in which VirB6 acts as a gatekeeper that couples tip contact to channel opening, while KikA shapes a pointed adhesin at the apex. By comparing this architecture to the type III and type VI secretion systems, we reveal convergent gating and penetration principles to contact-dependent bacterial secretion.

Learning is a basic form of adaptation that organisms display from neural systems to single cells. In associative learning, the pairing of two stimuli lets one of them evoke the response to the other. Engineering this behavior in a living cell is difficult, because coincidence detection, memory, and conditional readout must be wired into a single regulatory network. Here we build a consortium of five E. coli strains that distributes the operations of associative learning and communicates through one diffusible quorum-sensing signal. The same signal acts as the memory trace. One strain detects the coincidence of the two inputs, a second maintains the memory through positive feedback in the shared medium, a third reads it out on the conditioned stimulus, a fourth reports the unconditioned response, and a fifth erases the memory on command. We show experimentally that the assembled consortium learns the association, since a stimulus that is silent before training evokes a strong response after pairing. Memory is retained through a medium exchange in which both stimuli are removed, and then the conditioned stimulus alone evokes the response. The reporter is not produced without its input. An erase command returns the response to the background. A population model of the consortium accounts for the sequence of responses and gives design rules for writing, retention, and erasure, and a trade-off in storing memory in a shared extracellular signal. Distributing the operations over strains makes each of them separately tunable and testable and provides a route to microbial communities that learn from their history.