End-product inhibition represents a major challenge in the microbial synthesis of various value-added chemicals. Cadaverine, a key monomer for polyamide synthesis, exhibits severe cytotoxicity, limiting its high-titer biosynthesis. Here, transcriptomic analysis and genome-wide library screening were integrated to systematically elucidate the cytotoxic mechanisms of cadaverine in Escherichia coli (E. coli) and identify beneficial genes for enhanced tolerance and overproduction. Transcriptomic analysis revealed that high concentrations of cadaverine disrupted cell membrane integrity and impaired oxidative phosphorylation, leading to redox imbalance and reactive oxygen species (ROS) accumulation. Subsequent genome-wide screening further confirmed these toxicity mechanisms and uncovered crucial cellular defense strategies. Functional validation highlighted the important role of NikR, UbiE, and YcbX in enhancing membrane integrity, restoring respiratory function and ROS homeostasis, or scavenging 6-N-hydroxylaminopurine (6-HAP) to prevent DNA damage. Among these, YcbX emerged as the most effective target for improving production. Consequently, we constructed a robust E. coli strain by implementing a dynamic regulation system for YcbX expression under cadaverine-responsive promoters, which significantly enhanced cadaverine biosynthesis to 87.2 g/L (a 46.8% enhancement). This work provides an in-depth understanding of cadaverine toxicity and tolerance, offering valuable targets and strategies for the rational design of high-performance microbial cell factories for diamines.
Artificial membraneless organelles (MLOs) are emerging as spatial organizers in synthetic biology, yet their applications remain largely confined to post-translational regulation. Here, we engineer synthetic condensates that act as modular hubs for targeted mRNA sequestration, enabling programmable post-transcriptional control in Escherichia coli. By incorporating orthogonal RNA-binding protein-aptamer interaction into the compartment, the designed MLOs can enhance translation through the integration of the translation machinery or repress specific pathways by isolating target transcripts. Tailoring condensate properties allows flexible functional optimization. We demonstrate rescue of bacterial cell division by sequestering a division inhibitor mRNA, and significantly improve the production of lacto-N-tetraose and lacto-N-neotetraose through compartmentalization or suppression of competing metabolic genes. Our work expands the functional repertoire of synthetic MLOs beyond enzyme scaffolding to encompass RNA-centric regulation, offering an adaptable strategy for reprogramming cellular functions in synthetic biology and biomanufacturing.
In this study, we investigated how methionine availability influences the phenotype and metabolism of the methionine-auxotrophic E. coli ΔmetA strain. To identify metabolic and physiological changes, the strain was cultured under two conditions: i) a methionine excess condition, where 1 mM of methionine was added to the medium; and ii) a methionine limited condition, where the medium did not contain any methionine, but instead methionine was fed slowly to maintain methionine concentration below 1 μM. To quantify metabolic fluxes, isotopic tracers [1,2-13C]glucose and [1,6-13C]glucose were applied and 13C-metabolic flux analysis (13C-MFA) was performed. To obtain acceptable fits of the labeling data the metabolic network model had to be updated. First, secretion reactions for four amino acids, i.e. glutamate, threonine, lysine and glycine, were added, which were found to accumulate in the medium under methionine limitation, both under growth and non-growth conditions. Second, a reaction was added to one-carbon metabolism that explicitly captures the incorporation of labeled methyl carbon derived from glucose into methionine. This cycle has thus far been overlooked in 13C-MFA models, however, our results suggest that taking the methylation of methionine into consideration is critical for successfully implementation of 13C-MFA. Under methionine limitation, we observed: 1) reduced oxidative pentose phosphate pathway flux; 2) significant increase in the TCA cycle flux and anaplerotic flux into the TCA cycle; 3) activation of a normally dormant pathway from threonine to glycine; 4) secretion of glutamate, threonine, lysine and glycine; and 5) recycling of 15% of intracellular methionine through the methylation cycle. In contrast, when the ΔmetA strain was grown in the presence of excess methionine, all of the unique features of this strain were masked and the flux phenotype simply reflected wild-type E. coli phenotype with only minor flux changes directly related to the gene knockout itself. We also performed co-culture experiments using ΔmetA and ten different auxotrophic E. coli strains, and cultured the same strains on spent medium from ΔmetA culture. We found that the growth behavior of the co-cultures matched with the growth behavior observed for the auxotrophic strains grown on ΔmetA's spent medium. Taken together, this study presents a robust, high-resolution approach to dissect nutrient-limited metabolism and provides novel insights that can advance our understanding of syntrophic interactions in microbial communities.
GENKI (Generative ENsemble KPI-Informed) is a variational autoencoder-based framework for large-scale kinetic modeling of metabolism. Developed for metabolic engineering applications, GENKI is designed to improve the recovery of kinetically feasible models that reproduce experimentally observed phenotypes under genetic and environmental perturbations. The framework is trained on feasible kinetic model ensembles and uses phenotype-based key performance indicators (KPIs), derived from multi-omics and bioprocess data, to label and enrich models according to their agreement with mutant and condition-specific observations. This enables targeted generation of biologically relevant parameter sets with improved predictive performance. Crucially, GENKI recovers kinetic parameter sets that jointly reproduce wild-type and multiple perturbed physiologies within a single model. We apply GENKI to large-scale kinetic models of Escherichia coli and Saccharomyces cerevisiae under enzyme perturbations and oxygen shifts. In both systems, GENKI enriches kinetic ensembles with models that more accurately reproduce experimentally observed physiologies across multiple perturbations and conditions. GENKI therefore provides a practical framework for perturbation-aware kinetic model refinement within iterative Design-Build-Test-Learn workflows.
The heterologous production of terpene in microbial hosts is often limited by inefficient and unstable pathway expression, creating a major bottleneck for industrial-scale synthesis. While E. coli as a chassis offers significant advantages, such as rapid growth, ease of cultivation, and genetic tractability. Its endogenous supply of terpenoid precursors remains a critical constraint, fundamentally restricting high-yield production. To address this challenge, we developed a genomically integrated Mevalonate (MVA) pathway from Actinomycetota in E. coli BL21(DE3) to enhance terpene precursor supply. Our approach began with an in silico multi-layer global genome mining analysis of 25,261 Actinomycetota genomes to identify a series of MVA pathway enzymes with potentially high catalytic efficiency, created a high-efficiency chassis E. coli MVA platform (ecMVA-1 and ecMVA-2) for terpene precursor synthesis. Its functionality was validated by testing eight distinct TSs. Among them, the fermentation of artemisinin precursor amorphadiene using a 5-liter bioreactor yielded 947.80 mg/L. These results indicated that E. coli (MVA) is well-suited for TS studies in the laboratory as well as holding significant promise for industrial applications. In addition, this in silico approach offers a new perspective for metabolic engineering and provides potential reservoir of diverse chassis for the industrial production of terpenoid-derived compounds.
