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Instant Taq: Rapid Autoinducible Expression and Chromatography-free Purification of Taq polymerase.

DNA modifying enzymes are ubiquitous reagents in synthetic biology. Producing these enzymes often requires large culture volumes, purified nucleases and chromatographic separations to make enzymes of necessary quality. We sought to leverage synthetic biology tools to develop engineered strains allowing for not only the production but rapid purification of these reagents. Toward this goal, we report an E. coli strain enabling the rapid production and purification of Taq polymerase. The method relies on 1) autoinducible expression achieving high protein titers, 2) autolysis and auto DNA/RNA hydrolysis via lysozyme and a mutant benzonase, and 3) heat denaturation under reducing conditions to precipitate contaminating proteins including the mutant benzonase. Taq polymerase is obtained at high purities (>95% pure by SDS-PAGE) and is readily usable in standard reactions. The method takes less than 1 hour of hands-on time, does not require special equipment, expensive reagents or affinity purification. We expect this simple methodology and approach will improve access not only to Taq polymerase but to numerous additional commonly utilized reagent proteins. HighlightsO_LIProtein titers ~ 1g/L achieved in 20 mL shakeflasks. C_LIO_LI4 mg of purified Taq (corresponding to 5,000 Units, or 4,000 PCR reactions) per 20 mL shake flask. C_LIO_LIInstant Taq is equivalent to commercial preparations in routine PCR C_LI

synthetic biology↗

A Hidden Proportional Feedback Mechanism Underlies Enhanced Dynamic Performance and Noise Rejection in Sensor-Based Antithetic Integral Control

Efficient regulation of cellular processes is essential for both endogenous and synthetic biological processes. The design of biomolecular feedback controllers that achieve robust and timely regulation is the subject of considerable research at the interface between synthetic biology and control theory. Integral feedback controllers, known for their ability to confer the property of Robust Perfect Adaptation (RPA), are increasingly becoming common features in biological control design. Antithetic integral feedback (AIF) controllers, in particular, have enabled effective chemical reaction realizations of integral controllers that deliver RPA in both deterministic and stochastic settings. This paved the way to experimental implementations of integral controllers in bacterial and mammalian cells. While AIF controllers deliver favorable adaptation properties, they do not necessarily lead to good transient performance or noise reduction properties and may in some cases lead to increased overshoot or cell-to-cell variability. These limitations are commonly circumvented by augmenting new circuitry that realize proportional or derivative feedback mechanisms to enhance dynamic and noise rejection features without affecting the AIF controllers adaptation properties. In this paper, we report that a sensor-based variant of the basic AIF motif exhibits favorable transient dynamic properties and (as reported elsewhere) reduced noise variance. We show that these features are attributed to a "hidden" proportional feedback component that is inherent in the controller structure and that such mechanism is solely responsible for the controllers underlying enhanced dynamic performance and noise rejection properties. This sensor-based AIF controller hence offers a minimal biomolecular realization of Proportional-Integral (PI) control, whereby both integral and proportional feedback mechanisms are achieved through a single actuation reaction.

synthetic biology↗

Tailor-made sRNAs: a toolbox to control metabolic targets

Pseudomonas putida is a highly attractive production system for industrial needs. Modulation of gene expression is an urgent need to redesign P. putida metabolism for its improvement as biocatalyst at industrial level. We report the construction of a small RNA-based system with potential to be used for different purposes in synthetic biology. Due to their modular composition, design facilities and ability in tuning gene expression, sRNAs constitute a powerful tool in genetic and metabolic engineering. In the toolbox presented here, the synthetic sRNA is specifically directed to any region of a chosen target. The expression of the synthetic sRNAs is shown to differentially modulate the level of endogenous and reporter genes. The antisense interaction of the sRNA with the mRNA results in different outcomes. Depending on the particularity of each sRNA-target mRNA pair, we managed to demonstrate the duality of this system, able either to repress or overexpress a given gene. This system combines a high specificity with a wide applicability due to its ability to modulate the expression of virtually any given gene. By plugging-in and -out genetic circuits, this tailor-made regulatory system can be used to redesign P. putida metabolism, fulfilling an important industrial gap in synthetic biology.

synthetic biology↗

Linear-double-stranded DNA (ldsDNA) based AND logic computation in mammalian cells

Synthetic biology employs engineering principles to redesign biological system for clinical or industrial purposes. The development and application of novel genetic devices for genetic circuits construction will facilitate the rapid development of synthetic biology. Here we demonstrate that mammalian cells could perform two- and three-input linear-double-stranded DNA (ldsDNA) based Boolean AND logic computation. Through hydrodynamic ldsDNA delivery, two-input ldsDNA-base AND-gate computation could be achieved in vivo. Inhibition of DNA-PKcs expression, a key enzyme in non-homologous end joining (NHEJ), could significantly downregulate the intensity of output signals from ldsDNA-based AND-gate. We further reveal that in mammalian cells ldsDNAs could undergo end processing and then perform AND-gate calculation to generate in-frame output proteins. Moreover, we show that ldsDNAs or plasmids with identical overlapping sequences could also serve as inputs of AND-gate computation. Our work establishes novel genetic devices and principles for genetic circuits construction, thus may open a new gate for the development of new disease targeting strategies and new protein genesis methodologies.

synthetic biology↗

Exploiting noise, nonlinearity, and feedback to differentially control multiple synthetic cells with a single optogenetic input

Synthetic biology seeks to develop modular bio-circuits that combine to produce complex, controllable behaviors. These designs are often subject to noisy fluctuations and uncertainties, and most modern synthetic biology design processes have focused to create robust components to mitigate the noise of gene expression and reduce the heterogeneity of single-cell responses. However, deeper understanding of noise can achieve control goals that would otherwise be impossible. We explore how an "Optogenetic Maxwell Demon" could selectively amplify noise to control multiple cells using single-input-multiple-output (SIMO) feedback. Using data-constrained stochastic model simulations and theory, we show how an appropriately selected stochastic SIMO controller can drive multiple different cells to different user-specified configurations irrespective of initial condition. We explore how controllability depends on cells regulatory structures, the amount of information available to the controller, and the accuracy of the model used. Our results suggest that gene regulation noise, when combined with optogenetic feedback and non-linear biochemical auto-regulation, can achieve synergy to enable precise control of complex stochastic processes.

synthetic biology↗

Emergent Threshold Dynamics in Unified Metabolic-Regulatory Models: Achieving Spontaneous Homeostasis Through Bidirectional ATP-Pathway Coupling

BackgroundFor decades, computational biology has failed to create unified models where metabolic state and regulatory control are bidirectionally coupled: metabolic models optimize flux but cannot represent dynamic regulation, while regulatory models treat ATP as a fixed parameter rather than a dynamic variable affected by pathway activity. This fundamental limitation prevents computational recapitulation of emergent threshold behaviors--spontaneous homeostasis, adaptive reorganization, pathway switching--observed in living organisms. The challenge requires formalisms where (1) metabolic state governs regulatory decisions AND (2) regulatory choices consume metabolic resources, producing emergent dynamics from feedback rather than programming. MethodsWe introduce Signal Hierarchical Petri Nets, extending Hybrid Petri Nets with bidirectional metabolic-regulatory coupling through energy-dependent layer organization. Unlike classical approaches, ATP is simultaneously a regulatory signal (governing pathway availability through quantitative thresholds) and a material substrate (consumed by pathway activity). When ATP depletes below 1000 {micro}M, high-cost pathways automatically become unavailable; pathway activity consuming ATP creates feedback affecting subsequent pathway accessibility. This bidirectional coupling enables emergent threshold behaviors impossible in classical formalisms. We demonstrate the paradigm through macrocyclic peptide transport across 53 metabolic conditions, where drug accumulation depends on ATP-governed pathway reorganization. ResultsThe formalism produces three emergent behaviors never achieved in unified metabolic-regulatory models. (1) Spontaneous homeostasis without programming: Despite 113-fold permeability variation from N-methylation, ATP-replete cells maintain constant drug accumulation (CV=0.066%)--homeostatic compensation emerges from ATP-consumption feedback, not explicit control logic. (2) Threshold-triggered reorganization: ATP depletion to 300 {micro}M triggers 8533-fold active-to-passive transport shifts with paradoxical 141% accumulation increase from efflux collapse. (3) Tissue-specific dynamics from identical parameters: Tumor hypoxia (ATP=1200 {micro}M) versus normal tissue (ATP=5000 {micro}M) produces 6.62-fold selectivity differences from differential pathway accessibility--same model, different emergent outcomes. Computational predictions achieve r=0.911 correlation with experimental cyclosporin permeability (n=32). ConclusionsSignal Hierarchical Petri Nets represent the first computational formalism achieving emergent threshold dynamics through bidirectional metabolic-regulatory coupling. The paradigm enables in silico recapitulation of adaptive cellular behaviors previously impossible to model, with applications extending beyond drug transport to any biological system where metabolic state governs regulatory reorganization: cancer metabolism, ischemia, synthetic biology, and aging research. Author SummaryLiving cells exhibit remarkable adaptive behaviors: they maintain stable internal conditions despite environmental changes (homeostasis), reorganize their biochemical machinery when energy runs low, and switch between operating modes at precise threshold values. For decades, computational biologists have struggled to build models that recapitulate these emergent behaviors--our computer simulations could only exhibit the dynamics we explicitly programmed into them. We solved this fundamental challenge by creating a new computational formalism where metabolic state and regulatory control form a bidirectional feedback loop: energy availability governs which biochemical pathways can operate, while pathway activity consumes energy. This simple coupling produces complex emergent behaviors never seen in previous computational models. Our simulations spontaneously exhibit homeostasis--maintaining constant drug levels despite 113-fold variation in membrane permeability--without any programmed control logic. The same model produces different emergent behaviors in different tissue contexts: tumor cells versus normal cells show 6-fold differences in drug accumulation from identical parameters, purely from different starting energy levels. We demonstrate this paradigm using drug transport as a case study, but the implications extend far beyond: cancer metabolism, brain injury during stroke, synthetic biology circuit design, and aging research all involve systems where metabolic-regulatory feedback governs cellular adaptation. This formalism provides computational biology with a long-missing capability--the ability to model emergent threshold behaviors computationally.

systems biology↗

Benchmarking PWM and SVM-based Models for Transcription Factor Binding Site Prediction: A Comparative Analysis on Synthetic and Biological Data

Transcription Factors (TFs) are essential regulatory proteins that control the cellular transcriptional states by binding to specific DNA sequences known as Transcription Factor Binding Sites (TFBSs) or motifs. Accurate TFBS identification is crucial for unraveling regulatory mechanisms driving cellular dynamics. Over the years, various computational approaches have been developed to model TFBSs, with Position Weight Matrices (PWMs) being one of the most widely adopted methods. PWMs provide a probabilistic framework by representing nucleotide frequencies at every position within the binding site. While effective and interpretable, PWMs face significant limitations, such as their inability to capture positional dependencies or model complex interactions. To address these, advanced methods, such as Support Vector Machine (SVM)-based models, have been introduced. Leveraging human ChIP-seq data from ENCODE, this study systematically benchmarks the predictive performance of PWM and SVM-based models across different scenarios. We evaluate the impact of key factors such as training dataset size, sequence length, and kernel functions (for SVMs) on models performance. Additionally, we explore the impact of synthetic versus real biological background data during model training. Our analysis highlights strengths and limitations of both PWM and SVM-based approaches under different conditions, providing practical guidance for selecting and tailoring models to specific biological datasets. To complement our analysis, we present a comprehensive database of pretrained SVM models for TFBS detection, trained on human ChIP-seq data from diverse cell lines and tissues. This resource aims to facilitate broader adoption of SVM-based methods in TFBS prediction and enhance their practical utility in regulatory genomics research.

bioinformatics↗

Prediction of metabolic dynamics through deep learning and high-throughput multiomics data

Synthetic biologys remarkable potential to tackle important societal problems is held back by our inability to predictably engineer biological systems. Here, we collected one of the largest public multiomics synthetic biology datasets generated to date, and used it to train a novel deep learning algorithm able to predict product and metabolic dynamics with great accuracy, starting to approach the predictive capabilities found in physics and chemistry. We were able to predict production time series with 90-99% accuracy, and final production with 96% accuracy. Further, we were able to produce good predictions for a majority of extracellular metabolites, and twenty different intracellular metabolites. These predictions were provided for a target of industrial relevance: a non-model yeast (Pichia kudriavzevii) engineered to produce large amounts of malonic acid, a desirable biomanufacturing target. This approach is generally applicable to any host, pathway, and product because all required knowledge is inferred from experimental data.

bioengineering↗

A two-step selection method for in vitro evolution of translational proteins

Improving the reconstituted translation system is a key requirement for bottom-up synthetic biology. Here, we developed a two-step in vitro evolutionary method that can be used for improving translational proteins. In this method, two distinct conditions were sequentially applied while maintaining genotype-phenotype linkage in water-in-oil droplets. Using this method, we performed in vitro evolution of four translation factors, IleRS, PheRS, EF-G, and EF-Tu, and identified mutations that modestly enhanced translation activity in in vitro expression assays. One of the EF-G mutations (P610S) increased activity per protein approximately 2-fold for the recombinant protein purified from E. coli. This selection method is useful for improving translational proteins for bottom-up synthetic biology.

synthetic biology↗

Desiccated cyanobacteria serve as efficient plasmid DNA carrier in space flight

Effective transport of biological systems as cargo during space travel is a critical requirement to use synthetic biology and biomanufacturing in outer space. Bioproduction using microbes will drive the extent to which many human needs can be met in environments with limited resources. Vast repositories of biological parts and strains are available to meet this need, but their on-site availability requires effective transport. Here, we explore an approach that allows DNA plasmids, a ubiquitous synthetic biology part, to be safely transported to the International Space Station and back to the Kennedy Space center, without low-temperature or cryogenic stowage. Our approach relied on the cyanobacterium Nostoc punctiforme PC73102, that is naturally tolerant to prolonged desiccation. Desiccated N. punctiforme was able to carry the non-native pSCR119 plasmid as intracellular cargo safely to space and back. Upon return to the laboratory, the extracted plasmid showed no DNA damage or additional mutations and could be used as intended to transform the model Synbio host Escherichia coli to bestow Kanamycin resistance. This proof-of-concept study provides the foundation for a ruggedized transport host for DNA to environments where there is a need to reduce equipment and infrastructure for biological parts stowage and storage.

microbiology↗

Tailoring CRISPR-Cas Immunity for the Degradation of Antibiotic Resistance Genes

The evolution and dissemination of antibiotic resistance genes (ARGs) are prompting severe health and environmental issues. While environmental processes are key barriers preventing the spread of ARGs, they are often sources of ARGs at the same time, as ARGs may be required and accumulate in the biological treatment units. An upgrading of environmental biotechnology is imperative and urgent. ARGs confer antibiotic resistance based on the DNA sequences rather than the chemistry of DNA molecules. An ARG can be considered degraded if its sequence was disrupted. Therefore, we present here that CRISPR-Cas immunity, an archaeal and bacterial immune system for eliminating invading foreign DNAs, can be repurposed and tailored for the degradation of ARGs. By deploying an artificial IncP machinery, the designed system, namely VADER, can be successfully delivered via bacterial conjugation. Then, we propose a new sector for ARG degradation to be implemented as a complement to the biological units in the framework of environmental processes. In this endeavor, a prototype conjugation reactor at a 10-mL-scale was devised, and 100% of the target ARG were eliminated in the transconjugated microbes receiving VADER in the reactor. By generating a nexus of synthetic biology and environmental biotechnology, we believe that our work is not only an enterprise for tackling ARG problems but also a potential solution for managing undesired genetic materials in general in the future. ImportanceAntibiotic resistance has been causing severe health problems and leading to millions of deaths in recent years. Environmental processes, especially the wastewater treatment sector, are important to barrier the spread of antibiotic resistance from the pharmaceutical industry, hospitals, or civil sewage. However, they have been identified as the source of antibiotic resistance at the same time, as antibiotic resistance with its main cause antibiotic resistance genes (ARGs) may be required and accumulate in the biological treatment units, leading to the dissemination of ARGs. Here, we transplanted the CRISPR-Cas system, an immune system via programmable DNA cleavage, to environmental biotechnology for tackling the antibiotic resistance dilemma thereof, and we propose a new sector in environmental processes specialized in ARG removal with a reactor inhabiting the CRISPR-Cas system per se. Our study provides a new angle to resolve public health issues via the implementation of synthetic biology at the process level.

synthetic biology↗

Native and engineered clifednamide biosynthesis in multiple Streptomyces spp.

Polycyclic tetramate macrolactam (PTM) natural products are produced by actinomycetes and other bacteria. PTMs are often bioactive, and the simplicity of their biosynthetic clusters make them attractive for bioengineering. Clifednamide-type PTMs from Streptomyces sp. JV178 contain a distinctive ketone group, suggesting the existence of a novel PTM oxidizing enzyme. Here, we report the new cytochrome P450 enzyme (CftA) is required for clifednamide production. Genome mining was used to identify several new clifednamide producers, some having improved clifednamide yields. Using a parallel synthetic biology approach, CftA isozymes were used to engineer the ikarugamycin pathway of Streptomyces sp. NRRL F-2890 to yield clifednamides. Further, we observed that strong CftA expression leads to the production of a new PTM, clifednamide C. We demonstrate the utility of both genome mining and synthetic biology to rapidly increase clifednamide production and identify a PTM tailoring enzyme for rational molecule design.

synthetic biology↗

Programming animal physiology and behaviors through engineered bacteria

A central goal of synthetic biology is to predictably and efficiently reprogram living systems to perform computations and carry out specific biological tasks. Although there have been many advances in the bio-computational design of living systems, these advances have mainly been applied to microorganisms or cell lines; programming animal behavior and altering animal physiology remain challenges for synthetic biology because of the system complexity. Here, we present a bacteria-animal symbiont system in which engineered bacteria recognize external signals and modulate animal gene expression, twitching behavior, and fat metabolism through RNA interference. By using genetic circuits in bacteria to control RNA expression, we programmed the physiology and behavior of the model animal Caenorhabditis elegans with logic gates. We anticipate that engineered bacteria can be used more extensively to program animal metabolism and behaviors for agricultural, therapeutic, and basic science applications. One Sentence SummaryAnimal physiology and behaviors are programmed via engineered bacteria.

synthetic biology↗

DNA Origami Lipid Membrane Interactions Defined at Single-Molecular Resolution

Rigid DNA nanostructures that bind to floppy bilayer membranes are of fundamental interest as they replicate biological cytoskeletons for synthetic biology, biosensing, and biological research. Here, we establish principles underpinning the controlled interaction of DNA structures and lipid bilayers. As membrane anchors mediate interaction, more than 20 versions of a core DNA nanostructure are built each carrying up to five individual cholesterol anchors of different steric accessibility within the 3D geometry. The structures binding to membrane vesicles of tunable curvature is determined with ensemble methods and by single-molecule localization microscopy. This screen yields quantitative and unexpected insight on which steric anchor points cause efficient binding. Strikingly, defined nanostructures with a single molecular anchor discriminate effectively between vesicles of different nanoscale curvatures which may be exploited to discern diagnostically relevant membrane vesicles based on size. Furthermore, we reveal anchor-mediated bilayer interaction to be co-controlled by non-lipidated DNA regions and localized membrane curvatures stemming from heterogenous lipid composition, which modifies existing biophysical models. Our study extends DNA nanotechnology to control interactions with bilayer membranes and thereby facilitate the design of nanodevices for vesicle-based diagnostics, biosensing, and protocells.

biophysics↗

Directed Evolution of Acoustic Reporter Genes Using High-Throughput Acoustic Screening

A major challenge in the fields of biological imaging and synthetic biology is noninvasively visualizing the functions of natural and engineered cells inside opaque samples such as living animals. One promising technology that addresses this limitation is ultrasound (US), with its penetration depth of several cm and spatial resolution on the order of 100 {micro}m.1 Within the past decade, reporter genes for US have been introduced2,3 and engineered4,5 to link cellular functions to US signals via heterologous expression in commensal bacteria and mammalian cells. These acoustic reporter genes (ARGs) represent a novel class of genetically encoded US contrast agent, and are based on air-filled protein nanostructures called gas vesicles (GVs).6 Just as the discovery of fluorescent proteins was followed by the improvement and diversification of their optical properties through directed evolution, here we describe the evolution of GVs as acoustic reporters. To accomplish this task, we establish high-throughput, semi-automated acoustic screening of ARGs in bacterial cultures and use it to screen mutant libraries for variants with increased nonlinear US scattering. Starting with scanning site saturation libraries for two homologs of the primary GV structural protein, GvpA/B, two rounds of evolution resulted in GV variants with 5- and 14-fold stronger acoustic signals than the parent proteins. We anticipate that this and similar approaches will help high-throughput protein engineering play as large a role in the development of acoustic biomolecules as it has for their fluorescent counterparts.

bioengineering↗

A programmable probiotic encapsulation system enhances therapeutic delivery in vivo

Recent advances in therapeutic modulation of human microbiota have driven new efforts to engineer living microbial medicines using synthetic biology. However, a long-standing challenge for live bacterial therapies is balancing the high dose required to achieve robust efficacy with the potential for sepsis. Here, we developed a genetically encoded microbial encapsulation system with tunable and dynamic expression of surface capsular polysaccharides to enhance therapeutic delivery. Following a synthetic small RNA knockdown screen of the capsular biosynthesis pathway, we constructed synthetic gene circuits that regulate bacterial encapsulation based on sensing the levels of environmental inducer, bacterial density, and blood pH. The induced encapsulation system enabled tunable immunogenicity and survivability of the probiotic Escherichia coli, resulting in increased maximum tolerated dose and enhanced efficacy in murine cancer models. Furthermore, triggering in situ encapsulation was found to increase microbial translocation between mouse tumors, leading to efficacy in distal tumors. The programmable encapsulation system demonstrates a new approach to control microbial therapeutic profiles in vivo using synthetic biology.

synthetic biology↗

One-pot synthesis of prenylated proteins utilizing E. coli cell-free expression

Bottom-up synthetic biology is a powerful tool for uncovering the mechanisms underlying vital biological processes, such as signaling and cell polarization. The core principle of reconstituting cellular functions in their minimal forms can be achieved through modular protein design. However, assembling multiple purified proteins into a functional and synchronized system remains a technical challenge. The fact that many regulatory proteins show direct or indirect membrane interactions further exacerbates the complications. Here, we introduce the Cell-Free prenylated Protein Synthesis (CFpPS) system which enables the production of prenylated proteins in a single reaction mix, through reconstituted prenylation machinery. Not only does the CFpPS system offer a fast and reliable method for producing solubilized prenylated proteins, but it can also produce the protein of interest directly in the vicinity of biomimetic membranes, thus enabling microscopy-based functional assessment. As proof of principle, we demonstrate synthesis and solubilization of various important signaling proteins from the Ras superfamily, as well as membrane binding and extraction of the key polarity regulator Cdc42. Furthermore, our method can be used to confer membrane affinity to any protein, simply by adding a 4-peptide motif to the C-terminus of the protein. In sum, the CFpPS system offers a versatile and effective platform for designing peripheral membrane proteins for synthetic biology applications.

synthetic biology↗

BRIDGE: A broad-host platform for interkingdom DNA delivery enabling the genetic domestication of phylogenetically diverse yeasts

Efficient DNA delivery remains one of the greatest barriers to engineering non-conventional yeasts, limiting their genetic domestication and exploitation as next-generation microbial cell factories. Here, we present BRIDGE (Bacteria-to-yeast Rapid Interkingdom DNA Gene Exchange), an integrated synthetic biology platform for broad-host interkingdom DNA delivery. BRIDGE is centred on a compact 6-kb Pan/ARS-oriT broad-host BRIDGE vector, enabled by a superconjugative helper plasmid (pSC5). Using BRIDGE, we expanded interkingdom DNA transfer from representatives of four genera accessible using pSC5 alone to ten phylogenetically diverse yeast genera, including Saccharomyces, Maudiozyma, Starmerella, Kazachstania, Yarrowia, Zygosaccharomyces, Lachancea, Kluyveromyces, Pichia and Komagataella. To demonstrate the versatility of BRIDGE, we engineered a 16-kb visual reporter carrying a synthetic five-gene violacein biosynthetic pathway from Chromobacterium violaceum. Individual transcriptional units were first constructed using the YeastFab modular cloning system and subsequently assembled into the complete pathway by single-step in vivo homologous recombination in Saccharomyces cerevisiae, followed by rapid plasmid recovery using the EASY-C platform. The reporter was successfully delivered to all ten yeast genera, demonstrating BRIDGE-mediated transfer of complete multigene synthetic pathways. Robust violacein production was observed across all six Saccharomyces species tested, providing a rapid visual marker for transformant identification, while modest functional pathway expression was also detected in Kazachstania and Kluyveromyces. Successful pathway transfer, albeit without visible pigmentation, was achieved in the remaining genera, indicating that regulatory compatibility, rather than DNA transfer, is the principal determinant of heterologous pathway expression in distantly related yeasts. Collectively, BRIDGE establishes an integrated Design-Build-Recover-Deliver workflow that combines modular in vitro construction of transcriptional units, single-step in vivo assembly of multigene pathways, rapid plasmid recovery using the EASY-C platform and broad-host interkingdom DNA delivery. This versatile framework enables the rapid genetic modification of previously intractable microorganisms, expanding the synthetic biology toolbox for sustainable biomanufacturing and industrial biotechnology.

synthetic biology↗