ACS Synthetic Biology

Autonomous bioluminescence systems enable continuous light emission in engineered organisms by genetically encoding both luciferase enzymes and their substrate biosynthetic pathways, offering a powerful platform for non-invasive and long-term monitoring of biological processes. However, bioluminescence output is highly sensitive to substrate availability and host metabolic state, often leading to signal instability under energy-limited conditions. Here, we report an alternative luciferin biosynthetic strategy for the bacterial Lux bioluminescence system in which the adenosine triphosphate (ATP)-dependent LuxEC complex is replaced by -dioxygenase (DOX), an enzyme that directly converts fatty acids into fatty aldehydes without consuming ATP. Using machine learning-guided directed evolution, we engineered DOX variants that markedly enhanced bioluminescence intensity when coupled with bacterial luciferase. The resulting ATP-free Lux bioluminescence system enabled single-cell-level bioluminescence imaging and maintained stable light emission under diverse antibiotic treatments, demonstrating enhanced robustness against metabolic perturbations. SIGNIFICANCEAutonomous bioluminescence imaging has become an attractive method for long-term observation of biological phenomena without the need for exogenous substrate addition. However, the light output of existing autonomous bioluminescence systems, including bacterial and fungal pathways, remains ATP-dependent and often declines when cellular metabolism is perturbed, limiting their reliability for quantitative analysis. Here, we report the development of an ATP-independent substrate biosynthesis pathway for the bacterial luciferase system using DOX. To improve system performance, we applied machine learning- guided directed evolution, which significantly enhanced signal intensity and enabled single-cell bioluminescence imaging. Furthermore, the DOX-based bacterial luciferase system maintained stable luminescence under antibiotic treatments, in contrast to the conventional ATP-dependent bacterial luciferase system. In summary, our findings establish a robust ATP-independent autonomous bioluminescence imaging platform that enables monitoring of cellular events under metabolic perturbations.

Cyanobacterium aponinum UTEX 3222 is a fast-growing marine cyanobacterium that reaches high cell densities, making it an attractive chassis strain for low-cost biomanufacturing. However, past efforts to genetically engineer UTEX 3222 have been unsuccessful. We hypothesized that the complex restriction modification systems in the UTEX 3222 genome constitute a major barrier to genetic transformation. Using a combined methylation avoidance and methylation mimicking approach, we overcame this barrier and successfully transformed UTEX 3222 by natural transformation, generating a large panel of fully segregated axenic strains. Transformation method was also established for a novel shuttle vector that replicates in UTEX 3222. A large-scale promoter screen identified promoters which exhibited strong expression at high cell density. These advances establish the foundation for developing UTEX 3222 as a scalable platform for photosynthetic biomanufacturing.

Engineered microbial consortia offer a promising strategy for chemical production by distributing specialized functions among microbial strains, reducing metabolic burden, facilitating modular pathway optimization, reducing toxicity, and increasing strain stability. However, differences in growth rates can destabilize population composition, compromising productivity and limiting their applicability. Here, we developed a multichromatic optogenetic Toxin-Antitoxin (optogeneticTA) platform for dynamic control of Escherichia coli consortia of up to four members using blue, red, and near-infrared light and darkness. By varying light intensities or pulses, we precisely program and dynamically modulate the population composition of two-, three-, and four-membered consortia. We further developed a modular mathematical framework that captures and predicts population dynamics of these optogenetically controlled co-cultures. Applying dynamic control to a two-membered engineered consortium increased phenol production by ~69% relative to unregulated consortia. These results establish a programmable platform for stabilizing and dynamically optimizing microbial consortia, with potential applications across microbial biomanufacturing.

Engineering G protein-coupled receptors (GPCRs) for biosensing applications remains challenging due to structural and functional constraints when such proteins are expressed in a heterologous host. OrthoRep offers the continuous accumulation and selection of signal-enhancing mutations in yeast, yet it has not been applied to GPCR evolution. We apply OrthoRep-driven mutagenesis to evolve opioid GPCRs and improve yeast-based biosensors: first, we express the human -opioid receptor (OPRM1) on the OrthoRep p1 plasmid and couple ligand activation to growth. Serial cell passaging of the biosensor generated functionally diverse mutants. However, mutations that decouple receptor activation from selection arose over longer passaging campaigns, halting evolution. We resolve this issue using a site-specific recombinase that re-introduces evolved variants onto the p1 landing pad of an unmutated biosensor, allowing continued selection. This led to an ~18-fold improvement in sensitivity of a mutant OPRM1 over wt-OPRM1. Additional polymerases were added to the platform, broadening the mutational profiles available. Assembling these components forms the platform we call HERO (Heterologous Engineering of Receptors using OrthoRep). Lastly, we deploy HERO using automation to improve the human {delta}-opioid receptor's weak response to an agonist by ~40-fold. Sensitivity also improved for a structurally dissimilar ligand assayed on the same evolved variants.

Synthetic biologists aim for standardization and simplicity in order to ensure reproducibility and reliability. Toolkits offer both, making it easier to share DNA sequences between labs while minimizing context dependent effects from sequences surrounding the construct of interest. The Yeast Toolkit (YTK) for the model organism Saccharomyces cerevisiae has found wide adoption within the scientific community. While the Toolkit and its existing extensions offer a large variety of different parts, there is a lack of characterized logic gates and insulator containing backbones for the assembly of genetic circuits with the yeast toolkit syntax. In this work, we present the Yeast Logic Toolkit (YLT). YLT contains 40 pre-assembled backbones with dropouts, 109 parts for NOT/NOR gate assembly, and a subdivided promoter Type 2 part that allows more flexible 5'UTR design. For insulation, the backbones contain spacer sequences and self-cleaving ribozymes which buffer against transcriptional read-through from upstream sequences. Due to our subdivision of Type 2 parts, we are able to manipulate the 5' untranslated region without a cloning scar upstream of the start codon. We also characterized the NOT and NOR gates from the Cello library for yeast in the sequence context of the yeast toolkit cloning scheme. This toolkit extension will make it easier and faster for scientists to assemble genetic circuits in yeast while providing a minimum of context dependent effects such as genetic read-trough due to the standardization of up- and downstream sequences surrounding the transcriptional units.

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.

Tandem Interspaced Guide RNA (TIGR)-TIGR-associated (Tas) systems are a newly discovered family of ultracompact, modular RNA-guided DNA-targeting proteins that function without a protospacer adjacent motif (PAM) requirement. Their utility as genome engineering tools in microbes remains unexplored. Here, we report the first functional implementation of TIGR-Tas in Saccharomyces cerevisiae for genome engineering. We show that TasR from Parcubacteria (ParTasR) can be programmed by user-defined tigRNAs to generate targeted DNA double-strand breaks at yeast endogenous loci. By co-delivering ParTasR with customized tigRNAs and donor templates, we achieved precise gene fragment deletion and targeted codon substitutions at multiple genomic loci. The multiplex genome engineering capability of this TIGR-Tas system was demonstrated through high-efficiency multiplex gene disruption and chromosomal assembly of a lycopene biosynthesis pathway while inactivating an endogenous gene. This work establishes TIGR-Tas as a valuable addition to the yeast genome engineering toolbox, particularly for applications requiring PAM-independent targeting or compact delivery.

Preserving the genotype-phenotype linkage is critical in high-throughput screens exploring large parameter spaces. Screening pooled libraries for recombinant protein secretion is therefore particularly difficult, since protein diffusion into the medium disrupts this linkage. Existing methods impose significant restrictions on the environment of the screened cells. Here, we present AutoGrowth, a growth-based screening strategy linking growth rate to secretion via a single-cell biosensor. We demonstrated the proportional modulation of growth rate by secretion and validated the approach on five model proteins. As proof of concept, we screened a signal peptide library in bioreactors for improved secretion of a single-chain variable fragment by coupling AutoGrowth with deep sequencing. Additionally, through spike-in experiments, we showed that AutoGrowth can identify favorable variants diluted at ratios as low as one in hundreds of millions in three days. Our growth-based strategy provides unprecedented throughput potential and ensures selection of genetic variants presenting advantageous secretion and fitness capabilities.

Fluorescent proteins are fundamental tools for cellular imaging. Most fluorescent proteins in routine use, including GFP, are derived from the jellyfish Aequorea victoria and emit blue-green light, which is strongly absorbed and scattered by tissue, limiting imaging depth. Far-red and near-infrared fluorescent proteins, engineered from bacteriophytochromes, address this limitation because far-red light penetrates tissue considerably further. However, these proteins are typically much dimmer than their A. victoria -derived counterparts. Improving brightness by conventional directed evolution requires screening large random mutant libraries, a process that is slow, labor-intensive, and often impractical outside specialized laboratories. We utilized an active-learning-guided directed evolution workflow that identified improved variants from substantially less data than conventional screening. Each round coupled automated, miniaturized cell-free protein expression directly from a DNA template without cloning or cell culture, with a machine-learning model retrained on cumulative sequence-function data to nominate the most informative variants for the next round. Applied to miRFP670nano3, this workflow screened 120 variants across successive rounds and identified twelve with improved brightness, the best four-fold brighter in bacterial systems. However, these gains did not translate when the variants were evaluated in mammalian cells, indicating that performance can be strongly dependent on cellular context. Retrospective simulation across benchmark datasets from ProteinGym showed that performing more experimental batches with fewer samples per batch consistently accelerated convergence to high-fitness sequences. Incorporating protein-language-model derived zero-shot fitness priors also accelerated convergence, but only in proportion to how well each prior score correlated with the true fitness landscape. Together, these findings established generalizable design rules, favoring smaller acquisition batches and confidence-weighted priors, for engineering proteins from minimal experimental data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=55 SRC="FIGDIR/small/744534v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@14992f7org.highwire.dtl.DTLVardef@14fad5borg.highwire.dtl.DTLVardef@1fe4ec2org.highwire.dtl.DTLVardef@e4b1f0_HPS_FORMAT_FIGEXP M_FIG C_FIG

The valorisation of biological polymers requires microbial systems that can both access recalcitrant substrates and convert the resulting carbon into useful products. Although microbial genome and metagenome resources provide an expanding reservoir of candidate depolymerizing and modifying enzymes, most discovery workflows remain disconnected from enzyme optimisation and host adaptation. Here we present a coupled sequence-based enzyme discovery, enzyme evolution and synthetic yeast chassis adaptation strategy for microbial biopolymer valorisation. Focusing on laccases for the depolymerisation of lignin as a proof of concept, we combine sequence data mining for enzyme discovery, modular yeast surface display for functional screening, directed evolution for enzyme optimisation and synthetic yeast genome diversification for chassis improvement. In our study, surface display enabled functional benchmarking and recovery of improved laccase variants and synthetic-genome-enabled diversification provided a route to explore host configurations that influence display and enzyme performance. By integrating enzyme-level and chassis-level optimisation, this framework addresses a central bottleneck in converting microbial biodiversity by computational sequence repository mining into deployable biomanufacturing systems. Our results establish laccases as tractable entry points for oxidative biopolymer conversion and provide a generalizable platform for engineering yeast systems for sustainable carbon valorisation.

Rising demand for rare earth elements, including lanthanides (Lns), has intensified environmental pressures and supply-chain vulnerabilities, motivating the development of bio-based methods for their extraction and separation. However, the lack of high-throughput assays for analysing the selectivity of lanthanide-binding proteins remains a key bottleneck in engineering bio-based Ln-extraction systems. Here, we report the development of high-throughput assays based on Ln-responsive protein biosensors. These {beta}-lactamase-based biosensors contain receptors with a single Ln-binding site derived from either lanmodulin or the AI-designed protein RF2. We established multiplexed colourimetric assays that quantify biosensor activity and selectivity in vitro and in the periplasm of E. coli. We further demonstrate that E. coli cells expressing these biosensors exhibit Ln-dependent survival in the presence of {beta}-lactam antibiotics. These platforms enable large-scale testing of Ln biosensors and Ln-binding proteins.

Genome transplantation is a key technology for synthetic genomics, enabling entire genomes to be transferred into recipient cells. Despite its importance, genome transplantation remains confined to a small number of wall-less Mollicute species and is often characterized by low efficiencies, limiting the development and testing of synthetic genomes. Because genome transplantation relies on the same polyethylene glycol mediated DNA delivery process used for plasmid transformation, improving DNA uptake is an important step toward more efficient transplantation systems. Here, we systematically optimized polyethylene glycol-mediated transformation in Mesoplasma florum, a fast-growing, non-pathogenic Mollicute with a reduced genome that represents an attractive chassis for synthetic genomics. We evaluated 20 parameters spanning cell physiology, DNA preparation, membrane conditioning, and recovery conditions. DNA topology emerged as the strongest determinant of transformation efficiency, with highly compacted DNA preparations producing up to three orders of magnitude more transformants than conventional plasmid preparations. Growth phase and polyethylene glycol concentration also strongly influenced transformation outcomes. To identify potential recipients for future genome transplantation experiments, we further screened ten strains belonging to the Mesoplasma lineage. Transformation efficiencies varied widely among strains, and Mesoplasma entomophilum W17 emerged as a particularly promising candidate. Moreover, W17 supported replication of plasmids carrying the M. florum oriC, suggesting compatibility between the replication systems of the two species. Together, these results establish an improved transformation workflow for M. florum, identify DNA topology as a major determinant of DNA uptake, and reveal a new candidate recipient for future genome transplantation studies. These advances provide practical tools and biological insights for synthetic genome engineering in Mollicutes.

Building high-performance microbial cell factories requires dynamic coordination of resource allocation among cellular growth, target-product biosynthesis, and endogenous host metabolism. However, existing polyploid engineering strategies rely primarily on static manipulation of chromosome copy number. Although increasing gene dosage can enhance biosynthetic capacity, static designs cannot readily accommodate the changing metabolic demands encountered during fermentation. Here, we developed a metabolite-responsive dynamic polyploid engineering strategy that couples chromosome ploidy to the cellular metabolic state. We first constructed a high-performance L-threonine biosensor and used it to sense intracellular L-threonine levels and regulate ftsZ expression, a key cell-division gene, thereby establishing a dynamic polyploid system that requires neither exogenous inducers nor antibiotics. This system enabled engineered cells to progressively transition from polyploid to haploid during fermentation, accompanied by stage-specific remodeling of cellular physiology and metabolism. Physiological characterization revealed a marked increase in cell size and alterations in cell-envelope properties during the polyploid phase, followed by a gradual decrease in chromosome copy number as fermentation progressed. Transcriptomic and metabolomic analyses further demonstrated that dynamic ploidy transitions induced global metabolic network rewiring, remodeling the tricarboxylic acid cycle and amino acid metabolism while redirecting carbon flux toward the biosynthesis of aspartate-family amino acids. Ultimately, dynamic polyploid engineering substantially enhanced L-threonine production, enabling the engineered strain to achieve an L-threonine titer of 183.1 g/L and a yield of 0.67 g/g glucose in 5-L fed-batch fermentation without antibiotics or exogenous inducers. These findings show that dynamic regulation of chromosome ploidy can couple gene-dosage control with remodeling of cellular physiology and metabolic networks, providing a new engineering strategy to overcome the limitations of static polyploid designs and build high-performance microbial cell factories.

Efficient conversion of polymeric feedstocks for sustainable bioprocessing requires robust strategies for enzyme assembly and cell-surface attachment. In nature, cellulosomes achieve highly efficient lignocellulosic polysaccharide deconstruction through scaffoldin-mediated organization of carbohydrate-active enzymes via specific cohesin-dockerin interactions. These modular binding pairs are therefore attractive tools for synthetic biology and engineered whole-cell biocatalysis, yet their performance has been studied mainly in vitro or in yeast or Gram-positive bacteria. The factors governing their function on the microbial surfaces - particularly those of Gram-negative bacteria - remain incompletely understood. Here, we investigated the binding efficiency and interaction stability of two thermophilic cohesin-dockerin pairs from Acetivibrio thermocellus and Acetivibrio clariflavus displayed on the surface of the genome-streamlined strain Pseudomonas putida EM371 using an Ag43-based display system from Escherichia coli and a dockerin-tagged fluorescent reporter. We show that binding efficiency is strongly affected by the temperature at which the cohesin-dockerin complex is formed. We further demonstrate that the interaction stability of the A. clariflavus pair can be substantially improved by targeted amino acid substitutions in the dockerin domain guided by molecular dynamics simulations and free-energy calculations. These results identify key parameters controlling the performance of thermophilic cohesin-dockerin modules on living bacterial cell surfaces and establish a computation-guided strategy for engineering more stable cellulosome-derived assembly interfaces, advancing the development of modular whole-cell platforms for sustainable biotechnology applications. TOC graphics O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/743725v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@18a97b6org.highwire.dtl.DTLVardef@1ee3ff4org.highwire.dtl.DTLVardef@a8dd60org.highwire.dtl.DTLVardef@5dd332_HPS_FORMAT_FIGEXP M_FIG C_FIG Cohesin-dockerin pairs provide strong and modular non-covalent interactions for synthetic biology and biotechnology applications. We establish an experimental and computational pipeline to improve their two key properties - binding efficiency and interaction stability - on the surface of Pseudomonas putida, enabling more robust cell-surface assembly systems.

Engineering microbial populations to perform complex functions requires programming not only cellular behaviour but also the functional organization through which biological activities are distributed across populations. While phenotypic heterogeneity is often regarded as variability to suppress, it can also serve as a foundation for organizing specialized functions within genetically homogeneous microbial systems. Here, we introduce PROMETEO, a modular genetic-circuit framework that programs population composition through architecture-encoded regulatory and translational asymmetries. Using a library of 27 asymmetric bistable circuits, we demonstrate that circuit architecture reproducibly specifies phenotypic distributions spanning a broad range of population compositions without continuous external induction. Programmed population structures remained stable over serial propagation and were qualitatively conserved across Escherichia coli and Pseudomonas putida. Stochastic and deterministic modelling accurately predicted architecture-dependent population compositions and hysteresis regimes, providing a quantitative framework for rational design. We further show that programmable population composition supports multiple modes of functional organization, including stable parallel specialization, inducible temporal redistribution of cellular states, and spatial ecological compartmentalization through biofilm-associated and planktonic subpopulations. As a demonstration of these capabilities, architecture-programmed organization enabled distributed Congo Red biotransformation through coordinated reductive and oxidative activities, achieving up to 98% dye removal in spatially compartmentalized populations. Together, these results establish population composition as a programmable property of genetic circuit design and provide a general strategy for engineering distributed functions within genetically homogeneous microbial populations.

Degron tags enable rapid, tuneable and reversible control of protein expression, and have become a critical tool for target validation and elucidating biological mechanisms. Tag engineering uses genome editing to generate biallelic gene fusions, though this process remains laborious and requires new donor vectors to be generated for each locus. Here, we have generated a series of universal donor vectors to enable biallelic integration of different degron tags (AID, dTAG, BromoTag, SMASh) using different selection strategies (FACS, drug selection). Using this system, tags can be fused in-frame to endogenous protein C-termini via homology-independent repair following Cas9-induced double strand breaks, as shown here using human lymphoma cells, mouse embryonic stem cells and colonic tumour organoids. This workflow simplifies the genetic engineering of degron tags and is applicable in contexts where homology-dependent repair is challenging.

Forster resonance energy transfer (FRET) is commonly used to monitor protein-protein interactions in situ. The high spatiotemporal resolution and facile implementation inside complex molecular environments have spearheaded FRET's widespread adoption in biosensing. Despite these advantages, current FRET biosensors are largely restricted to the detection of the presence/absence of individual inputs and are thus unable to sense several multiplexable inputs simultaneously within complex milieu of biological environments. In this work, we introduce a generalizable strategy to construct genetically encoded protein-based FRET biosensors capable of recognizing multiple inputs following Boolean logic-type (YES/OR/AND) operations. These topologically specified FRET sensors powerfully expand the input capacity in sensing protein-protein interactions while providing a user-programmable platform for monitoring heterogeneous biological activities both in vitro and in living cells.

Synthetic endosymbiosis, the deliberate introduction of bacteria into eukaryotic host cells, provides a tractable model for how endosymbiotic relationships become established, and a route to cells with new metabolic capabilities that never evolved naturally in that lineage. Current methods for introducing bacteria into mammalian cells rely on microinjection, engineered invasion, listeriolysin-mediated phagosomal escape, or naturally permissive hosts. These methods are invasive, low-throughput, or costly, and most require the prospective endosymbiont to be genetically tractable, which excludes endosymbiotic bacteria which are of great interest as potential organelle precursors. Here we describe a protocol based on polyethylene glycol (PEG), long used to fuse mammalian cells, that delivers Escherichia coli into HeLa cells without genetic modification of either partner or the use of specialised equipment. We optimised the governing parameters in two stages: first, bacterial density, gentamicin concentration, and PEG molecular weight and concentration were optimised; then, medium composition and the timing of fusion and recovery. Across four parameters screened, 10% PEG-3350 applied for 4 min to HeLa cells 12 h after seeding, followed by 4 h in gentamicin-containing medium, gave the most consistent delivery. Under these conditions, confocal microscopy and z-stack reconstruction detected mCherry-tagged E. coli inside HeLa cells. Applying the same protocol to Candidatus Kinetoplastibacterium crithidii, a naturally occurring {beta}-proteobacterial endosymbiont of the trypanosomatid Angomonas deanei, we observed the endosymbiont inside the HeLa cells. This protocol offers an accessible entry point for constructing artificial endosymbioses.

Alternative {sigma} factors enable bacteria to reprogram transcription in response to environmental cues and provide powerful tools for synthetic gene regulation. Extracytoplasmic function (ECF) {sigma} factors have recently been implemented as orthogonal transcriptional switches in several bacteria, including the -proteobacterium Sinorhizobium meliloti. Here, we investigated how promoter sequence context influences the activity and specificity of heterologous ECF-dependent core promoters. Combining ECF core promoters from Pseudomonas syringae and Escherichia coli with flanking sequences derived from strong promoters of S. meliloti and other proteobacteria increased promoter activity by up to 45-fold while preserving {sigma} factor specificity. We identified a short upstream AT motif, resembling a minimal UP element, as a key determinant of promoter strength. Targeted mutagenesis confirmed its role in supporting transcription initiation. Engineering promoter-flanking sequences together with the AT motif generated promoter libraries spanning up to a 75-fold range of activities. Importantly, promoter activity hierarchies were maintained within synthetic multi-gene regulons controlled by a single ECF master regulator, demonstrating modular and predictable gene expression. Our results establish promoter environment engineering as a robust strategy for expanding the dynamic range of orthogonal ECF regulatory systems and facilitate the scalable design of synthetic transcriptional programs in bacteria.

We present a robust and versatile in vitro transcription (IVT) assay based on an optimized Broccoli RNA aptamer sequence. When paired with the fluorophore DFHBI-1T, this system enables real-time monitoring of multi-round transcription over several hours. To facilitate streamlined promoter analysis, we developed the pIVT3 plasmid backbone. The system was validated using both the single-subunit T7 RNA polymerase and the multi-subunit Escherichia coli RNA polymerase; notably, the activity of the E. coli enzyme remained strictly dependent on the presence of a {sigma} factor and a cognate promoter. To optimize the signal-to-noise ratio, we incorporated two rrnBT1 terminators upstream of the promoter of interest. This modification effectively eliminated background transcription for weak promoters (PlivJ) and prevented interference from read-through transcription in strong synthetic promoters (Ptrc*). Furthermore, we demonstrated the assays utility for drug discovery by characterizing the time- and dose-dependent inhibitory kinetics of rifampicin. Collectively, these results establish the Broccoli-based IVT system as a highly adaptable platform for quantifying promoter strength and screening small-molecule inhibitors of bacterial transcription. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/744185v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1e0c991org.highwire.dtl.DTLVardef@d154aeorg.highwire.dtl.DTLVardef@10e95fcorg.highwire.dtl.DTLVardef@98ea80_HPS_FORMAT_FIGEXP M_FIG C_FIG