Synthetic Biology

Tuning protein expression in non-model organisms is often constrained by the lack of validated genetic parts and predictive design tools. Translational tuning through the modulation of upstream untranslated regions (5'-UTRs) offers a potentially organism-agnostic route, but existing methods typically rely on mechanistic assumptions, prior knowledge that may not be available in non-model contexts, or the screening of sequence libraries. Here, we present a simple generative approach for creating synthetic 5'-UTR libraries based solely on the genomic sequence statistics of any desired organism. The method uses a sliding-window n-gram language model applied to native 5'-UTR sequences to produce novel sequences that preserve organism-specific base distributions and motifs without hard-coding specific motifs or mechanistic rules into inflexible statistical templates. We have applied this approach to the model bacterium Escherichia coli and the non-model probiotic Limosilactobacillus reuteri. Libraries of approximately 1,000 sequences were generated for each organism, from which about 100 unique sequences were experimentally tested for translation of a fluorescent reporter protein. In both organisms, the synthetic libraries yielded a broad range of translation levels from this relatively small number of tested variants. Sequences derived from an organisms own genomic statistics generally performed better in that organism than sequences derived from the other species. Correlations of individual sequence performance across the two species were weak, and thermodynamic predictions of ribosome binding strength showed very little predictive power, especially in the non-model L. reuteri. The results demonstrate that simple statistical language model approaches applied to genomic data can generate functional translational regulatory sequence libraries without detailed mechanistic knowledge or explicit reference to consensus motifs. The approach requires minimal computational resources, avoids reproducing native sequences, and can be readily applied to any organism with a sequenced genome. This strategy may lower technical barriers to expression tuning in non-model organisms.

{beta}-galactosidases (BGs) are essential enzymes widely used in the food industry, particularly in the production of lactose-free products. Among them, the BG from Aspergillus oryzae is of industrial relevance due to its activity at acidic pH and moderate thermal tolerance. However, enhancing its catalytic performance remains a key challenge. Traditional enzyme engineering methods are time-consuming and resource-intensive, limiting their scalability. Recent advances in Artificial Intelligence (AI), particularly those based on Natural Language Processing, offer a promising alternative by enabling efficient exploration of protein sequence space and prediction of beneficial mutations. In this study, we introduce an ensemble-based, zero-shot Protein Language Model pipeline that reconciles predictions from six independent models (ESM2 and the five ESM1v variants) combined with a diversity-aware candidate selection strategy. Applied to the BG from A. oryzae, this approach identified beneficial mutations leading to novel enzyme variants with up to a four-fold increase in catalytic efficiency on oNPGal, a two-fold increase on lactose, and, independently, a T338I variant with markedly enhanced thermostability ({approx}80% residual activity after 24 h at 60 {degrees}C), all without requiring supervised fine-tuning on experimental fitness data. Our results demonstrate that consensus across an ensemble of PLMs can efficiently enrich beneficial substitutions in industrially relevant enzymes and substantially reduce the number of wet-lab candidates that need to be screened. Table of Contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/726739v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@18084f7org.highwire.dtl.DTLVardef@99a102org.highwire.dtl.DTLVardef@19a64forg.highwire.dtl.DTLVardef@1f59cff_HPS_FORMAT_FIGEXP M_FIG C_FIG

Escherichia coli is a well-established model organism in molecular biology and biotechnology. Despite its long history as a laboratory workhorse, the efficient single-step chromosomal integration of large DNA fragments remains a challenge. Currently known methods are either simple but have limitations on insert size, or flexible but laborious requiring plasmid construction or multi-step procedures. Here, we present PhAGE (Phage-Assisted Genome Engineering), which enables the integration of [~]20 kb DNA fragments into E. coli genome within a single day. PhAGE method uses in vitro packaging of recombinant DNA into bacteriophage capsids, followed by general transduction to introduce pre-assembled DNA with flanking homology arms into recipient cells. This approach allows efficient and landing pad-free integration of large constructs into the target loci. We demonstrate its usefulness through rapid integration of multi-gene operons. PhAGE resolves the long-standing trade-off between simplicity and insert size in E. coli genome engineering, accelerating strain construction across a wide range of applications, from biosynthetic pathway engineering to genome-scale design.

Efficient genome writing in mammalian cells requires robust methods for integrating large DNA payloads. The previously described method mammalian Switching Antibiotic resistance markers Progressively for Integration (mSwAP-In) enables iterative, biallelic genome rewriting in mammalian stem cells with DNA payloads exceeding 100 kb. However, the lack of standardized vectors and certain technical constraints have limited its broader adoption. Here we present an improved plasmid toolkit designed to streamline the implementation of mSwAP-In. The toolkit includes two core vectors. pLP-TK (pCTC174) is a landing-pad plasmid compatible with Golden Gate assembly of genomic homology arms and supports both mSwAP-In and the recombinase-mediated cassette exchange method Big-IN. mSwAP-In MC2v2 (pKBA135) is a versatile Big DNA assembly and delivery vector that supports Gibson-based assembly and incorporates positive, negative, and fluorescent selection markers, as well as a backbone counterselection cassette to minimize unwanted plasmid integration. The vector architecture also enables propagation in yeast and bacterial hosts, inducible plasmid copy-number amplification in standard E. coli strains, and CRISPR/Cas9-mediated payload release through preinstalled guide RNA target sites. We further characterize the FCU1/5-FC counterselection system in mouse embryonic stem cells and define conditions that minimize its bystander toxicity. Finally, we provide a set of Cas9-gRNA expression plasmids optimized for common mSwAP-In applications. Together, these reagents constitute a standardized and experimentally validated toolkit that simplifies large-scale genome writing using mSwAP-In.

Protein synthesis using recombinant elements (PURE) system has been widely applied in various biological research fields and synthetic cell construction. Optimization efforts to enhance the PURE system performance by adjusting its individual components have remained limited to the expression of single genes with a small number of molecular compositions tested, making it difficult to link component composition to system-level performance across different DNA contexts. Here, we combine automated acoustic liquid handling with an active learning framework to explore broadly the compositional landscape of PURE system. By grouping the 69 individual components (including proteins and tRNAs) into 21 functional sets and iteratively guiding experiments with active learning, we rapidly identify improved compositions and demonstrated up to 3-fold enhancement in protein yield and translation rate for a single reporter gene. We further show that optimization drivers differ between low and high DNA concentrations, revealing that optimal PURE compositions are DNA concentration-dependent. We then apply this optimization strategy to enhance the expression of a 41-kb synthetic chromosome containing 15 genes by maximizing the fluorescence intensities of two reporter proteins. While a 3-fold improvement could be reached on the two gene products guiding learning, a full proteomic analysis revealed that optimization is gene-specific, i.e., changes in PURE system compositions differently impact the amounts of synthesized proteins encoded on the same DNA template. Together, this work establishes active learning as an efficient strategy to navigate the high-dimensional PURE compositional space and provides mechanistic insight into DNA context-dependence of gene expression optimization.

Antimicrobial resistance (AMR) is a growing global threat to human health, and rapid methods for characterising emerging antimicrobial resistance genes (ARGs) are needed. Here, we develop a semi-automated workflow using cell-free gene expression (CFE) systems to measure the activity of two ARGs encoded on plasmid DNA that produce rifampicin-inactivating and gentamicin-inactivating enzymes. We validated the use of a small benchtop Myra liquid handling system compared to manual pipetting, with no statistical differences observed. After optimising the pre-incubation time of ARGs and dispensing protocol, expression of aac(3)-IIa increased the half-maximal inhibition concentration (IC50) of gentamicin by over 150-fold, while arr-3 increased the IC50 of rifampicin by approximately 20-fold compared to controls. Future work could extend this platform to characterise novel ARGs identified through genomic surveillance or rapidly profile activity of new or derivative antibiotics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/720151v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1a61fe3org.highwire.dtl.DTLVardef@1778eadorg.highwire.dtl.DTLVardef@380be4org.highwire.dtl.DTLVardef@194bb63_HPS_FORMAT_FIGEXP M_FIG C_FIG

Recombinant human lactopontin (rhLPN), an equivalent of human milk lactopontin, is of increasing interest for human nutrition applications due to its roles in mineral binding, gastrointestinal function and immune modulation. These properties depend strongly on post-translational modifications, particularly phosphorylation and glycosylation. Here, we report the production of rhLPN in Kluyveromyces lactis at laboratory and pilot scale and present a comprehensive molecular comparison with native human lactopontin (nhLPN) isolated from human milk. Mass spectrometry-based peptide mapping confirmed the primary structure and identified extensive phosphorylation, consistent with the native protein. Middle-up analyses demonstrated closely matched phosphoform distributions between rhLPN and nhLPN, while glycosylation profiling revealed a defined population of low-complexity O-glycoforms localized to the N-terminus. Functional assessment demonstrated substantially greater iron binding by phosphorylated rhLPN compared with dephosphorylated and non-phosphorylated forms. Similar phosphorylation-dependent behaviour was observed for bovine lactopontin, supporting a conserved role for phosphorylation in mineral interaction. Across five 750 L pilot scale batches, both phosphorylation and glycoform distributions were highly consistent, indicating robust process reproducibility. Together, these results demonstrate that rhLPN produced in K. lactis recapitulates key structural and functional attributes of nhLPN, supporting its suitability as a scalable ingredient for nutrition applications.

Course-based Undergraduate Research Experiences (CUREs) have emerged as a transformative approach to science education, expanding access to authentic research opportunities beyond the traditional undergraduate research assistant (URA) training. By embedding research into a curriculum, CUREs engage a broad and diverse population of students in a classroom environment that emphasizes experimental design, data analysis, and scientific communication. However, this has been difficult to develop for fields such as plant synthetic biology due to the long timescales of plant transformation. One avenue around this problem is to utilize a recent innovation that enables high throughput and rapid screening of gRNA efficacy by leveraging viral-based delivery of guide RNAs (gRNAs). In this work, we develop and validate a CURE with undergraduate students at Colorado State University (CSU). Students worked in teams to design and test efficacy of gRNAs targeting a Cas9-based transcriptional repressor to different regions of the promoters of the three GIBBERELLIN INSENSITIVE 1 genes (GID1a, GID1b, and GID1c) in Arabidopsis thaliana. Over the semester, students generated and analyzed gene expression data to understand the efficiency of twelve new gRNAs. We further validated CURE student-identified gRNAs with an undergraduate research assistant (URA) that assessed target gene expression and phenotypic outcomes in stable transgenic lines expressing SynTF constructs with the strongest gRNAs from the class. We further describe the curriculum structure to facilitate adoption at other institutions and present student-generated datasets demonstrating the utility of ViN-based screening for identifying effective SynTF gRNAs for plant functional genomics and engineering. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/715601v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@13869f5org.highwire.dtl.DTLVardef@b469feorg.highwire.dtl.DTLVardef@9aa51borg.highwire.dtl.DTLVardef@cdc129_HPS_FORMAT_FIGEXP M_FIG C_FIG

Rapid advancements in AI have enabled significant progress in protein and nucleic acid design, but they also pose biosecurity challenges. We examine the vulnerabilities of biosecurity screening software (BSS) to AI-reformulated synthetic homologs of proteins of concern (POCs) that have been fragmented into smaller segments. We evaluate four BSS tools that were recently patched to enhance their AI resiliency. Without any further modification, we found that two of the four tools were capable of robustly detecting fragments as short as 50 nucleotides, demonstrating screening capabilities that exceed those requested in the U.S. Framework for Nucleic Acid Synthesis. Upgraded versions of the other two tools improved performance. Although our findings confirm the effectiveness of the tested BSS tools, at the same time, they emphasize the urgency of developing alternate BSS approaches to counter evolving AI-enabled biosecurity risks.

Combinatorial mutagenesis is essential for exploring protein sequence-function landscapes in engineering applications. However, while large-scale machine learning benchmarks exist for protein function prediction, they are primarily limited to single-mutant libraries, leaving a critical gap for combinatorial mutagenesis. Here we introduce CombinGym, a benchmarking platform featuring 14 curated combinatorial mutagenesis datasets spanning 9 proteins with diverse functional properties including binding affinity, fluorescence, and enzymatic activities. We evaluated nine machine learning algorithms from five methodological categories (alignment-based, protein language, structure-based, sequence-label, and substitution-based) across multiple prediction tasks, assessing both zero-shot and supervised learning performance using Spearmans {rho} and Normalized Discounted Cumulative Gain metrics. Our analysis reveals the substantial impact of measurement noise and data processing strategies on model performance. By implementing hierarchical dataset splits (0-vs-rest, 1-vs-rest, 2-vs-rest, and 3-vs-rest scenarios), we demonstrate the value of lower-order mutation data for empowering machine learning models to predict higher-order mutant properties. We validated this capacity through both in silico simulation (improving fluorescence brightness of an oxygen-independent fluorescent protein) and experimental validation (engineering enzyme substrate specificity), achieving a substantial increase in specific activity. All datasets, benchmarks, and metrics are available through an interactive website (https://www.combingym.org), facilitating collaborative dataset expansion and model development through integration with automated biofoundry platforms.

Bacillus subtilis is an important chassis for biotechnology, but its use in multiplex genome engineering is limited by low natural transformation efficiency. Here, we compared inducible promoter systems for synthetic activation of the competence regulator ComK and evaluated their effects on the comG operon competence reporter and transformation efficiency. Xylose- and mannitol-inducible systems outperformed IPTG-based constructs and shifted 96-99% of cells into a reporter-positive competent state. However, reporter activation alone did not predict transformation potential. Optimization of culture density and induction timing increased transformant yield 45-fold relative to the initial protocol and 2800-fold relative to the conventional Spizizen method. Disruption of native competence regulatory genes did not improve performance and often reduced transformation output, highlighting the importance of endogenous regulatory circuitry. Using the optimized strain and protocol, we achieved co-transformation frequencies of 11-18% and constructed multiplex spore-display libraries containing fluorescent protein fusions integrated at multiple loci. Screening identified strong dual-display combinations and showed that cargo loading depends on anchor protein, integration locus, and genetic background. SscA fusions supported the highest display capacity and promoted synergistic co-display. Together, these results show improvements in natural transformation-based genome engineering in B. subtilis and provide insight into the construction of multifunctional engineered spores.

Translation initiation has become an attractive target for engineering orthogonal translation systems, yet the extent to which these systems retain functionality across distinct host backgrounds remains poorly defined. In bacteria, start codon recognition depends on pairing between the initiator tRNA anticodon and a suitable start codon within the appropriate distance from the Shine-Dalgarno sequence. These sequence-specific interactions enable translation initiation to be reprogrammed through anticodon engineering. What is currently missing is an understanding of how anticodon mutants of initiator tRNAs function across different bacterial strains. Here, we systematically evaluated the portability of a library of twelve i-tRNA anticodon mutants paired with their complementary non-canonical start codons. Most i-tRNA-start codon pairs supported detectable translation initiation across multiple strains, demonstrating broad functional portability. However, initiation efficiency, absolute system output, and fitness effects varied substantially between strains. Comparative genomic analyses revealed host-specific gene differences broadly, and endogenous tRNA gene sequence and copy number specifically, was associated with this variability. While most i-tRNA variants were well tolerated, a subset produced strain-dependent growth defects that primarily affected growth rate rather than final culture density. Together, these findings show that translation initiation efficacy of engineered i-tRNAs is partially strain-dependent and that host background must be considered a key design variable when deploying these translation systems. Looking forward, this study provides a framework for host-aware selection of microbial chassis for orthogonal translation applications in synthetic biology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/719103v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@118b02borg.highwire.dtl.DTLVardef@1d5dab0org.highwire.dtl.DTLVardef@1088d0borg.highwire.dtl.DTLVardef@63eb74_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

Budding yeast Saccharomyces cerevisiae is a workhorse chassis for producing added food and agricultural compounds. However, building multi-enzymatic pathways for these chemicals often requires iterative genomic integration, underscoring the need for efficient, rapid genome-editing tools that can reliably target transcriptionally active chromosomal regions. In this study, to accelerate strain construction, we established a genome-editing toolkit to rapidly engineer eight loci, highly expressed hot-spots, but nonessential genomic sites suitable for stable pathway assembly. Our approach integrates three key design features: (i) selectable markers to enable rapid screening of edited cells, (ii) extended homology arms that leverage the yeast homology-directed repair machinery for robust genomic integration, and (iii) co-delivery of Cas9 and guide RNAs to promote efficient double-stranded DNA breaks at specific integration sites. The sequence independence of FASTOP relies on the release of integration cassettes from integrative vectors, mediated by restriction digestion at two flanking multiple-cutting sites in the integration module to minimize the risk of introducing sequence errors during PCR amplification of the integration cassettes. Following the introduction of a fluorescent reporter cassette, we observed high integration efficiencies across the target sites. We then integrated the biosynthetic pathway of plant-derived flavonoid naringenin into the hot-spots of the yeast genome using the FASTOP toolkit. Our results demonstrated that upon expressing the five essential genes in simple shake flask culture, naringenin production reached 505.7 mg/L, representing a significant (69-fold) increase over previously reported titers for comparable minimal heterologous pathways in S. cerevisiae. Together, the FATSOP toolkit provides a user-friendly platform for reliably modifying hot-spot loci to rapidly construct multi-enzymatic metabolic pathways in S. cerevisiae, while achieving high production levels for high-value food-relevant metabolites.

Fluorescent reporters cover a wide range of applications in both basic and applied research. Whether a study involves microscopic imaging to study (co)-localization of proteins, FRET, biosensing, or quantifying gene expression, fluorophores are attractive reporter candidates due to their relatively straightforward in vivo readout. For microbiological applications, a wide variety of fluorescent proteins with varying excitation and emission wavelengths, brightness levels, and maturation times are available. Careful consideration is required when selecting from this large suite of proteins, especially when choosing multiple fluorophores. This is further complicated in phototrophic organisms, which exhibit strong autofluorescence, especially towards the red part of the spectrum, effectively eliminating common candidates such as mCherry. In this study, the specific properties and performance of a selection of fluorescent proteins are systematically evaluated against the background of photosynthetic pigment-derived autofluorescence in the cyanobacterium Synechocystis sp. PCC 6803. Specific readouts of different combinations of fluorescent proteins are also analyzed using high-throughput methods, namely plate reader fluorescent scans and single-cell flow cytometry to quantify fluorescence. The ultimate goal is to assess each fluorescent protein with regard to: 1.) Its ability to be discerned from cyanobacterial autofluorescence. 2.) Its compatibility with other fluorophores in this context. 3.) Its overall suitability in cyanobacterial research. Several highly suitable fluorescent proteins for use in cyanobacteria are identified, including mTagBFP2, mNeonGreen and mScarlet-I and suitable combinations, covering nearly the whole spectrum of visible light. This study expands the knowledge and toolset for current and future researchers and uncovers a whole spectrum of possibilities for fluorescent protein selection in cyanobacterial cell biology.

De novo protein design has advanced rapidly in recent years, yet the programmable recognition of specific DNA sequences remains a longstanding challenge. Here we describe a deep learning based approach for designing sequence selective DNA binding proteins. Our method combines structure generation using RFdiffusion3 with explicit screening against off-target interactions using AlphaFold3. We test this approach by generating 96 designs for each of 15 diverse DNA targets and identify specific binders for 7 targets, representing a ~100-fold improvement in success rates over previous approaches. We further characterize the binding landscape using variant competition assays and randomized library screening, revealing robust sequence discrimination across diverse targets. Together, these results represent a significant step forward in de novo sequence specific DNA binder design.

Precise regulation of gene expression in batch bacterial cultures is challenging because the underlying dynamics vary with cellular physiological state over time. Although cell-silicon systems enable rapid, real-time optogenetic control, disturbance rejection remains difficult in batch culture because the plant dynamics shift across growth phases, limiting the effectiveness of fixed-gain controllers designed under constant-growth assumptions. Here, we present a multiscale model-guided feedback control framework for disturbance rejection in batch E. coli cultures. Frequency-response analysis shows that the input-output dynamics of gene expression depend strongly on growth phase, revealing operating-point-dependent limits on the disturbance rejection performance of a fixed-gain PID controller. To address this limitation, we develop two growth-aware control strategies: a gain-scheduled PID (PID-GS) controller that adapts to cellular physiological state, and a gain-scheduled feedback-feedforward controller (PID-GS-FF) that further compensates for growth perturbations. We also introduce a controller evaluation framework that identifies three distinct operating regimes for targeted experimental validation. Together, these results show that accounting for growth-state-dependent dynamics is necessary for robust disturbance rejection in batch culture and provide a control-oriented framework for regulating living systems with shifting operating conditions.

BackgroundEngineered synthetic RNAs enable cellular control by sensing and responding to intracellular biomolecules. Recently developed sense-edit-switch RNAs (sesRNAs) based on Adenosine Deaminase Acting on RNA (ADAR)--which edits a stop codon to switch on custom payload translation in the presence of a target RNA--are consistently functional across different species. The ability of sesRNAs to couple bespoke payload translation to the presence of cell type-specific transcripts will usher in an era of precise cell-targeted biotechnological interventions. ResultsTo expedite the generation of sesRNAs, we develop ADAR-Sense--a universal web tool for automated sensor design based on user-defined sensor length, sensor-target RNA mismatch number, mismatch proximity to the ADAR-editable stop codon, and targeted custom element inclusion for improved ADAR recruitment and subsequent payload induction. ConclusionsCompared to current tools, the simplicity and flexibility of ADAR-Sense will streamline the design and screening of sesRNAs in new cell types and conditions, supporting the swift adoption of this sensing platform in both basic and translational research.

The genus Mycobacterium is increasingly recognized as a major clinical concern due to diseases such as tuberculosis, along with the emergence of antimicrobial-resistant strains, underscoring the urgent need for advanced genetic tools to study mycobacterial biology and pathogenesis. Progress in this area relies heavily on the functional characterization of previously unannotated genes, which necessitates tightly regulated expression systems. Here, we report the development of an improved tetracycline-regulated vector platform, comprising the episomal pM(R)T2 and integrative pMI(R)T2 series, which builds upon the previously described pMT vector system. The T2 vector series incorporates a fine-tuned TetRO system for enhanced transcriptional control. The pMT2 vectors function as tetracycline-inducible systems, whereas the pMRT2 variants utilize a reverse tetracycline repressor (RevTetR) to enable tetracycline-repressible gene regulation. Additionally, the integrative variant, pMI(R)T2 switches the oriM element with the integrase and attP sites derived from mycobacteriophage L5, facilitating stable genomic integration and controlled expression of concentration-sensitive genes, including toxins. To expand the selection flexibility, the pAN(R)Tet series replaces the kanamycin resistance cassette with a hygromycin resistance cassette. Functional validation of gene regulation in M. smegmatis and M. bovis BCG shows that both TetR and RevTetR systems provide reliable inducible and repressible controls, respectively, upon anhydrotetracycline addition. Taken together, these vectors constitute a versatile, tightly regulated genetic toolkit with significant potential to accelerate research and therapeutic development in mycobacterial systems.

Zymomonas mobilis is an ethanologenic Alphaproteobacterium with many interesting characteristics for fundamental research and applied microbial engineering. Although genetic engineering has been established for Z. mobilis since the 1980s, a rich set of inducible transcriptional regulators is still unavailable. In this work, seven different chemically inducible promoters have been systematically tested for their functionality in Z. mobilis. In particular, for the first time, NahR-PsalTTC, VanRAM-PvanCC, CinRAM-Pcin and LuxR-PluxB have been characterized in Z. mobilis, alongside the commonly used regulator-promoter pairs TetR-Ptet and LacI-PlacT7A1_O3O4, and the less commonly used XylS-Pm. All promoters investigated in this work are compatible with the Golden Gate modular cloning framework Zymo-Parts. Characterization was carried out with a shuttle vector backbone based on pZMO7, which has so far been rarely used for applications in Z. mobilis but seems to be completely stable without selection and generates high and uniform levels of expression. From the experimental results presented, it can be concluded that VanRAM-PvanCC and CinRAM-Pcin are particularly promising for broad use in the Z. mobilis community. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/712268v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@16579e6org.highwire.dtl.DTLVardef@1262533org.highwire.dtl.DTLVardef@15456a2org.highwire.dtl.DTLVardef@3af98_HPS_FORMAT_FIGEXP M_FIG C_FIG