Abstract In biotechnological applications, it is often necessary to introduce genes or entire pathways into a host cell, which can create a significant metabolic burden on the host, limiting productivity. In this study, we systematically investigated the physiological stress responses of Pseudomonas putida during heterologous protein production using a modular monitoring system consisting of a plasmid encoding a heterologous protein fused to eGFP and a chromosomally integrated capacity reporter. Our findings reveal that translation is the main bottleneck, with translational capacity becoming saturated under high expression loads. While increasing the strength of the RBS improved protein production for non-burdensome proteins, this effect was not observed for larger fusion proteins. Variations in fusion protein size suggested that translational demand, rather than the overall mass of protein produced, determines metabolic burden. We further evaluated how resource availability affects protein expression by modifying the metabolic regime or supplementing with amino acids. While the carbon source affected cellular capacity, it did not significantly alter heterologous protein production. Amino acid supplementation alleviated the growth defects of MBPeGFP-producing cells and modestly improved protein production rates. Together, these findings emphasize that metabolic burden is influenced not only by the size of the produced protein but also by transcript architecture, resource allocation, and the physiological state of the host. Therefore, successful optimization of heterologous protein production requires a holistic approach integrating construct design with host physiology and cultivation strategies.

Abstract Scaffolds are powerful tools in synthetic biology used for various applications, from increasing yield to optimizing signalling specificity. Protein scaffolds can be built by fusing peptide binding domains (PBD) and attaching the peptide they bind to the enzymes, inducing spatial proximity. Only a few PBD-peptide combinations have been tested in this context, and no combination produced a high yield in yeast, an important chassis in biotechnology. Therefore, there is a need for more exploration of PBD-peptide pairs to be used in this model. Scaffold characterization is challenging because it is often dependent on a model pathway with an output that is difficult to measure quantitatively. Here, we use a protein-fragment complementation assay (PCA) to study scaffolding efficiency in yeast, which allows to couple scaffolding efficiency with growth rate. First, we characterize the strength of PBD-peptide interactions (PPI) and the binding availability of the PBDs and peptides. Then, we test different scaffold architectures and expression levels to quantify the simultaneous binding of peptide pairs to the scaffold. We show that PPI strength of the weakest binding PBD-peptide pair is critical for scaffolding efficiency and that PPI strength is limited by low binding availability of some domains and peptides in vivo. Also, we find that slight architectural variations and expression levels have a significant impact on scaffolding efficiency detected by DHFR PCA. Finally, we used DHFR PCA approaches to characterize novel PBD-peptide pairs and we identified pairs to expand the sequence toolbox for scaffold design in yeast through DHFR PCA easy-to-read signal.

Whole-cell biosensors detecting the heavy metal arsenic have been widely studied for their potential in environmental monitoring. And while inducible biosensors have been shown to be an effective tool to tune the operational range, a thoroughly characterized inducible biosensor is currently lacking. Here, we present an Escherichia coli biosensor for arsenic in which the transcription factor (TF) gene arsR is inducible by naringenin, a plant-derived secondary metabolite. Increasing the naringenin concentration reduced the basal output while increasing both the dynamic range and sensing threshold of the biosensor dose-response curve, but the operational range appeared constrained by a fixed upper limit. Comparison with a previously published phenomenological model revealed good overall agreement between experimental data and model predictions, except for the behaviour of the maximum output and threshold. This work expands the biosensor toolbox with a profoundly characterized arsenic biosensor and raises a potential practical limit to dose-response curve engineering by tuning TF expression alone.

Guide RNA (gRNA) arrays can enable targeting multiple genomic loci simultaneously using CRISPR-Cas9. In this study, we present a streamlined and efficient method to rapidly construct gRNA arrays with up to 10 gRNA units in a single day. We demonstrate that gRNA arrays maintain robust functional activity across all positions, and can incorporate libraries of gRNAs, combining scalability and multiplexing. Our approach will streamline combinatorial perturbation research by enabling the economical and rapid construction, testing, and iteration of gRNA arrays. To facilitate the adoption of this approach, we have made a web tool to design oligo sequences necessary to assemble gRNA arrays.

Fine-tuning of gene expression is often required to achieve competitive production levels in microbial cell factories. Several orthogonal expression systems based on heterologous regulatory parts have been developed for Saccharomyces cerevisiae. In laboratory conditions the systems demonstrate predictable results, but few expression systems have been tested in industrial conditions. Here, a new expression system based on the bacterial gene cusR was developed for S. cerevisiae, and two previous developed systems, the strong Bm3R1-based system and the quinic acid inducible Q-system, were adapted for compatibility with the Yeast MoClo Toolkit. The bacterial transcription factors CusR and Bm3R1 acted as DNA binding domains, and fused to a viral activation domain, they functioned as transcriptional activators. The Q-system is originally from Neurospora crassa and consists of a transcriptional repressor, QS, which in the absence of quinic acid blocks the activity of a transcriptional activator, QF2. Quinic acid binds to QS, inhibiting QS from blocking the activity of QF2 in a dose-dependent manner. The gene expression systems were assessed in industrially relevant conditions, proving a predictable performance at low pH. The performance of the constitutive systems was predictable also at high temperature and in a synthetic lignocellulosic hydrolysate medium. Altogether, the MoClo-compatible expression systems enable fast construction of fine-tuned production pathways for S. cerevisiae cell factories used for industrial applications.

The β-proteobacterial species Curvibacter sp. AEP1-3 is a model organism for the study of symbiotic interactions as it is the most abundant colonizer of Hydra vulgaris. Yet, genetic tools for Curvibacter are still in their infancy; few promoters have been characterized so far. Here, we employ an oligonucleotide-based strategy to develop novel expression systems Curvibacter. Potential promoters were systematically mined from the genome in silico. The sequences were cloned as a mixed library into a mCherry reporter vector and positive candidates were selected by Flow Cytometry to be further analysed through plate reader measurements. From 500 candidate sequences, 25 were identified as active promoters of varying expression strength levels. Plate reader measurements revealed unique activity profiles for these sequences across growth phases. The expression levels of these promoters ranged over two orders of magnitudes and showed distinct temporal expression dynamics over the growth phases: while three sequences showed higher expression levels in the exponential phase, we found 12 sequences saturating expression during stationary phase and 10 that showed little discrimination between growth phases. From our library, promoters of the genes dnaK, rpsL and an acyl-homoserine-lactone (AHL) synthase stood out as the most interesting candidates fit for a variety of applications. We identified enriched transcription factor binding motifs among the sorted 33 sequences and genes encoding for homologs of these transcription factors in close proximity to the identified motifs. In this work, we show the value of employing comprehensive high-throughput strategies to establish expression systems for novel model organisms.

Sophisticated genetic engineering tasks such as protein domain grafting and multi-gene fusions are hampered by the lack of suitable vector backbones. In particular, many restriction sites are in the backbone outside the polylinker region (multiple cloning site; MCS) and thus unavailable for use, and the overall length of a plasmid correlates with poorer ligation efficiency. To address this need, we describe the design and validation of a collection of six minimal integrating or centromeric shuttle vectors for Saccharomyces cerevisiae, a widely used model organism in synthetic biology. We constructed the plasmids using de novo gene synthesis and consisting only of a yeast selection marker (HIS3, LEU2, TRP1, URA3, KanMX, or natMX6), a bacterial selection marker (ampicillin resistance), an origin of replication, and the MCS flanked by M13 forward and reverse sequences. We used truncated variants of these elements where available and eliminated all other sequences typically found in plasmids. The MCS consists of ten unique restriction sites. To our knowledge, at sizes ranging from ~2.6 to 3.5 kb, these are the smallest shuttle vectors described for yeast. Further, we removed common restriction sites in the open reading frames and terminators, freeing up ~30 cut sites in each plasmid. We named our pLS series in accordance with the well-known pRS vectors, which are on average 63% larger: pLS400, pLS410 (KanMX); pLS403, pLS413 (HIS3); pLS404, pLS414 (TRP1); pLS405, pLS415 (LEU2); pLS406, pLS416 (URA3); and pLS408, pLS418 (natMX6). This resource substantially simplifies advanced synthetic biology engineering in S. cerevisiae.

Heparosan is a natural polymer with unique chemical and biological properties, that holds great promise for biomedical applications. The molecular weight (Mw) and polydispersion index (PDI) are critical factors influencing the performance of heparosan-based materials. Achieving precise control over the synthesis process to consistently produce heparosan with low Mw and low PDI can be challenging, as it requires tight regulation of reaction conditions, enzyme activity, and precursor concentrations. We propose a novel approach utilizing synthetic biology principles to precisely control heparosan biosynthesis in bacteria. Our strategy involves designing a biomolecular controller that can regulate the expression of genes involved in heparosan biosynthesis. This controller is activated by biosensors that detect heparosan precursors, allowing for fine-tuned control of the polymerization process. Through this approach, we foresee the implementation of this synthetic device, demonstrating the potential to produce low Mw and low PDI heparosan in the probiotic E. coli Nissle 1917 as a biosafe and biosecure biofactory. This study represents a significant advancement in the field of heparosan production, offering new opportunities for the development and manufacturing of biomaterials with tailored properties for diverse biomedical applications.

Plant synthetic biologists have been working to adapt the CRISPRa and CRISPRi promoter regulation methods for applications such as improving crops or installing other valuable pathways. With other organisms, strong transcriptional control has typically required multiple gRNA target sites, which poses a critical engineering choice between heterogeneous sites, which allow each gRNA to target existing locations in a promoter, and identical sites, which typically require modification of the promoter. Here, we investigate the consequences of this choice for CRISPRi plant promoter regulation via simulation-based analysis, using model parameters based on single gRNA regulation and constitutive promoters in Nicotiana benthamiana and Arabidopsis thaliana. Using models of 2–6 gRNA target sites to compare heterogeneous versus identical sites for tunability, sensitivity to parameter values, and sensitivity to cell-to-cell variation, we find that identical gRNA target sites are predicted to yield far more effective transcriptional repression than heterogeneous sites.