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Results for “Synthetic Biology”

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bioRxiv2025

Enabling regiospecific di-halogenation in one-pot reactions using an engineered single-component flavin-dependent tryptophan halogenase

Installation of halogen atoms to organic compounds through flavin-dependent halogenases (FDHs) catalyzed reactions has emerged as an attractive strategy in organic biochemistry and synthetic biology. A cyanobacterial FDH termed AetF that contains FDH and flavin reductase in the same polypeptide chain could become a useful tool enzyme as the need of including a reductase is omitted. Notably, AetF exploits a unique substrate-interaction network to consecutively brominate tryptophan (Trp) on C5 and then C7 to generate di-brominated Trp. In this study, structure-based engineering is used to eliminate the C7 halogenation capacity of AetF. The resulting variant AetF-AIF that contains three residue substitutions mainly catalyzes bromination or iodination on C5 of Trp. We also show that combining AetF-AIF in tandem with the wild type AetF enables the generation of heterogeneously halogenated Trp in a one-pot reaction. The final products, 5-Br-7-I-Trp or 5-I-7-Br-Trp depending on the addition order of halides, account for a high ratio in the reaction mixture without additional purification step. These results display the potentials of single-component Trp-FDHs in a wide application of the halogenation reactions. Table of Content Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/687318v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@101bd34org.highwire.dtl.DTLVardef@12ca797org.highwire.dtl.DTLVardef@aee280org.highwire.dtl.DTLVardef@1984907_HPS_FORMAT_FIGEXP M_FIG A single-component flavin-dependent halogenase that catalyzes di-bromination of tryptophan was engineered as a mono-halogenase and coupled in a one-pot reaction for the synthesis of heterogeneously halogenated tryptophan. C_FIG

molecular biology↗

bioRxiv2025

Quantification of Cholesterol Incorporation in Giant Unilamellar Vesicles Produced by a Modified cDICE Method

AbstractCholesterol is an essential component of eukaryotic cell membranes, influencing membrane packing, fluidity, and domain formation. Replicating these properties in model membranes is critical for reconstitution studies, but common emulsion-based methods for producing giant unilamellar vesicles (GUVs) fail to incorporate cholesterol efficiently. Here, we use methyl-{beta}- cyclodextrin-cholesterol (M{beta}CD-CL) complexes to deliver cholesterol into GUVs produced by the emulsion droplet interface crossing encapsulation (eDICE) method and demonstrate a convenient way to quantify the degree of cholesterol incorporation using fluorescent membrane biosensors. Spectral imaging of NR12A as well as fluorescence lifetime imaging of Flipper-TR revealed dose- dependent increases in cholesterol content for DOPC GUVs upon M{beta}CD-CL addition, consistent with increased membrane order. By calibrating these effects against GUVs with defined cholesterol contents prepared via gel-assisted swelling, we found that the cholesterol content of eDICE vesicles can be increased to at least 40 mol%. Binary mixtures of DOPC with saturated lipids (DMPC and PC(18:0-14:0)) showed a similar trend as pure DOPC GUVs. Interestingly, we could trigger liquid-ordered domain formation by adding cholesterol to DOPC:DMPC vesicles. Our findings provide a quantitative and non-disruptive method to modulate and assess cholesterol content in emulsion-based GUVs, advancing their use in bottom-up synthetic biology and membrane biophysics.

biophysics↗

bioRxiv2025

A unique mechanism explaining the outstanding performance of a newly discovered doxycycline riboswitch

Synthetic riboswitches offer a versatile and protein-independent solution for conditional gene regulation. They consist of a regulatory domain and an aptamer domain that binds a specific ligand, with their performance largely depending on the ability of the aptamer to control the regulatory domain. Expanding the range of synthetic riboswitches therefore requires the discovery and characterization of new regulatory aptamers. In the present study, we identified a doxycycline-binding aptamer with outstanding regulatory properties in both yeast and human cells which are based on a unique structural dynamic upon ligand binding. Single-molecule force spectroscopy revealed that doxycycline binding strongly stabilizes an intermediate aptamer conformation offering mechanistic insights into its function. The identification of the aptamer through a combination of parallel SELEX and subsequent in vivo screening in yeast, also provided valuable insights into selection dynamics and the first proof for the effectiveness of RNA Capture-SELEX in aptamer selection. Together, the presented data deepen our understanding of regulatory aptamer selection and functionality while adding a high-performing doxycycline-responsive aptamer to the synthetic biology toolbox.

molecular biology↗

bioRxiv2025

Functional genomics in a microbe that degrades and metabolizes PET plastic

Piscinibacter sakaiensis (formerly Ideonella sakaiensis) was the first bacterial species known to both completely degrade and assimilate polyethylene terephthalate (PET). However, the absence of efficient genetic tools has limited direct engineering of this organism, forcing most efforts to rely on heterologous expression of PET-degrading enzymes in model hosts. Here, we establish foundational genetic tools to engineer P. sakaiensis. We identify a functional plasmid origin of replication, multiple new selectable markers, and a transposon system for the strain. We use these tools to construct a genome-wide, barcoded transposon mutant library for pooled high-throughput functional screens. We apply this mutant library to growth on PET and identify metabolic and physiological genes that impact PET biodegradation. We also use this library to reveal mutants with improved DNA uptake for genome engineering. Together, these advances provide a platform for functional genomics in P. sakaiensis and positions this naturally evolved plastic-degrading bacterium as an engineerable chassis for synthetic biology and sustainable materials research.

microbiology↗

bioRxiv2025

Overcoming the eIF2α Brake in Human Cell-Derived Translation Systems

Cell-free translation from human cells is a powerful platform for studying mammalian gene expression and building synthetic biology tools, but productivity is often curtailed by inhibitory phosphorylation of eIF2 on residue Ser52. Here we systematically explored complementary strategies to bypass this initiation block across editable and hard-to-edit human cell types. In Expi293F suspension cells, precise genome editing of EIF2S1 to block Ser52 phosphorylation (eIF2-S52A) produced high-activity extracts. Genetic knockout of EIF2AK2 (PKR)-the principal eIF2 kinase engaged in eIF2 phosphorylation in Expi293F lysates-also improved translation, further establishing eIF2 phosphorylation as the dominant bottleneck in Expi293F translation extracts. Because genome editing is impractical in many contexts including primary human cells, we also implemented expression-based rescue of eIF2 phosphorylation: stable piggyBac integration of truncated GADD34 (PPP1R15A) and K3L, a viral eIF2 decoy, under control of a Tet-inducible promotor in induced pluripotent stem cells (iPSCs) and primary human fibroblasts. After differentiating engineered KOLF2.1J iPSCs into cardiomyocytes, we found that stable GADD34/K3L expression increased translation output in cardiomyocyte translation extracts. Using the piggyBac expression system in primary fibroblasts also resulted in improved translational output. Together these data pinpoint eIF2 phosphorylation as the key barrier to robust translation in human cell translation extracts. They also show that editing eIF2 or removing PKR is optimal where genome editing is feasible, while providing a portable GADD34/K3L expression cassette enables production of translationally active lysates from systems where genome editing is challenging or not possible.

biochemistry↗

bioRxiv2025

Flux modelling analysis reveals the metabolic impact of cryptic plasmids and environmental conditions in probiotic Escherichia coli Nissle 1917

Escherichia coli Nissle 1917 (EcN) is a well-characterized Gram-negative probiotic distinguished by its unique, strain-specific physiology. Genome-scale metabolic models (GEMs) are powerful tools for elucidating metabolic traits and predicting genotype-phenotype relationships. Although several EcN GEMs have been published, none have explicitly represented its probiotic physiology. Here, we present a manually curated GEM of EcN that, for the first time, incorporates the energetic costs associated with its cryptic plasmids. Inclusion of a plasmid-specific module improved biomass yield predictions and overall model accuracy, providing a more physiologically realistic representation of EcN metabolism. Using COBRA methodologies and possibilistic metabolic flux analysis, this model and previous EcN reconstructions were systematically compared to evaluate the trade-off between model complexity and predictive performance. The analysis revealed that increased structural detail does not necessarily enhance quantitative accuracy and that predictive reliability depends on both computational methodology and model context. Metabolomic profiling under gut-like anaerobic conditions further showed that EcN exhibits a distinctive metabolic phenotype, characterized by elevated amino acid consumption and enhanced short-chain fatty acid production. These findings highlight the unique probiotic physiology of EcN and demonstrate the utility of metabolic modeling for reproducing and exploring such traits. Overall, this study provides a quantitatively reliable and physiologically relevant framework for modeling E. coli Nissle 1917 and related commensal bacteria, supporting advances in probiotic engineering, synthetic biology, and bioprocess design. Graphical Abstract SummaryThis study presents a manually curated genome-scale model of Escherichia coli Nissle 1917 that accounts for the metabolic cost of its cryptic plasmids. Through systematic comparison with previous reconstructions and validation against fluxomics datasets, the models improved accuracy in predicting growth and fluxes. Simulations and experiments under gut-like conditions provide new insights into EcNs unique probiotic traits. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/688048v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@6c8c98org.highwire.dtl.DTLVardef@8263e3org.highwire.dtl.DTLVardef@6c04c3org.highwire.dtl.DTLVardef@1acb811_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Overriding bioprocess perturbations with a cell machine interface for reliable microbial stress-response control

Controlling cell population dynamics and phenotypic diversification is a key objective in systems and synthetic biology, particularly for ensuring uniform responses from engineered gene circuits. While cell-machine interfaces have been employed to modulate host-gene circuit interactions, environmental perturbations typical of industrial bioreactor conditions remain underexplored. In this study, we investigate the impact of such perturbations on the general stress response in Escherichia coli and Saccharomyces cerevisiae. Using scale-down bioreactor experiments, we evaluate the performance of the Segregostat, a real-time control system that leverages automated flow cytometry to induce dynamic nutrient shifts. The Segregostat achieves robust stress response control, even under severe perturbations such as extended residence times in a two-compartment reactor. We hypothesize that this robustness arises from the systems ability to amplify host-compatible fluctuations beyond bioreactor-induced perturbations. Our findings highlight the importance of integrating environmental factors into control strategies for reliable gene circuit behavior in industrial bioprocessing environments.

bioengineering↗

bioRxiv2025

Computational design of cysteine proteases

Despite advances in de novo enzyme design, success has been largely limited to low energy barrier model reactions. Amide bonds such as those linking amino acids along the peptide backbone are stable for hundreds of years in neutral aqueous solution because of the high energy barrier to hydrolysis. Here we describe the de novo design of enzymes which utilize an activated cysteine nucleophile to hydrolyze the polypeptide backbone in a sequence-dependent manner, with a success rate of 13/69=19% and rate enhancements over the background reaction (kcat/kuncat) of up to 3 x 10^7. The designed proteases have folds very different from proteases in nature (TM score < 0.50), and six crystal structures are very close to the design models (Ca RMSDs < 1.2 A), highlighting the capacity for generalization and the accuracy of the design methodology. Experimental and computational analyses suggest that the remaining gap in activity to the most active native cysteine proteases arises from imperfections in active site preorganization and substrate positioning. The designed proteases efficiently cleave their targets in mammalian cells, opening the door to a wide range of synthetic biology applications.

biochemistry↗

bioRxiv2025

Fluorescent Biosensor-Guided Engineering of Enzyme Cascades for Electrochemical Applications

Substrate channeling is a strategy for enhancing flux and yield in enzymatic cascades and is increasingly relevant for applications in biocatalysis, biotechnology, and bioelectrochemical systems. However, efforts to engineer channeling are limited by the lack of high-throughput methods to evaluate and optimize channeling efficiency. Here, we present a fluorescence-based screening assay to rapidly assess substrate channeling in a model system involving fumarase and malate dehydrogenase, two sequential enzymes from the Krebs cycle. By expressing genetic fusions in E. coli, quantifying intermediate (malate) and product (NADH) formation in lysate using orthogonal fluorescent readouts, and comparing product-to-intermediate ratios, we screened a library of linker variants designed to promote electrostatic channeling. A top-performing construct was identified and validated through classical channeling assays. This hit demonstrated increased product yield and current output when immobilized on electrodes with a bilayer architecture, highlighting utility in bioelectrocatalysis. We further showed that the channeling linker could be applied to a de novo designed single-chain fumarase, which preserved channeling capability and exhibited improved thermal stability. These results establish a generalizable and scalable method for engineering and evolving substrate channeling, with broad implications for pathway optimization and enzyme design in synthetic biology, bioprocessing, and energy applications.

biochemistry↗

bioRxiv2017

Impact of division rate and cell size on gene expression noise

The cell division rate, size, and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli d other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persistors cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding of circuits robustness across growth conditions is key for the effective design of synthetic biological systems.

systems biology↗

bioRxiv2017

Active deep learning reduces annotation burden in automatic cell segmentation

The relationship between cellular architecture and cellular state and function is apparent, but not yet completely understood. Precise characterization of cellular state is important in many fields, from pathology to synthetic biology. High-content high-throughput microscopy is now more than ever accessible to researchers. This allows for collection of large amount of cellular images. Naturally, the analysis of this data cannot be left to manual investigation and needs to resort to the use of efficient computing algorithms for cellular detection, segmentation, and tracking. Annotation is required for building high quality algorithms. Medical professionals and researchers spend a lot of effort and time in annotating cells. This task has proved to be very repetitive and time consuming. The experts time is valuable and should be used effectively. Our hypothesis is that active deep learning will help to share some of the burden that researchers face in their everyday work. In this paper, we focus specifically on the problem of cellular segmentation.\n\nWe approach the segmentation task using a classification framework. Each pixel in the image is classified based on whether the patch around it resides on the interior, boundary or exterior of the cell. Deep convolutional neural networks (CNN) are used to perform the classification task. Active learning is the method used to reduce the annotation burden. Uncertainty sampling, a popular active learning framework is used in conjunction with CNN to segment the cells in the image. Three datasets of mammalian nuclei and cytoplasm are used for this work. We show that active deep learning significantly reduces the number of training samples required and also improves the quality of segmentation.

bioinformatics↗

bioRxiv2017

Assessing and characterising the repertoire of constitutive promoter elements in soil metagenomic libraries in Escherichia coli

Although functional metagenomics has been widely employed for the discovery of genes relevant to biotechnology and biomedicine, its potential for assessing the diversity of transcriptional regulatory elements of microbial communities has remained poorly explored. Here, we have developed a novel framework for prospecting, characterising and estimating the accessibility of promoter sequences in metagenomic libraries by combining a bi-directional reporter vector, high-throughput fluorescence assays and predictive computational methods. Using the expression profiling of fluorescent clones from two independent libraries from soil samples, we directly analysed the regulatory dynamics of novel promoter elements, addressing the relationship between the \"metaconstitutome\" of a bacterial community and its environmental context. Through the construction and screening of plasmid-based metagenomic libraries followed by in silico analyses, we were able to provide both (i) a consensus exogenous promoter elements recognizable by Escherichia coli and (ii) an estimation of the accessible promoter sequences in a metagenomic library, which was close to 1% of the whole set of available promoters. The results presented here should provide new directions for the exploration through functional metagenomics of novel regulatory sequences in bacteria, which could expand the Synthetic Biology toolbox for novel biotechnological and biomedical applications.

microbiology↗

bioRxiv2017

Rapid, modular, and cost-effective generation of donor DNA constructs for CRISPR-based gene knock-in

CRISPR-based gene knock-in at endogenous sites is desirable in multiple fields such as quantitative studies of signal transduction pathways and gene regulation, synthetic biology, and disease modeling. Contrasting the knock-out procedure, a key step of CRISPR knock-in procedure relies on the homology-directed repairing (HDR) process that requires a donor construct as repair template. Therefore, it is desirable to generate a series of donor DNA constructs efficiently and cost-effectively. In this study, we developed a general Gibson assembly procedure that combines strengths of a Modular Overlap-Directed Assembly with Linkers (MODAL) strategy and a restriction enzyme based hierarchical framework. This procedure also allows fusing sgRNAs to the constructs for enhanced homology-directed repairing efficiency. Experimental tests on multiple constructs achieved from 3-8 folds of increase in assembly efficiency to high yield of constructs that failed to make with conventional Gibson assembly. The modularized procedure is simple, fast and cost-effective while making multiple constructs, and a computer package is provided for customized design.

molecular biology↗

bioRxiv2017

Peripheral infrastructure vectors and an extended set of plant parts for the modular cloning system

Standardized DNA assembly strategies facilitate the generation of multigene constructs from collections of building blocks in plant synthetic biology. A common syntax for hierarchical DNA assembly following the Golden Gate principle employing Type IIs restriction endonucleases was recently developed, and underlies the Modular Cloning and GoldenBraid systems. In these systems, transcriptional units and/or multigene constructs are assembled from libraries of standardized building blocks, also referred to as phytobricks, in several hierarchical levels and by iterative Golden Gate reactions. This combinatorial assembly strategy meets the increasingly complex demands in biotechnology and bioengineering, and also represents a cost-efficient and versatile alternative to previous molecular cloning techniques. For Modular Cloning, a collection of commonly used Plant Parts was previously released together with the Modular Cloning toolkit itself, which largely facilitated the adoption of this cloning system in the research community. Here, a collection of approximately 80 additional phytobricks is provided. These phytobricks comprise e.g. modules for inducible expression systems, different promoters or epitope tags, which will increase the versatility of Modular Cloning-based DNA assemblies. Furthermore, first instances of a \"peripheral infrastructure\" around Modular Cloning are presented: While available toolkits are designed for the assembly of plant transformation constructs, vectors were created to also use coding sequence-containing phytobricks directly in yeast two hybrid interaction or bacterial infection assays. Additionally, DNA modules and assembly strategies for connecting Modular Cloning with Gateway Cloning are presented, which may serve as an interface between available resources and newly adopted hierarchical assembly strategies. The presented material will be provided as a toolkit to the plant research community and will further enhance the usefulness and versatility of Modular Cloning.

plant biology↗

bioRxiv2018

Efficient computation of co-transcriptional RNA-ligand interaction dynamics

Riboswitches form an abundant class of cis-regulatory RNA elements that mediate gene expression by binding a small metabolite. For synthetic biology applications, they are becoming cheap and accessible systems for selectively triggering transcription or translation of downstream genes. Many riboswitches are kinetically controlled, hence knowledge of their co-transcriptional mechanisms is essential. We present here an efficient implementation for analyzing co-transcriptional RNA-ligand interaction dynamics. This approach allows for the first time to model concentration-dependent metabolite binding/unbinding kinetics. We exemplify this novel approach by means of the recently studied I-A 2-deoxyguanosine (2dG)-sensing riboswitch from Mesoplasma florum.

bioinformatics↗

bioRxiv2018

Ostreococcus tauri is a high-lipid content green algae that extrudes clustered lipid droplets

Lipid droplet biogenesis, accumulation and secretion is an important field of research spanning biofuel feedstock production in algae and yeast to plant-microbe symbiosis or human metabolic disorders and other diseases. Here we evaluate the critical elements that influence lipid accumulation in the highly simplified and smallest known eukaryote Ostreococcus tauri and identify several conditions that satisfy its classification as an oleaginous green alga. In addition, these experiments revealed the release of excess lipids in pea-pod like structures where many dense lipid droplets are clustered in a linear fashion surrounded by an enveloping membrane which contrasts with known mechanisms from other eukaryotes. These results highlight the potential for Ostreococcus tauri to probe the evolution of lipid droplet dynamics as an emerging model organism with a compacted eukaryotic genome and also to impact lipid feedstock bioproduction applications either directly or using synthetic biology.\n\nOne Sentence SummaryThe smallest known eukaryote Ostreococcus tauri is oleaginous and sheds lipid droplets as pea-pod like membrane enclosed clusters.

cell biology↗

bioRxiv2018

Single day construction of multi-gene circuits with 3G assembly

The ability to rapidly design, build, and test prototypes is of key importance to every engineering discipline. DNA assembly often serves as a rate limiting step of the prototyping cycle for synthetic biology. Recently developed DNA assembly methods such as isothermal assembly and type IIS restriction enzyme systems take different approaches to accelerate DNA construction. We introduce a hybrid method, Golden Gate-Gibson (3G), that takes advantage of modular part libraries introduced by type IIS restriction enzyme systems and isothermal assembly s ability to build large DNA constructs in single pot reactions. Our method is highly efficient and rapid, facilitating construction of entire multi-gene circuits in a single day. Additionally, 3G allows generation of variant libraries enabling efficient screening of different possible circuit constructions. We characterize the efficiency and accuracy of 3G assembly for various construct sizes, and demonstrate 3G by characterizing variants of an inducible cell-lysis circuit.

bioengineering↗

bioRxiv2018

Adhesion of active cytoskeletal vesicles

Regulation of adhesion is a ubiquitous feature of living cells, observed during processes such as motility, antigen recognition or rigidity sensing. At the molecular scale, a myriad of mechanisms are necessary to recruit and activate the essential proteins, while at the cellular scale efficient regulation of adhesion relies on the cells ability to adapt its global shape. To understand the role of shape remodeling during adhesion, we use a synthetic biology approach to design a minimal model, starting with a limited number of building blocks. We assemble cytoskeletal vesicles whose size, reduced volume, and cytoskeleton contractility can be independently tuned. We are able to show that these cytoskeletal vesicles can sustain strong adhesion to solid substrates only if molecular motors are able to actively remodel the actin cortex. When the cytoskeletal vesicles are deformed under hypertonic osmotic pressure, they develop a crumpled geometry with huge deformations. In the presence of molecular motors, these deformations are dynamic in nature and can compensate for an absence of excess membrane area needed for adhesion to take place. When the cytoskeletal deformations are able to compensate for lack of excess membrane area, the cytoskeletal vesicles are able to attach to the rigid glass surfaces even under strong adhesive forces. The balance of deformability and adhesion strength is identified to be key to enable cytoskeletal vesicles to adhere to solid substrates.

biophysics↗