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Results for “synthetic biology”

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bioRxiv2018

Optimally designed vs intuition-driven inputs: the study case of promoter activity modelling

Synthetic biology is an emerging engineering discipline that aims at synthesising logical circuits into cells to accomplish new functions. Despite a thriving community and some notable successes, the basic task of assembling predictable gene circuits is still a key challenge. Mathematical models are uniquely suited to help solve this issue. Yet in biology they are perceived as expensive and laborious to obtain because low-information experiments have often been used to infer model parameters. How much additional information can be gained using optimally designed experiments? To tackle this question we consider a building block in Synthetic Biology, an inducible promoter in yeast S. cerevisiae. Using in vivo data we re-fit a mathematical model for such a system; we then compare in silico the quality of the parameter estimates when model calibration is done using typical (e.g. step inputs) and optimally designed experiments. We find that Optimal Experimental Design leads to ~70% improvement in the predictive ability of the inferred models. We conclude providing suggestions on how optimally designed experiments can be implemented in vivo.

bioengineering↗

bioRxiv2024

Predictable modulation of plant root development using engineered cytokinin regulators

Achieving precise control over quantitative developmental phenotypes is a key objective in plant biology. Recent advances in synthetic biology have enabled tools to reprogram entire developmental pathways; however, the complexity of designing synthetic genetic programs and the inherent interactions between various signaling processes remains a critical challenge. Here, we leverage Type-B response regulators to modulate the expression of genes involved in cytokinin-dependent growth and development processes. We rationally engineered these regulators to modulate their transcriptional activity (i.e., repression or activation) and potency while reducing their sensitivity to cytokinin. By localizing the expression of these engineered transcription factors using tissue-specific promoters, we can predictably tune cytokinin-regulated traits. As a proof of principle, we deployed this synthetic system in Arabidopsis thaliana to either decrease or increase the number of lateral roots. The simplicity and modularity of our approach makes it an ideal system for controlling other developmental phenotypes of agronomic interest in plants.

plant biology↗

bioRxiv2023

TReSR: A PCR-compatible DNA sequence design method for engineering proteins containing tandem repeats

Protein tandem repeats (TRs) are motifs comprised of near-identical contiguous sequence duplications. They are found in approximately 14% of all proteins and are implicated in diverse biological functions facilitating both structured and disordered protein-protein and protein-DNA interactions. These functionalities make protein TR domains an attractive component for the modular design of protein constructs. However, the repetitive nature of DNA sequences encoding TR motifs complicates their synthesis and mutagenesis by traditional molecular biology workflows commonly employed by protein engineers and synthetic biologists. To address this challenge, we developed a computational protocol to significantly reduce the complementarity of DNA sequences encoding TRs called TReSR (for Tandem Repeat DNA Sequence Redesign). The utility of TReSR was demonstrated by constructing a novel constitutive repressor synthesized by duplicating the LacI DNA binding domain into a single-chain TR construct by assembly PCR. Repressor function was evaluated by expression of a fluorescent reporter delivered on a single plasmid encoding a three-component genetic circuit. The successful application of TReSR to construct a novel TR-containing repressor with a DNA sequence that is amenable to PCR-based construction and manipulation will enable the incorporation of a wide range of TR-containing proteins for protein engineering and synthetic biology applications.

synthetic biology↗

bioRxiv2025

Structural and functional characterisation of a reconstructed ancestral strigolactone receptor

Strigolactones are phytohormones that regulate shoot branching and promote root interactions with arbuscular mycorrhizal fungi. In seed plants, strigolactone perception begins with the enzyme-receptor DWARF14 (D14), an /{beta}-hydrolase that is believed to have evolved via gene duplication from the karrikin receptor, KARRIKIN INSENSITIVE2 (KAI2). D14 and KAI2 are highly homologous at the sequence and structural levels, and both proteins bind and cleave similar butenolide compounds. Nevertheless, the two signalling pathways are distinct, as the activity of one receptor cannot compensate for the loss of the other. Here, we apply ancestral sequence reconstruction to generate a D14 protein representative of seed plants to study the evolution of substrate specificity, and to explore desirable traits for protein engineering. We describe the structure, as well as the in vitro and in planta activity of ancestral D14, showing that substrate specificity does not meaningfully differ from that of Arabidopsis thaliana D14. We also demonstrate that ancestral D14 shows higher recombinant yields, greatly increased thermostability, and enhanced catalytic activity relative to D14 from A. thaliana. This work provides insight into the evolution of phytohormone signalling and presents a robust scaffold for the application of D14-type proteins in synthetic biology. SIGNIFICANCE STATEMENTAncestral sequence reconstruction of the strigolactone receptor DWARF14 (D14) reveals that its substrate specificity has remained largely conserved in seed plants. Additionally, the ancestral reconstruction exhibits superior recombinant yields, thermostability, and catalytic activity relative to Arabidopsis thaliana D14, providing both evolutionary insights and promising utility for synthetic biology applications.

plant biology↗

bioRxiv2023

Systematic identification of transcriptional activator domains from non-transcription factor proteins in plants and yeast

Transcription factors promote gene expression via trans-regulatory activation domains. Although whole genome scale screens in model organisms (e.g. human, yeast, fly) have helped identify activation domains from transcription factors, such screens have been less extensively used to explore the occurrence of activation domains in non-transcription factor proteins, such as transcriptional coactivators, chromatin regulators and some cytosolic proteins, leaving a blind spot on what role activation domains in these proteins could play in regulating transcription. We utilized the activation domain predictor PADDLE to mine the entire proteomes of two model eukaryotes, Arabidopsis thaliana and Saccharomyces cerevisiae (1). We characterized 18,000 fragments covering predicted activation domains from >800 non-transcription factor genes in both species, and experimentally validated that 89% of proteins contained fragments capable of activating transcription in yeast. Peptides with similar sequence composition show a broad range of activities, which is explained by the arrangement of key amino acids. We also annotated hundreds of nuclear proteins with activation domains as putative coactivators; many of which have never been ascribed any function in plants. Furthermore, our library contains >250 non-nuclear proteins containing peptides with activation domain function across both eukaryotic lineages, suggesting that there are unknown biological roles of these peptides beyond transcription. Finally, we identify and validate short, universal eukaryotic activation domains that activate transcription in both yeast and plants with comparable or stronger performance to state-of-the-art activation domains. Overall, our dual host screen provides a blueprint on how to systematically discover novel genetic parts for synthetic biology that function across a wide diversity of eukaryotes. Significance StatementActivation domains promote transcription and play a critical role in regulating gene expression. Although the mapping of activation domains from transcription factors has been carried out in previous genome-wide screens, their occurrence in non-transcription factors has been less explored. We utilize an activation domain predictor to mine the entire proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae for new activation domains on non-transcription factor proteins. We validate peptides derived from >750 non-transcription factor proteins capable of activating transcription, discovering many potentially new coactivators in plants. Importantly, we identify novel genetic parts that can function across both species, representing unique synthetic biology tools.

genetics↗

bioRxiv2025

Elucidation and de novo Reconstitution of Glyceollins Biosynthesis

Glyceollins are phytoalexins produced by soybeans in response to stressors such as pathogen invasion, injury, and environmental challenges. In addition to their antibacterial and antifungal activities, these compounds have attracted significant attention for their potential anticancer, anti-inflammatory, and antioxidant properties. However, their limited accessibility--due to the challenges and high costs associated with organic synthesis or purification from treated soybean seedlings--has hindered further physiological and biochemical studies. Moreover, the incomplete understanding of glyceollin biosynthesis, particularly the final cyclization steps, remains a major barrier to elucidating the physiological functions of glyceollins and achieving sustainable production through synthetic biology. In this study, we identified previously uncharacterized genes encoding two reductases for 7,2,4-trihydroxyisoflavanol (THI) biosynthesis and five P450 enzymes responsible for the final oxidative cyclization in glyceollin I, II, and III biosynthesis, thereby completing the entire glyceollin biosynthetic pathway. By reconstructing the pathway de novo through synthetic biology, we achieved the successful production of glyceollins from simple carbon sources in bakers yeast. This work advances the understanding of glyceollin biosynthesis in soybean, facilitates sustainable production in microbial hosts and offers new opportunities for their application in agriculture and biology.

biochemistry↗

bioRxiv2026

Transcription terminators can be strong promoters

A dogma of RNA synthesis is that promoter activity and termination are unrelated processes governed by completely different DNA sequences. Serendipitously, we find that two class II terminators for bacteriophage T7 RNA polymerase (RNAP), the natural T7 concatemer junction (CJ) and the artificial vesicular stomatitis virus (VSV) terminator, are context-dependent strong promoters for E. coli RNAP. However, the third class II member, the artificial human preproparathyroid hormone (PTH) terminator, is not a promoter. Transcription start sites and mutagenesis for CJ and VSV reveal a {sigma}70 extended TGn promoter motif that is lacking in PTH. Furthermore, results resolve the prior paradox of class II termination apparently occurring only in vitro, not in vivo, enabling demonstration of class II termination in vivo by both T7 and E. coli RNAPs. In addition, engineering of T7 RNAP increases its efficiency of class II termination in vivo. Given that prokaryotic synthetic biology is reliant on class I (hairpin) terminators, with T{Phi} used almost exclusively in constructs for T7 RNAP, recombination is an issue in large constructs. Thus, small class II terminators, now better understood with respect to terminator and promoter activities in the context of various sequences and RNAPs, extends the synthetic biology toolkit.

molecular biology↗

bioRxiv2023

OpenCell: A Low-cost, Open-Source, 3-in-1 device for DNA Extraction

High-cost DNA extraction procedures pose significant challenges for budget-constrained laboratories. To address this, we introduce OpenCell, an economical, open-source, 3-in-1 laboratory device that combines the functionalities of a bead homogenizer, a microcentrifuge, and a vortex mixer. OpenCell utilizes modular attachments that magnetically connect to a central rotating brushless motor. This motor couples to an epicyclic gearing mechanism, enabling efficient bead homogenization, vortex mixing, and centrifugation within one compact unit. OpenCells design incorporates multiple redundant safety features, ensuring both the devices and operators safety. Additional features such as RPM measurement, programmable timers, battery operation, and optional speed control make OpenCell a reliable and reproducible laboratory instrument. In our study, OpenCell successfully isolated DNA from Spinacia oleracea (spinach), with an average yield of 2.3 {micro}g and an A260/A280 ratio of 1.77, demonstrating its effectiveness for downstream applications such as Polymerase Chain Reaction (PCR) amplification. With its compact size (20 cm x 28 cm x 6.7 cm) and lightweight design (0.8 kg), comparable to the size and weight of a laptop, OpenCell is portable, making it an attractive component of a lab-in-a-backpack for resource-constrained environments in low-and-middle-income countries and synthetic biology in remote field stations. Leveraging the accessibility of 3D printing and off-the-shelf components, OpenCell can be manufactured and assembled at a low unit cost of less than $50, providing an affordable alternative to expensive laboratory equipment costing over $4000. OpenCell aims to overcome the barriers to entry in synthetic biology research and contribute to the growing collection of frugal and open hardware.

bioengineering↗

bioRxiv2024

Engineering an anti-CD206-synNotch receptor: insightsinto the development of novel synthetic receptors

Immune cells play a pivotal role in the establishment, growth and progression of tumors at primary and metastatic sites. Macrophages, in particular, play a critical role in suppressing immune responses and promoting an anti-inflammatory environment through both direct and indirect cell-cell interactions. However, our understanding of the mechanisms underlying such interactions is limited due to a lack of reliable tools for studying transient interactions between cancer cells and macrophages within the tumor microenvironment. Recent advances in mammalian synthetic biology have introduced a wide range of synthetic receptors that have been used in diverse biosensing applications. One such synthetic receptor is the synNotch receptor, which can be tailored to sense specific ligands displayed on the surface of target cells. With this study, we aimed at developing a novel CD206-synNotch receptor, targeting CD206+ macrophages, a population of macrophages that play a crucial role in promoting metastatic seeding and persistent growth. Engineered in cancer cells and used in mouse metastasis models, such tool could help monitor and understand the effects cell-cell interactions between macrophages and cancer cells have on metastasis establishment. Here, we report the development of cancer landing pad cells for versatile applications, the engineering of CD206-synNotch cells, report the measurements of their activity and specificity, and discuss the unexpected caveats when considering their in vivo applications.

synthetic biology↗

bioRxiv2025

Evaluation of Engineering Potential in Undomesticated Microbes with VECTOR

Genetic engineering research has predominantly focused on well-characterized organisms like Escherichia coli and Bacillus subtilis, with methods that often fail to translate to other microorganisms. This limitation presents a significant challenge, particularly given the increasing isolation of large microbial collections through high-throughput culturomics. In response, we developed a scalable, high-throughput pipeline to evaluate the engineerability of diverse microbial community members we named VECTOR (Versatile Engineering and Characterization of Transferable Origins and Resistance). We utilized a library of vectors with the Bacterial Expression Vector Archive (BEVA) architecture that included combinations of three antibiotic resistance genes, and three broad host range origins of replication (pBBR1, RK2 and RSF1010) or the restricted host range R6K with an integrative mariner transposon. We tagged each vector with green fluorescent protein and a unique nucleotide barcode. The resulting plasmids were delivered en masse to libraries of undomesticated microbes from plant microbiomes in workflows designed to evaluate their ability to be engineered. Utilizing OD600 and relative fluorescence measurements, we were able to monitor genetic cargo transfer in real time, indicating successfully engineered strains. Next-generation sequencing of plasmid molecular barcodes allowed us to identify specific vector architectures that worked well in particular bacterial strains from a large community. Modifications to the procedure facilitated isolation of engineered microbes. Our results underscore the potential of this approach to rapidly develop toolkits for the efficient engineering of a wide range of cultivatable microorganisms. ImportanceUndomesticated, cultured microbial strains contain a largely untapped reservoir of genetic potential for synthetic biology, and are increasingly being utilized in synthetic communities for microbial ecology research or biotechnology.. However, these strains often have unique physiological or ecological characteristics that make them difficult to engineer using traditional methods. Current approaches are often restricted by inefficient plasmid delivery and integration, which stifles progress in unlocking the promise of undomesticated strains. Our research addresses this challenge by developing VECTOR (Versatile Engineering and Characterization of Transferable Origins and Resistance), a scalable, high-throughput pipeline that utilizes modular vectors and efficient engineering workflows to identify host range and improve plasmid uptake. By optimizing plasmid architectures and pooling them for simultaneous screening across a range of bacterial strains, VECTOR enhances engineering efficiency and opens new avenues for advancements in biotechnology.

synthetic biology↗

bioRxiv2022

A Comprehensive Conceptual and Computational Dynamics Framework for Autonomous Regeneration of Form and Function in Biological Organisms

In biology, regeneration is a mysterious phenomenon that has inspired self-repairing systems, robots, and biobots. It is a collective computational process whereby cells communicate to achieve an anatomical set point and restore original function in regenerated tissue or the whole organism. Despite decades of research, the mechanisms involved in this process are still poorly understood. Likewise, the current algorithms are insufficient to overcome this knowledge barrier and enable advances in regenerative medicine, synthetic biology, and living machines/biobots. We propose a comprehensive conceptual framework for the engine of regeneration with hypotheses for the mechanisms and algorithms of stem cell-mediated regeneration that enables a system like the planarian flatworm to fully restore anatomical (form) and bioelectric (function) homeostasis from any small- or large-scale damage. The framework extends the available regeneration knowledge with novel hypotheses to propose collective intelligent self-repair machines, with multi-level feedback neural control systems, driven by somatic and stem cells. We computationally implemented the framework to demonstrate the robust recovery of both anatomical and bioelectric homeostasis in an in silico worm that, in a simple way, resembles the planarian. In the absence of complete regeneration knowledge, the framework contributes to understanding and generating hypotheses for stem cell mediated form and function regeneration which may help advance regenerative medicine and synthetic biology. Further, as our framework is a bio-inspired and bio-computing self-repair machine, it may be useful for building self-repair robots/biobots and artificial self-repair systems. SummaryA conceptual framework for the machinery of self-repair in living systems that enables a synthetic organism to accurately regenerate form and function from any disturbance and damage.

bioinformatics↗

bioRxiv2025

Exploring the Modularity of Thalassiosira oceanica Aureochrome1 Photoreceptor-Transcription Factor: Characterization and Component Engineering

The sensory transcription factors carry out gene expression in response to external stimuli like light. Further, they can be used in synthetic biological circuits to manipulate gene expression and thus can alter behavioral responses effectively. In this study, we characterize blue-light responsive photoreceptor transcription factor, Aureochrome (ToAureo1), from photosynthetic marine diatom Thalassiosira oceanica. In silico modeling and docking studies on C-terminal LOV (light-oxygen-voltage) and N-terminal bZIP (basic leucine zipper) domain (ToAubZL) revealed conserved 3D structures as well as interaction with Aureo-box containing substrate DNA. Modularity is an essential feature for synthetic biology inspired designs. Spectroscopy studies not only detected slow dark recovery kinetics, but also established the modular nature of both sensor and effector domains of ToAureo1. First, to check the compatibility of ToAureo1 bZIP with other non-Aureochrome LOV sensors; second, to explore the essentiality of heterogeneous linker connecting effector and sensor domains; and third, to generate temporal scaffolds for optogenetics, chimeric photoreceptor-transcription factor, AsTobZL was generated. Upon blue-light illumination, both the wildtype and synthetic constructs showed a four-fold increase in DNA binding activity. Formation of higher order structures in both cases could be important for transcription activation. Their dark recovery time, however, varied significantly by about ten-fold - further establishing modularity in LOV sensors. Our design further proves the non-essentiality of native ToAureo1 linker between LOV and bZIP in blue-light mediated DNA binding, thereby opening up newer options in optogenetics. Compatibility among different ToAureo-bZIP partners further strengthens their possible role in light-regulated transcription factor interaction networks through large-scale participation.

biochemistry↗

bioRxiv2025

Intelligent Design of Escherichia coli Terminators

Terminators are specific nucleotide sequences located at the 3 end of a gene and contain transcription termination information. As a fundamental genetic regulatory element, terminators play a crucial role in the design of gene circuits. Accurately characterizing terminator strength is essential for improving the precision of gene circuit designs. Experimental characterization of terminator strength is time-consuming and labor-intensive; therefore, there is a need to develop computational tools capable of accurately predicting terminator strength. Current prediction methods do not fully consider sequence or thermodynamic information related to terminators, lacking robust models for accurate prediction. Meanwhile, deep generative models have demonstrated tremendous potential in the design of biological sequences and are expected to be applied to terminator sequence design. This study focuses on intelligent design of Escherichia coli terminators and primarily conducts the following research: (1) To construct an intrinsic terminator strength prediction model for E. coli, this study extracts sequence features and thermodynamic features from E. coli intrinsic terminators. Machine learning models based on the selected features achieved a prediction performance of R2 = 0.72. (2) This study employs a generative adversarial network (GAN) to learn from intrinsic terminator sequence training data and generate terminator sequences. Evaluation reveals that the generated terminators exhibit similar data distributions to intrinsic terminators, demonstrating the reliability of GAN-generated terminator sequences. (3) This study uses the constructed terminator strength prediction model to screen for strong terminators from the generated set. Experimental verification shows that among the 18 selected terminators, 72% exhibit termination efficiencies greater than 90%, confirming the reliability of the intelligent design approach for E. coli terminators. In sum, this study constructs a terminator strength prediction model and a terminator generation model for E. coli, providing model support for terminator design in gene circuits. This enhances the modularity of biological component design and promotes the development of synthetic biology.

synthetic biology↗

bioRxiv2026

A Transformer-based Multi-omics Model for Translation Efficiency in S. cerevisiae

Precise regulation of protein synthesis is fundamental to cellular homeostasis and remains a primary target for synthetic biology applications. However, the non-linear relationship between mRNA abundance and protein levels presents complexities that poses challenges for predictive engineering. Here, we present TRIM, a Transformer-based RNA Inference Model that leverages full-length mRNA sequences and multi-omics data to predict translation efficiency. By employing a Parallel Expert Mixer, TRIM achieves robust prediction accuracy (R2 [≥] 0.8,Pearson r [≥] 0.9). Trained on multimodal data from massive Saccharomyces cerevisiae isolates, TRIM demonstrates outstanding biological interpretability, helping to decipher complex translational patterns such as synergistic effects between bases, sequence-dependent codon preference in different stages, and distinct attention on key secondary structures. These results indicate that the integration of multi-omics data with holistic sequence modeling can effectively decode the cis-regulatory grammar of translation as well as providing a scalable and interpretable generative framework for future synthetic biology engineering. Availability and ImplementationThe source code and data used to produce the results and analyses presented in the manuscript are available from Github (https://github.com/ZeusLiu666/TRIM).

Molecular Biology↗

bioRxiv2024

Compliant DNA Origami Nanoactuators as Size-Selective Nanopores

Biological nanopores crucially control the import and export of biomolecules across lipid membranes in cells. They have found widespread use in biophysics and biotechnology, where their typically narrow, fixed diameters enable selective transport of ions and small molecules as well as DNA and peptides for sequencing applications. Yet, due to their small channel sizes, they preclude the passage of large macromolecules, e.g., therapeutics. Here, we harness the unique combined properties of DNA origami nanotechnology, machine-inspired design, and synthetic biology, to present a structurally reconfigurable DNA origami MechanoPore (MP) that features a lumen that is tuneable in size through molecular triggers. Controllable switching of MPs between three stable states is confirmed by 3D-DNA-PAINT super-resolution imaging and through dye-influx assays, after reconstitution of the large MPs in the membrane of liposomes via an inverted-emulsion cDICE technique. Confocal imaging of transmembrane transport shows size-selective behaviour with adjustable thresholds. Importantly, the conformational changes are fully reversible, attesting to the robust mechanical switching that overcomes pressure from the surrounding lipid molecules. These MPs advance nanopore technology, offering functional nanostructures that can be tuned on-demand - thereby impacting fields as diverse as drug-delivery, biomolecule sorting and sensing, as well as bottom-up synthetic biology.

biophysics↗

bioRxiv2024

A Linear Mixed Effects Model for Evaluating Synthetic Gene Circuits

A significant advancement in synthetic biology is the development of synthetic gene circuits with predictive Boolean logic. However, there is no universally accepted or applied statistical test to analyze the performance of these circuits. Many basic statistical tests fail to capture the predicted logic (OR, AND, etc.) and most studies neglect statistical analysis entirely. As synthetic gene circuits shift toward advanced applications, primarily in computing, biosensing, and human health, it is critical to standardize the statistical methods used to evaluate gate success. Here, we propose the application of a linear mixed effects model to analyze and quantify genetic Boolean logic gate performance. First, we analyzed 144 currently published Boolean logic gates for trends and used unsupervised machine learning (k-means clustering) to validate the statistical model. Next, we utilized the model to generate estimates for the fixed effect of the ON state, {beta}, as a general descriptor of the Boolean nature of a circuit and used Monte Carlo simulations to recommend sample sizes for evaluating gate performance. Finally, we examined {beta} as a holistic metric for circuit performance using a series of nested repressor OR gates with intentionally degraded performance. We observed a linear correlation between {beta} and the predicted translation rate, highlighting the use of {beta} for the forward design of new Boolean gates. In summary, we utilized a linear mixed effects model to describe synthetic gene circuits and determined that the fixed effect, {beta}, is an appropriate descriptor of gate behavior that can be used to statistically evaluate performance. Significance statementThere is no standard method for statistically evaluating the success of biological Boolean logic gates and common statistical tests are not used or incorrectly applied. We propose the use of a linear mixed effects model to estimate the value for {beta}, a parameter which describes the difference between ON and OFF gate states. A null hypothesis of {beta} = 0 can then be tested to determine if there is a statistically significant difference between the two states.

synthetic biology↗

bioRxiv2024

Membrane fusion-based drug delivery liposomes transiently modify the material properties of synthetic and biological membranes

Many drug targets are located in intracellular compartments of cells but they often remain inaccessible to standard imaging and therapeutic agents. To aid intracellular delivery, drug carrier nanoparticles have been used to overcome the barrier imposed by the plasma membrane. The carrier must entrap large amounts of cargo, efficiently and quickly deliver the cargo in the cytosol or other intracellular compartments, and must be as inert as possible. In other words, they should not induce cellular responses or alter the cell state in the course of delivery. Here, we show that cationic liposomes with high charge density efficiently fuse with synthetic membranes and the plasma membrane of living cells. Direct fusion efficiently delivers large amounts of cargo to cells and cell-like vesicles within seconds, bypassing slow and often inefficient internalization-based pathways. These effects depend on liposome charge density and, to some extent, liposome concentration and the helper lipid. However, fusion-mediated cargo delivery results in the incorporation of large amounts of foreign lipids that leads to changes in the material properties of these membranes, namely modifications in membrane packing and fluidity, induction of membrane curvature, decrease in surface tension and the formation of (short-lived) pores. Importantly, these effects are transient and liposome removal allows cells to recover their state prior to liposome interaction.

biophysics↗

bioRxiv2025

Engineering signal peptide-mediated recombinant protein secretion in Komagataeibacter rhaeticus for biological Engineered Living Material applications

BackgroundBacterial nanocellulose (BC), produced by Komagataeibacter species, is an ideal scaffold for biological Engineered Living Materials (bioELMs) research. Current BC functionalization strategies often rely on secondary microbial hosts or post-production enzyme immobilization, limiting the scalability and modularity required for programmable bioELMs. Establishing a single-chassis system capable of simultaneous biopolymer synthesis and in situ functionalization remains a primary objective in bioELM research. This study addresses the need by benchmarking signal peptide-mediated protein translocation in K. rhaeticus iGEM, a model bacterium for BC-based bioELMs, enabling a synthetic biology framework for single-chassis based biomaterial functionalization. ResultsGenome-wide analysis confirmed the presence of a complete Sec translocation machinery in K. rhaeticus. Through liquid chromatography-tandem mass spectrometry and SignalP 5.0 prediction, native signal peptides were identified and evaluated alongside previously characterized heterologous signal peptides using {beta}-lactamase and mScarlet as cargo proteins. Protein translocation was found to depend on signal peptide identity, cargo type, and expression mode. Fluorescence imaging revealed cytoplasmic, polar, and peripheral localization patterns, confirming functional engagement with the native translocation machinery. A key limitation identified was the retention of recombinant proteins within the periplasm, restricting extracellular availability. Despite this, signal peptide-mediated translocation enabled the incorporation of enzymatic activity into BC during biosynthesis. A post-growth osmotic shock-release strategy increased measurable enzymatic activity by 30%, demonstrating a practical route to overcome this physiological bottleneck while maintaining the biomaterial production capacity. ConclusionsThis study benchmarks signal peptide-dependent protein translocation in K. rhaeticus and identifies periplasmic retention as a key constraint for extracellular protein release. By linking protein translocation to in situ BC functionalization, this work establishes a synthetic biology framework that supports the development of K. rhaeticus as a single-chassis platform for the scalable production of functionalized bioELMs.

bioengineering↗