SEARCH · bioRxiv Science

Results for “Synthetic Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 901 records · Page 50Linked to original sources

bioRxiv2023

A hierarchy of metabolite exchanges in metabolic models of microbial species and communities

The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology. Author summaryPathway analysis of constraint-based metabolic models makes it possible to disentangle metabolism into formally defined metabolic pathways. A promising but underexplored application of pathway analysis is to analyze exchanges of metabolites between cells and their environment, which could also help overcome computational challenges and allow scaling to larger systems. Here, we used four different pathway definitions to enumerate combinations of metabolite exchanges that support growth in models of microbial species and a microbial community. We found that metabolite exchanges from different pathway definitions were related to each other through a previously unknown hierarchy, and we show that this hierarchical relationship between pathways holds more generally. Moreover, the fraction of pathways in which each metabolite was exchanged turned out to be remarkably consistent across pathway definitions despite large differences in pathway counts. In summary, our work shows how pathway definitions and their metabolite exchange predictions are related to each other, and it facilitates scalable and interpretable pathway analysis with applications in fields such as metabolic engineering.

systems biology↗

bioRxiv2023

Accelerated design of Escherichia coli genomes with reduced size using a whole-cell model and machine learning surrogate

Whole-cell models (WCMs) are multi-scale computational models that aim to simulate the function of all genes and processes within a cell. This approach is promising for designing genomes tailored for specific tasks. However, a limitation of WCMs is their long runtime. Here, we show how machine learning (ML) surrogates can be used to address this limitation by training them on WCM data to accurately predict cell division. Our ML surrogate achieves a 95% reduction in computational time compared to the original WCM. We then show that the surrogate and a genome-design algorithm can generate an in silico reduced E. coli cell, where 40% of the genes included in the WCM were removed. The reduced genome is validated using the WCM and interpreted biologically using gene ontology analysis. This approach illustrates how the holistic understanding gained from a WCM can be leveraged for synthetic biology tasks while reducing its runtime.

bioengineering↗

bioRxiv2025

A membrane-driven biochemical oscillator tunable by the volume to surface area ratio

Oscillations are ubiquitous features of biological organisms, playing crucial roles in processes from circadian rhythms to developmental patterning. Protein-based biochemical oscillators have particular applications in synthetic biology because they can access fast and slow timescales that are independent from the transcription-translation machinery required of genetic oscillators. Here, we introduce and model such a mass-conserving biochemical oscillator using mass-action reaction kinetics that exploits dynamic changes to membrane phospholipid concentrations to drive proteins on and off the membrane in robust, tunable rhythms. Importantly, the oscillations rely on amplification of reactions on the membrane via dimensional reduction, and they are therefore tunable by variations in the volume-to-surface area ratio (V/A) of the system. With components inspired by the endocytic machinery, we show that a wide range of physiologically relevant biochemical rates can produce oscillations in part due to this independent geometric control. A broad computational screen of the high-dimensional parameter space reveals that oscillations require relatively strict enzyme kinetic design rules for low V/A but much more permissive kinetics for larger V/A. We validate that oscillations persist with more realistic reaction-diffusion simulations that captures explicit diffusion and stochastic, integer valued copy numbers, in overall good agreement with the period and amplitude of the deterministic oscillators. Because the oscillations rely on time-dependent changes to the surface properties and not post-translational modifications to the protein subunits, we demonstrate that it can be coupled to a self-assembling trimer, driving not only changes in localization but trimer yield. Our analysis establishes this membrane-localization oscillator as a new, geometry tunable and programmable timing module and suggests a potential for geometry sensing in engineered or cell-free systems.

biophysics↗

bioRxiv2026

A Comprehensive Epidermal Map from a Poplar Single-Cell Shoot Atlas Reveals New Trichome-Specific Genes

Poplar (Populus spp.) is a model system for tree biology. Specifically, P. tremula x P. alba INRA 717-1B4 (hereafter "poplar 717") has become an important platform for functional genomics and synthetic biology due to its rapid growth and ease of transgenesis. Here, we present a single-cell RNA-seq atlas of the poplar 717 shoot, including apical meristem, primary and secondary stems, and three stages of leaf development. Analysis of ca. 159,000 cells resolved 40 transcriptionally distinct clusters representing 7 major cell types, providing a high-resolution view of shoot development and tissue organization. We focused on the epidermis which constituted >15% of cells in the shoot atlas for in-depth characterization of epidermal heterogeneity. By integrating known marker genes with transcriptomic signatures consistent with established poplar leaf phytochemistry, we annotated epidermal cell subclusters corresponding to developmental stages, spatial location, and specialized cell types, including a distinct population of non-glandular trichomes. Coupling the single-cell RNA-seq atlas with bulk transcriptome data from glabrous mutants enabled the identification of novel trichome markers. Experimental validation of a representative trichome-specific promoter established a tool with potential to support cell type-targeted-metabolic engineering. We provide the poplar 717 atlas to the community through the BioPoplar Atlas Viewer (http://bio-poplar-atlas.com), providing a platform to explore the poplar transcriptome at single-cell resolution and a foundation for data-driven cell type-aware genetic engineering strategies in poplar.

plant biology↗

bioRxiv2022

Systematic Characterization of Double Emulsion Droplets for Biological Applications

Double emulsion droplets (DEs) are water/oil/water droplets that can be sorted via Fluorescence-Activated Cell Sorting (FACS), allowing for new opportunities in high-throughput cellular analysis, enzymatic screening, and synthetic biology. These applications require stable, uniform droplets with predictable microreactor volumes. However, predicting DE droplet size, shell thickness, and stability as a function of flow rate has remained challenging for monodisperse single core droplets and those containing biologically-relevant buffers, which influence bulk and interfacial properties. As a result, developing novel DE-based bioassays has typically required extensive initial optimization of flow rates to find conditions that produce stable droplets of the desired size and shell thickness. To address this challenge, we conducted systematic size parameterization quantifying how differences in flow rates and buffer properties (viscosity and interfacial tension at water/oil interfaces) alter droplet size and stability, across 6 inner aqueous buffers used across applications such as cellular lysis, microbial growth, and drug delivery, quantifying the size and shell thickness of >22,000 droplets overall. We restricted our study to stable single core droplets generated in a 2-step dripping-dripping formation regime in a straightforward PDMS device. Using data from 138 unique conditions (flow rates and buffer composition), we also demonstrated that a recent physically-derived size law of Wang et al1 can accurately predict double emulsion shell thickness for >95% of observations. Finally, we validated the utility of this size law by using it to accurately predict droplet sizes for a novel bioassay that requires encapsulating growth media for bacteria in droplets. This work has the potential to enable new screening-based biological applications by simplifying novel DE bioassay development.

bioengineering↗

bioRxiv2025

Negative design enables cell-free expression and folding of designed transmembrane β-Barrels

De novo design of membrane proteins (MPs) is a rapidly growing field with transformative potential for synthetic biology. Yet, progress has lagged behind that of soluble proteins, largely due to limited understanding of the fundamental principles governing MP folding, stability, and solubility--and their integration into computational models. Here, we use a cell-free expression system to bypass the cytotoxicity of failed or insoluble designs and investigate how sequence features influence the folding of synthetic transmembrane {beta}-barrels (TMBs). We find that even small, idealized TMBs challenge classical protein design workflows: sequences optimized solely for thermodynamic stability misfold and aggregate, preventing membrane insertion. Instead of bulk hydrophobicity, aggregation and {beta}-sheet propensity emerge as key determinants of membrane association. By designing better folding variants of a synthetic TMB, we demonstrate that suppressing aggregation-prone intermediates through local destabilization of {beta}-strands ("negative design") significantly improves folding efficiency. Strikingly, even substitutions typically considered highly destabilizing, such as prolines or polar threonines exposed to the bilayer core, can improve folding when strategically positioned, without significantly compromising thermodynamic stability. Based on these findings, we propose a framework for joint optimization of native stability and folding pathways for future MP and nanopore design. Significance StatementMembrane proteins are essential to biology and biotechnology, yet designing them from scratch remains challenging. Using synthetic transmembrane {beta}-barrels and a cell-free expression system with lipid vesicles, we show that conventional design strategies focused solely on native state stabilization can lead to misfolding and aggregation. By incorporating "negative design" features--specific mutations that locally disrupt {beta}-strand structure--we improve folding efficiency without compromising stability. Remarkably, a protein language model outperformed traditional energy-based methods in predicting these beneficial mutations. Our findings highlight the critical role of folding kinetics in membrane protein design and introduce new principles for engineering synthetic membrane proteins and {beta}-barrel nanopores.

biophysics↗

bioRxiv2025

Vacuum and sonication treatment enables efficient transient gene expression in various monocot and eudicot plant seedlings

Transient gene expression in intact plants is essential for rapidly addressing biological questions, where the current toolkit can be improved for higher efficiencies and broader plant species range. Here, we introduce VAST (Vacuum and Sonication-Assisted Transient expression): a transient transformation method that substantially enhances gene expression efficiency, reproducibility, and versatility across diverse monocot and eudicot intact seedlings. By systematically optimizing plant growth conditions and incorporating vacuum infiltration and sonication pre-treatments prior to seedling co-culture with Agrobacterium tumefaciens, we significantly improved transient gene expression efficiency while minimizing tissue damage compared to existing methods in Arabidopsis thaliana. We further demonstrated the broad applicability of VAST by successfully transforming key model and crop species, including tomato, Brassica rapa, Medicago sativa, Setaria italica (foxtail millet), switchgrass, and wheat. We also demonstrated a case study using the VAST-mediated transient transformation, where a cross-species analyses of nitrate-responsive gene expression highlighted both the conserved and diverged biological responses between the two model crops, A. thaliana and S. italica. VASTs simplicity, versatility, and efficiency provide a powerful tool for functional genomics, synthetic biology, and biotechnology research, opening new avenues for the rapid exploration of gene function, regulation, and editing in diverse plant systems.

plant biology↗

bioRxiv2022

On biological networks capable of robust adaptation in the presence of uncertainties: A systems-theoretic approach

Biological adaptation, the tendency of every living organism to regulate its essential activities in environmental fluctuations, is a well-studied functionality in systems and synthetic biology. In this work, we present a generic methodology inspired by systems theory to discover the design principles for robust adaptation, perfect and imperfect, in two different contexts: (1) in the presence of deterministic external disturbance and (2) in a stochastic setting. In all the cases, firstly, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as design requirements for the underlying networks. Thus, contrary to the existing approaches, the proposed methodologies provide an exhaustive set of admissible network structures without resorting to computationally burdensome brute-force techniques. Further, the proposed frameworks do not assume prior knowledge about the particular rate kinetics, thereby validating the conclusions for a large class of biological networks. In the deterministic setting, we show that unlike the incoherent feed-forward network structures (IFFLP), the modules containing negative feedback with buffer action (NFBLB) are robust to parametric fluctuations when a specific part of the network is assumed to remain unaffected. To this end, we propose a sufficient condition for imperfect adaptation and show that adding negative feedback in an IFFLP topology improves the robustness concerning parametric fluctuations. Further, we propose a stricter set of necessary conditions for imperfect adaptation. Turning to the stochastic scenario, we adopt a Wiener-Kolmogorov filter strategy to tune the parameters of a given network structure towards minimum output variance. We show that both NFBLB and IFFLP can be used as a reduced order W-K filter. Further, we define the notion of nearest neighboring motifs to compare the output variances across different network structures. We argue that the NFBLB achieves adaptation at the cost of a variance higher than its nearest neighboring motifs whereas the IFFLP topology produces locally minimum variance while compared with its nearest neighboring motifs. We present numerical simulations to support the theoretical results. Overall, our results present a generic, systematic, and robust framework for advancing the understanding of complex biological networks.

systems biology↗

bioRxiv2024

Flight-Scope: microscopy with microfluidics in microgravity

With the European Space Agency (ESA) and NASA working to return humans to the moon and onwards to Mars, it has never been more important to study the impact of altered gravity conditions on biological organisms. These include astronauts but also useful micro-organisms they may bring with them to produce food, medicine, and other useful compounds by synthetic biology. Parabolic flights are one of the most accessible microgravity research platforms but present their own challenges: relatively short periods of altered gravity ([~]20s) and aircraft vibration. Live-imaging is necessary in these altered-gravity conditions to readout any real-time phenotypes. Here we present Flight-Scope, a new microscopy and microfluidics platform to study dynamic cellular processes during the short, altered gravity periods on parabolic flights. We demonstrated Flight-Scopes capability by performing live and dynamic imaging of fluorescent glucose uptake by yeast, S. cerevisiae, on board an ESA parabolic flight. Flight-Scope operated well in this challenging environment, opening the way for future microgravity experiments on biological organisms.

biophysics↗

bioRxiv2019

Bound-state diffusion due to binding to flexible polymers in a selective biofilter

Selective biofilters are used by cells to control the transport of proteins, nucleic acids, and other macromolecules. Biological filters demonstrate both high specificity and rapid motion or high flux of proteins. In contrast, high flux comes at the expense of selectivity in many synthetic filters. Binding can lead to selective transport in systems in which the bound particle can diffuse, but the mechanisms that lead to bound diffusion remain unclear. Previous theory has proposed a molecular mechanism of bound-state mobility based only on transient binding to flexible polymers. However, this mechanism has not been directly tested in experiments. We demonstrate that bound mobility via tethered diffusion can be engineered into a synthetic gel using protein fragments derived from the nuclear pore complex. The resulting bound-state diffusion is quantitatively consistent with theory. Our results suggest that synthetic biological filters can be designed to to take advantage of tethered diffusion to give rapid, selective transport.\n\nSIGNIFICANCEBiological filters control the passage of proteins and other macromolecules between compartments of living systems. Determination of molecular mechanisms giving selective transport would enable the design of both selective filters and particles designed to penetrate biological barriers for drug delivery. One such mechanism arises from transient binding to dynamic polymer tethers. We designed a biomaterial which supports this type of tethered diffusion, demonstrating the potential to engineer bio-inspired filters.

biophysics↗

bioRxiv2021

A modular, dynamic, DNA-based platform for regulating cargo distribution and transport between lipid domains

Cell membranes regulate the distribution of biological machinery between phase-separated lipid domains to facilitate key processes including signalling and transport, which are among the life-like functionalities that bottom-up synthetic biology aims to replicate in artificial-cellular systems. Here, we introduce a modular approach to program partitioning of amphiphilic DNA nanostructures in co-existing lipid domains. Exploiting the tendency of different hydrophobic "anchors" to enrich different phases, we modulate the lateral distribution of our devices by rationally combining hydrophobes, and by changing nanostructure size and its topology. We demonstrate the functionality of our strategy with a bio-inspired DNA architecture, which dynamically undergoes ligand-induced reconfiguration to mediate cargo transport between domains via lateral re-distribution. Our findings pave the way to next-generation biomimetic platforms for sensing, transduction, and communication in synthetic cellular systems.

biophysics↗

bioRxiv2016

Multiplex shRNA Screening of Germ Cell Development by in vivo Transfection of Mouse Testis

Spermatozoa are one of the few mammalian cells types that cannot be fully derived in vitro, severely limiting the application of modern genomic techniques to study germ cell biology. The current gold standard approach of characterizing single gene knockout mice is slow as generation of each mutant line can take 6-9 months. Here, we describe an in vivo approach to rapid functional screening of germline genes based on a new non-surgical, non-viral in vivo transfection method to deliver nucleic acids into testicular germ cells. By coupling multiplex transfection of short hairpin RNA constructs with pooled amplicon sequencing as a readout, we were able to screen many genes for spermatogenesis function in a quick and inexpensive experiment. We transfected nine mouse testes with a pilot pool of RNAi against well-characterized genes to show that this system is highly reproducible and accurate. With a false negative rate of 18% and a false positive rate of 12%, this method has similar performance as other RNAi screens in the well-described Drosophila model system. In a separate experiment, we screened 26 uncharacterized genes computationally predicted to be essential for spermatogenesis and found numerous candidates for follow up studies. Further, by characterizing the effect of both libraries on neuronal N2a cells, we show that our screening results from testis are tissue-specific. Our calculations indicate that the current implementation of this approach could be used to screen thousands of protein-coding genes simultaneously in a single mouse testis. The experimental protocols and analysis scripts provided will enable other groups to use this procedure to study diverse aspects of germ cell biology ranging from epigenetics to cell physiology. This approach also has great promise as an applied tool for validating diagnoses made from medical genome sequencing, or designing synthetic biological sequences that act as potent and highly specific male contraceptives.

Genomics↗

bioRxiv2020

Understanding biochemical design principles with ensembles of canonical non-linear models

Systems biology applies concepts from engineering in order to understand biological networks. If such an understanding was complete, biologists would be able to design ad hoc biochemical components tailored for different purposes, which is the goal of synthetic biology. Needless to say that we are far away from creating biological subsystems as intricate and precise as those found in nature, but mathematical models and high throughput techniques have brought us a long way in this direction. One of the difficulties that still needs to be overcome is finding the right values for model parameters and dealing with uncertainty, which is proving to be an extremely difficult task. In this work, we take advantage of ensemble modeling techniques, where a large number of models with different parameter values are formulated and then tested according to some performance criteria. By finding features shared by successful models, the role of different components and the synergies between them can be better understood. We will address some of the difficulties often faced by ensemble modeling approaches, such as the need to sample a space whose size grows exponentially with the number of parameters, and establishing useful selection criteria. Some methods will be shown to reduce the predictions from many models into a set of understandable "design principles" that can guide us to improve or manufacture a biochemical network. Our proposed framework formulates models within standard formalisms in order to integrate information from different sources and minimize the dimension of the parameter space. Additionally, the mathematical properties of the formalism enable a partition of the parameter space into independent subspaces. Each of these subspaces can be paired with a set of criteria that depend exclusively on it, thus allowing a separate sampling/screening in spaces of lower dimension. By applying tests in a strict order where computationally cheaper tests are applied first to each subspace and applying computationally expensive tests to the remaining subset thereafter, the use of resources is optimized and a larger number of models can be examined. This can be compared to a complex database query where the order of the requests can make a huge difference in the processing time. The method will be illustrated by analyzing a classical model of a metabolic pathway with end-product inhibition. Even for such a simple model, the method provides novel insight. Author summaryA method is presented for the discovery of design principles, understood as recurrent solutions to evolutionary problems, in biochemical networks. The method takes advantage of ensemble modeling techniques, where a large number of models with different parameter values are formulated and then tested according to some performance criteria. By finding features shared by successful models, a set of simple rules can be identified that enables us to formulate new models that are known to perform well, a priori. By formulating the models within the framework of Biochemical Systems Theory (BST) we manage to overcome some of the obstacles often faced by ensemble modeling. Further analysis of the selected modeling with standard machine learning techniques enables the formulation of simple rules - design principles - for building good performing networks. We illustrate the method with a well-known case study: the unbranched pathway with end-product inhibition. The method manages to identify the known features of this well-studied pathway while providing additional guidelines on how the pathway kinetics can be tuned to achieve a desired functionality - e.g. demand vs supply control - as well as to identifying important tradeoffs between performance, robustness and and stability.

systems biology↗

bioRxiv2021

Toeholder: a software for automated design and in silico validation of toehold riboswitches

Synthetic biology aims to engineer biological circuits, which often involve gene expression. A particularly promising group of regulatory elements are riboswitches because of their versatility with respect to their targets, but early synthetic designs were not as attractive because of a reduced dynamic range with respect to protein regulators. Only recently, the creation of toehold switches helped overcome this obstacle by also providing an unprecedented degree of orthogonality. However, a lack of automated design and optimization tools prevents the widespread and effective use of toehold switches in high throughput experiments. To address this, we developed Toeholder, a comprehensive open-source software for toehold design and in silico comparison. Toeholder takes into consideration sequence constraints from experimentally tested switches, as well as data derived from molecular dynamics simulations of a toehold switch. We describe the software and its in silico validation results, as well as its potential applications and impacts on the management and design of toehold switches.

bioinformatics↗

bioRxiv2017

Prototyping And Implementation Of A Novel Feedforward Loop In A Cell-Free Transcription-Translation System And Cells

Building novel synthetic biological devices is a time-consuming task because of the lengthy cell-based testing and optimization processes. Recent progress made in the cell-free field suggests that the utilization of mathematical models and cell-free transcription-translation testing platforms to systematically design and test novel synthetic biocircuits may help streamline some of the processes. Here we present a study of building a novel functional biological network motif from scratch with the aid of the mathematical modeling and the cell-free prototyping. In this work, we demonstrated that we were able to make a 3-promoter feedforward circuit from a concept to a working biocircuit in cells within a month. We started with performing simulations with a cell-free transcription-translation simulation toolbox. After verifying the feasibility of the circuit design, we used a fast assembling method to build the constructs and used the linear DNAs directly in the cell-free system for prototyping. After additional tests and assemblies, we implemented the circuit in plasmid forms in cells and showed that the in vivo results were consistent with the simulations and the outcomes in the cell-free platform. This study showed the usefulness of modeling and prototyping in building synthetic biocircuits and that we can use these tools to help streamline the process of circuit optimizations in future studies.

bioengineering↗

bioRxiv2024

A vector system for single and tandem expression of cloned genes and multi-colour fluorescent tagging in Haloferax volcanii

Archaeal cell biology is an emerging field expected to identify fundamental cellular processes, help resolve the deep evolutionary history of cellular life, and contribute new components and functions in biotechnology and synthetic biology. To facilitate these, we have developed plasmid vectors that allow convenient cloning and production of proteins and fusion proteins with flexible, rigid, or semi-rigid linkers in the model archaeon Haloferax volcanii. For protein subcellular localization studies using fluorescent protein (FP) tags, we created vectors incorporating a range of codon-optimized fluorescent proteins for N- or C-terminal tagging, including GFP, mNeonGreen, mCherry, YPet, mTurquoise2 and mScarlet-I. Obtaining functional fusion proteins can be challenging with proteins involved in multiple interactions, mainly due to steric interference. We demonstrated the use of the new vector system to screen for improved function in cytoskeletal protein FP fusions, and identified FtsZ1-FPs that are functional in cell division and CetZ1-FPs that are functional in motility and rod cell development. Both the type of linker and the type of FP influenced the functionality of the resulting fusions. The vector design also facilitates convenient cloning and tandem expression of two genes or fusion genes, controlled by a modified tryptophan-inducible promoter, and we demonstrated its use for dual-colour imaging of tagged proteins in H. volcanii cells. These tools should promote further development and applications of archaeal molecular and cellular biology and biotechnology.

microbiology↗

bioRxiv2025

Investigating The Potential of Division of Labour in Synthetic Bacterial Communities for the Production of Violacein

With advancements in synthetic biology and metabolic engineering, microorganisms can now be engineered to perform increasingly complex functions, which may be limited by the resources available in individual cells. Division of labour in synthetic microbial communities offers a promising approach to enhance metabolic efficiency and resilience in bioproduction. By distributing complex metabolic pathways across multiple subpopulations, the resource competition and metabolic burden imposed on an individual cell is reduced, potentially enabling more efficient production of target compounds. Violacein is a high-value pigment with anti-tumour properties that exemplifies such a challenge due to its complex bioproduction pathway, imposing a significant metabolic burden on host cells. In this study we investigated the benefits of division of labour for violacein production by splitting the violacein bioproduction pathway between two subpopulations of Escherichia coli based synthetic communities. We tested several pathway splitting strategies and reported that splitting the pathway into two subpopulations expressing VioABE and VioDC at a final composition of 60:40 yields a 2.5 fold increase in violacein production as compared to a monoculture. We demonstrated that the coculture outperforms the monoculture when both subpopulations exhibit similar metabolic burden levels, resulting in comparable growth rates, and when both subpopulations are present in sufficiently high proportions.

bioengineering↗

bioRxiv2020

Polycistronic gene expression in the model microorganism Ustilago maydis

AbtractEukaryotic microorganisms transcribe monocistronic mRNAs to encode proteins. For synthetic biological approaches like metabolic engineering, precise co-expression of several proteins in space and time is advantageous. A straightforward approach is the application of viral 2A peptides to design synthetic polycistronic mRNAs in eukaryotes. Here, we establish such a system in the well-studied model microorganism Ustilago maydis. Using two fluorescence reporter proteins, we compared the activity of five viral 2A peptides. Their activity was evaluated in vivo using fluorescence microscopy and validated using fluorescence resonance energy transfer (FRET). Activity ranged from 20 to 100% and the best performing 2A peptide was P2A from porcine teschovirus-1. As proof of principle, we followed regulated gene expression efficiently over time and synthesised a tri-cistronic mRNA encoding biosynthetic enzymes to produce mannosylerythritol lipids (MELs). In essence, we evaluated 2A peptides in vivo and demonstrated the applicability of 2A peptide technology for U. maydis in basic and applied science.

microbiology↗