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Results for “Synthetic Biology”

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bioRxiv2023

The pAblo·pCasso self-curing vector toolset for unconstrained cytidine and adenine base-editing in Pseudomonas species

A synthetic biology toolkit, exploiting clustered regularly interspaced short palindromic repeats (CRISPR) and modified CRISPR-associated protein (Cas) base-editors, was developed for genome engineering in Gram-negative bacteria. Both a cytidine base-editor (CBE) and an adenine base-editor (ABE) have been optimized for precise single-nucleotide modification of plasmid and genome targets. CBE comprises a cytidine deaminase conjugated to a Cas9 nickase from Streptococcus pyogenes (SpnCas9), resulting in C[->]T (or G[->]A) substitutions. Conversely, ABE consists of an adenine deaminase fused to SpnCas9 for A[->]G (or T[->]C) editing. Several nucleotide substitutions were achieved using these plasmid-borne base-editing systems and a novel protospacer adjacent motif (PAM)-relaxed SpnCas9 (SpRY) variant. Base-editing was validated in Pseudomonas putida and other Gram-negative bacteria by inserting premature STOP codons into target genes, thereby inactivating both fluorescent proteins and metabolic (antibiotic-resistance) functions. The functional knockouts obtained by engineering STOP codons via CBE were reverted to the wild-type genotype using ABE. Additionally, a series of induction-responsive vectors was developed to facilitate the curing of the base-editing platform in a single cultivation step, simplifying complex strain engineering programs without relying on homologous recombination and yielding plasmid-free, modified bacterial cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/537106v2_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1ac5e70org.highwire.dtl.DTLVardef@16c0efforg.highwire.dtl.DTLVardef@14caaf4org.highwire.dtl.DTLVardef@6b968f_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2025

Modeling and minimization of dCas9-induced competition in CRISPRi-based genetic circuits

Implementing logic functions in living cells is a fundamental area of interest among synthetic biologists. The goal of designing biochemical circuits in synthetic biology is to make modular and tractable systems that perform well with predictable behaviors. Developing formalisms towards the design of such systems has proven to be difficult with the diverse retroactive effects that appear with respect to the context of the cell. Repressor-based circuits have various applications in biosynthesis, therapeutics, and bioremediation. Particularly using CRISPRi, competition for components of the system (unbound dCas9) can affect the achievable dynamic range of repression. Moreover, the toxicity of dCas9 via non-specific binding inhibits high levels of expression and limits the performance of genetic circuits. In this work, we study the computation of Boolean functions through CRISPRi based circuits built out of NOT and NOR gates. We provide algebraic expressions that allow us to evaluate the steady-state behaviors of any realized circuit. Our mathematical analysis reveals that the effective non-cooperativity of any given gate is a major bottleneck for increasing the dynamic range of the outputs. Further, we find that under the condition of competition between promoters for dCas9, certain circuit architectures perform better than others depending on factors such as circuit depth, fan-in, and fan-out. We pose optimization problems to evaluate the effects engineerable parameter values to find regimes in which a given circuit performs best. This framework provides a mathematical template and computational library for evaluating the performance of repressor-based circuits with a focus on effective cooperativity.

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bioRxiv2024

Semi-automated Production of Cell-free Biosensors

Cell-free synthetic biology biosensors have potential as effective in vitro diagnostic technologies for the detection of chemical compounds such as toxins and human health biomarkers. They have several advantages over conventional laboratory-based diagnostic approaches, including being able to be assembled, freeze-dried, distributed, and then used at the point-of-need. This makes them an attractive platform for cheap and rapid chemical detection across the globe. Though promising, a major challenge is scaling up biosensor manufacturing to meet the needs of their multiple uses. Currently, cell-free biosensor assembly during lab-scale development is mostly performed manually by the operator, leading to quality control and performance variability issues. Here we explore the use of liquid handling robotics to manufacture cell-free biosensor reactions. We compare both manual and semi-automated reaction assembly approaches using the Opentrons OT-2 liquid handling platform on two different cell-free gene expression assay systems that constitutively produce colorimetric (LacZ) or fluorescent (GFP) signals. We test the designed protocol by constructing an entire 384-well plate of fluoride sensing cell-free biosensors and demonstrate that they perform closely to expected detection outcomes.

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bioRxiv2025

Biosensor-Driven Strain Engineering Reveals Key Cellular Processes for Maximizing Isoprenol Production in Pseudomonas putida

Synthetic biology tools have accelerated the generation of simple mutants, but combinatorial testing remains challenging. High-throughput methods struggle translating from proof-of-principle molecules to advanced bioproducts. We address this challenge with a biosensor-driven strategy for enhanced isoprenol production in Pseudomonas putida, a key precursor for sustainable aviation fuel and platform chemicals. This biosensor leverages P. putidas native response to short-chain alcohols via a previously uncharacterized hybrid histidine kinase signaling cascade. Refactoring the biosensor for a conditional growth-based selection enabled identification of competing cellular processes with a [~]16,500-member CRISPRi-library. An iterative combinatorial strain engineering approach yielded an integrated P. putida strain producing [~]900 mg/L isoprenol in glucose minimal medium, a 36-fold increase. Ensemble -omics analysis revealed metabolic rewiring, including amino acid accumulation as key drivers of enhanced production. Techno-economic analysis elucidated the path to economic viability and confirmed the benefits of adding amino acids outweigh the additional costs. This study establishes a robust biosensor driven approach for optimizing other heterologous pathways, accelerating microbial cell factory development. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/643695v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@533605org.highwire.dtl.DTLVardef@ff98fforg.highwire.dtl.DTLVardef@9592fdorg.highwire.dtl.DTLVardef@1e52993_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2024

A Genetic Toggle Switch in Plants

In synthetic biology, genetic components are assembled to make transcriptional units, and transcriptional units are assembled into circuits to perform specific and predictable functions of a genetic device. Genetic devices have been described in bacteria, mammalian cell cultures and small organoids, yet development of programmable genetic circuits for devices in plants has lagged. Programmable genetic devices require defining the components quantitative functions. Because plants have long life spans, studies often use transient analysis to define quantitative functions while verification in stably engineered plants is often neglected and largely unknown. This raises a question if unique attributes of plants such as environmental sensitivity, developmental plasticity, or alternation of generations, adversely impacts predictability of plant genetic circuits and devices. Alternatively, it is also possible that genetic elements to produce predictable genetic devices for plants require rigorous characterization with detailed mathematical modeling. Here we use plant genetic elements with quantitatively characterized transfer functions and developed in silico models to guide their assembly into a genetic device: a toggle switch or a mutually inhibitory gene-regulatory device. Our approach allows computational selection of plant genetic components and iterative refinement of the circuit if the desired genetic functions are not initially achieved. We show that our computationally selected genetic circuit functions as predicted in stably engineered plants including through tissue and organ differentiation. Developing abilities to produce predictable and programmable plant genetic devices opens the prospect of predictably engineering plants unique abilities in sustainable human and environmental systems.

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bioRxiv2020

Repurposing chloramphenicol acetyltransferase for a robust and efficient designer ester biosynthesis platform

Robust and efficient enzymes are essential modules for metabolic engineering and synthetic biology strategies across biological systems to engineer whole-cell biocatalysts. By condensing an acyl-CoA and an alcohol, alcohol acyltransferases (AATs) can serve as an interchangeable metabolic module for microbial biosynthesis of a diverse class of ester molecules with broad applications as flavors, fragrances, solvents, and drop-in biofuels. However, the current lack of robust and efficient AATs significantly limits their compatibility with heterologous precursor pathways and microbial hosts. Through bioprospecting and rational protein engineering, we identified and repurposed chloramphenicol acetyltransferases (CATs) from mesophilic prokaryotes to function as robust and efficient AATs compatible with at least 21 alcohol and 8 acyl-CoA substrates for microbial biosynthesis of linear, branched, saturated, unsaturated and/or aromatic esters. By plugging the best engineered CAT (CATec3 Y20F) into the gram-negative mesophilic bacterium Escherichia coli, we demonstrated that the recombinant strain could effectively convert various alcohols into desirable esters, for instance, achieving a titer of 13.9 g/L isoamyl acetate with 95% conversion by fed-batch fermentation. The recombinant E. coli was also capable of simulating the ester profile of roses with high conversion (> 97%) and titer (> 1 g/L) from fermentable sugars at 37{degrees}C. Likewise, a recombinant gram-positive, cellulolytic, thermophilic bacterium Clostridium thermocellum harboring CATec3 Y20F could produce many of these esters from recalcitrant cellulosic biomass at elevated temperatures (>50{degrees}C) due to the engineered enzymes remarkable thermostability. Overall, the engineered CATs can serve as a robust and efficient platform for designer ester biosynthesis from renewable and sustainable feedstocks.

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bioRxiv2025

A protein-fragment complementation assay to quantify synthetic protein scaffold efficiency

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

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bioRxiv2021

Resolving phylogenetic and biochemical barriers to functional expression of heterologous iron-sulphur cluster enzymes

Many of the most promising applications of synthetic biology, including engineering of microbes for renewable chemical production, relies upon the ability of genetically-tractable hosts to express heterologous enzymes from foreign species. While countless methods for facilitating heterologous enzyme expression have been developed, comparable tools for facilitating heterologous enzyme activity are generally lacking. Such tools are needed to fully exploit the biosynthetic potential of the natural world. Here, using the model bacterium Escherichia coli, we investigate why iron-sulphur (Fe-S) enzymes are often inactive when heterologously expressed. By applying a simple growth complementation assay with collections of Fe-S enzyme orthologs from a wide range of prokaryotic diversity, we uncover a striking correlation between phylogenetic distance and probability of functional expression. Moreover, co-expression of a heterologous Fe-S biogenesis pathway increases the phylogenetic range of orthologs that can be functionally expressed. On the other hand, we find that heterologous Fe-S enzymes that require specific electron carrier proteins within their natural host are rarely functionally expressed unless their specific reducing partners are identified and co-expressed. We demonstrate in vitro that such selectivity in part derives from a need for low-potential electron donors. Our results clarify how phylogenetic distance and electron transfer biochemistry each separately impact functional heterologous expression and provide insight into how these barriers can be overcome for successful microbial engineering involving Fe-S enzymes.

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bioRxiv2023

Improved Production of Taxol(R) Precursors in S. cerevisiae using Combinatorial in silico Design and Metabolic Engineering

Integrated metabolic engineering approaches combining system and synthetic biology tools allow the efficient designing of microbial cell factories to synthesize high-value products. In the present study, in silico design algorithms were used on the latest yeast genome-scale model 8.5.0 to predict potential genomic modifications that could enhance the production of early-step Taxol(R) in previously engineered Saccharomyces cerevisiae cells. The solution set containing genomic modification candidates was narrowed down by employing the COnstraints Based Reconstruction and Analysis (COBRA) methods. 17 genomic modifications consisting of nine gene deletions and eight gene overexpression were screened using wet-lab studies to determine whether these modifications can increase the production yield of taxadiene, the first metabolite in the Taxol(R) through the mevalonate pathway. Depending on the cultivation condition, most of the single genomic modifications resulted in higher taxadiene production. The best-performing strain, named KM32, contained four overexpressed genes, ILV2, TRR1, ADE13 and ECM31, from the branched-chain amino acid biosynthesis, thioredoxin system, de novo purine synthesis, and the pantothenate pathway, respectively. Using KM32, taxadiene production was increased by 50%, reaching 215 mg/L of taxadiene. The engineered strain also produced 43.65 mg/L of taxa-4(20),11-dien-5-ol (T5-ol), and 26.2 mg/L of taxa-4(20),11-dien-5--yl acetate (T5Ac) which are the highest productions of these early-step Taxol(R) metabolites reported until now in S. cerevisiae. The findings of this study highlight that the use of computational and integrated approaches can ensure determining promising modifications that are difficult to estimate intuitively to develop yeast cell factories.

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bioRxiv2021

Biofoundry-assisted expression and characterisation of plant proteins

Many goals in synthetic biology, including the elucidation and refactoring of biosynthetic pathways and the engineering of regulatory circuits and networks, require knowledge of protein function. In plants, the prevalence of large gene families means it can be particularly challenging to link specific functions to individual proteins. However, protein characterisation has remained a technical bottleneck, often requiring significant effort to optimise expression and purification protocols. To leverage the ability of biofoundries to accelerate design-built-test-learn cycles, we present a workflow for automated DNA assembly and cell-free expression of plant proteins that accelerates optimisation and enables rapid progression to characterisation. First, we developed a phytobrick-compatible Golden Gate DNA assembly toolbox containing plasmid acceptors for cell-free expression using E. coli or wheat germ lysates as well as a set of N- and C-terminal tag parts for detection, purification, and improved expression/folding. We next optimised automated assembly of miniaturised cell-free reactions using an acoustic liquid handling platform and then compared tag configurations to identify those that increase expression. We additionally developed a luciferase-based system for rapid quantification that requires a minimal 11 aa tag and demonstrate facile removal of tags following synthesis. Finally, we show that several functional characterisation experiments can be performed with cell-free protein synthesis reactions without the need for protein purification. Together, the combination of automated assembly of DNA parts and cell-free expression reactions should significantly increase the throughput of experiments to test and understand plant protein function and enable the direct reuse of DNA parts in downstream plant engineering workflows.

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bioRxiv2026

Bispecific Coiled-Coil Reporters Enable Rapid, Multiplexed Detection of Mycoplasma via Toehold Switch Sensors

Cell-free synthetic biology offers a rapid prototyping environment for molecular diagnostics, yet the utility of these systems is often limited by the slow kinetics and restricted multiplexing capabilities of traditional protein reporters. Here, we report the BINOCULAR (BIspecific Novel Orthogonal Coiled-coil-Using LAteral flow Reporter) system based on supramolecular coiled-coil peptide conjugates that dramatically accelerates signal generation and enables robust multiplexing in cell-free transcription-translation reactions. Using BINOCULARs as toehold switch outputs, we achieve visible readout times of less than 5 minutes in lateral flow assays, achieving a [≥]12-fold increase in speed compared to standard cell-free reporters like {beta}-galactosidase or fluorescent proteins. To demonstrate the utility of this platform, we integrated the bispecific reporters with loop-mediated isothermal amplification (LAMP) and a lateral flow assay for the detection of mycoplasma, a common adventitious agent in biomanufacturing. The integrated system demonstrates exceptional sensitivity, achieving a detection limit of 1 copy/{micro}L for Mycoplasma 16S rRNA and a mammalian internal control gene. Cell-free assay results are available within 5 minutes following a 20-minute amplification step, and we successfully achieved the simultaneous detection of three distinct targets in a single reaction. Crucially, the platform maintains reliable performance in complex matrices and after lyophilization, making it more accessible for point-of-use need. Our findings suggest that the modular BINOCULAR architecture can overcome current bottlenecks in cell-free sensing systems, providing a versatile and scalable framework for rapid, point-of-use screening across clinical and industrial applications.

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bioRxiv2018

Deconstructing cell-free extract preparation for in vitro activation of transcriptional genetic circuitry

Recent advances in cell-free gene expression (CFE) systems have enabled their use for a host of synthetic biology applications, particularly for rapid prototyping of genetic circuits designed as biosensors. Despite the proliferation of cell-free protein synthesis platforms, the large number of currently existing protocols for making CFE extracts muddles the collective understanding of how the method by which an extract is prepared affects its functionality. Specifically, a key goal toward developing cell-free biosensors based on native genetic regulators is activating the transcriptional machinery present in bacterial extracts for protein synthesis. However, protein yields from genes transcribed in vitro by the native Escherichia coli RNA polymerase are quite low in conventional crude extracts originally optimized for expression by the bacteriophage transcriptional machinery. Here, we show that cell-free expression of genes under bacterial {sigma}70 promoters is constrained by the rate of transcription in crude extracts and that processing the extract with a ribosomal run-off reaction and subsequent dialysis can alleviate this constraint. Surprisingly, these processing steps only enhance protein synthesis in genes under native regulation, indicating that the translation rate is unaffected. We further investigate the role of other common process variants on extract performance and demonstrate that bacterial transcription is inhibited by including glucose in the growth culture, but is unaffected by flash-freezing the cell pellet prior to lysis. Our final streamlined protocol for preparing extract by sonication generates extract that facilitates expression from a diverse set of sensing modalities including protein and RNA regulators. We anticipate that this work will clarify the methodology for generating CFE extracts that are active for biosensing and will encourage the further proliferation of cell-free gene expression technology for new applications.

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bioRxiv2024

Computational pipeline for targeted integration and variable payload expression for bacteriophage engineering.

Bacteriophages offer a promising alternative to conventional antimicrobial treatments, particularly in cases where such treatments have proven ineffective. While naturally occurring phages serve as viable options for phage therapy, advances in synthetic biology and genome engineering enable precise modifications to phages to enhance their therapeutic potential. One such approach is the introduction of antimicrobial genetic payloads into the phage genome. Conventional practice is to integrate such payloads behind genes expressed at very high levels late in the infection cycle, such as the major capsid gene (cps). Nevertheless, phages engineered to contain antimicrobial payloads are often difficult to obtain. For instance, the high expression of toxic payloads can prematurely halt host metabolism, leading to the failure in assembling viable phage progeny. To potentially expand the range of genes viable as genetic payloads, we developed a method to identify intergenic loci with favorable expression levels. We used the machine learning (ML)-based promoter prediction algorithm PhagePromoter to identify these loci. We then used this information to design a computationally-assisted engineering pipeline for the integration of genomic payloads at these locations. We validated our approach experimentally, engineering phages with bioluminescent reporter payloads at various predicted loci. We used the well characterized, strictly lytic, Staphylococcus aureus -infecting bacteriophage, K, as an engineering scaffold and employed homologous recombination to engineer three recombinant phages containing the reporter payload at different predicted loci throughout the genome. The recombinant phages exhibited expression levels consistent with our computational predictions and showed temporal expression patterns corresponding to their genomic locations in early, middle, or late gene clusters. Our study underscores the potential of integrating computational tools with classical sequence analysis to streamline the phage engineering process. This approach not only facilitates the rational design of phages with targeted payload insertions but also paves the way for high-throughput, automated phage engineering, fostering a new era of personalized phage therapy.

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bioRxiv2019

Overcoming the design, build, test (DBT) bottleneck for synthesis of nonrepetitive protein-RNA binding cassettes for RNA applications

The design-build-test (DBT) cycle in synthetic biology is considered to be a major bottleneck for progress in the field. The emergence of high-throughput experimental techniques, such as oligo libraries (OLs), combined with machine learning (ML) algorithms, provide the ingredients for a potential "big-data" solution that can generate a sufficient predictive capability to overcome the DBT bottleneck. In this work, we apply the OL-ML approach to the design of RNA cassettes used in gene editing and RNA tracking systems. RNA cassettes are typically made of repetitive hairpins, therefore hindering their retention, synthesis, and functionality. Here, we carried out a high-throughput OL-based experiment to generate thousands of new binding sites for the phage coat proteins of bacteriophages MS2 (MCP), PP7 (PCP), and Q{beta} (QCP). We then applied a neural network to vastly expand this space of binding sites to millions of additional predicted sites, which allowed us to identify the structural and sequence features that are critical for the binding of each RBP. To verify our approach, we designed new non-repetitive binding site cassettes and tested their functionality in U2OS mammalian cells. We found that all our cassettes exhibited multiple trackable puncta. Additionally, we designed and verified two additional cassettes, the first containing sites that can bind both PCP and QCP, and the second with sites that can bind either MCP or QCP, allowing for an additional orthogonal channel. Consequently, we provide the scientific community with a novel resource for rapidly creating functional non-repetitive binding site cassettes using one or more of three phage coat proteins with a variety of binding affinities for any application spanning bacteria to mammalian cells.

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bioRxiv2021

From sequence to yield: deep learning for protein production systems

Many applications of synthetic biology involve engineering microbial strains to express high-value proteins. Thanks to advances in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain design and optimization. Such models, however, require large amounts of training data that are costly to acquire, which creates substantial entry barriers for many laboratories. Here, we study the relation between model accuracy and data efficiency in a large panel of machine learning models of varied complexity, from penalized linear regressors to deep neural networks. Our analysis is based on data from a large genotype-phenotype screen in Escherichia coli, which was generated with a design-of-experiments approach to balance coverage and depth of the genotypic space. We sampled these data to emulate scenarios with a limited number of DNA sequences for training, as commonly encountered in strain engineering applications. Our results suggest that classic, non-deep, models can achieve good prediction accuracy with much smaller datasets than previously thought, and provide robust evidence that convolutional neural networks further improve performance with the same amount of data. Using methods from Explainable AI and model benchmarking, we show that convolutional neural networks have an improved ability to discriminate between input sequences and extract sequence features that are highly predictive of protein expression. We moreover show that controlled sequence diversity leads to important gains in data efficiency, and validated this principle in a separate genotype-phenotype screen in Saccharomyces cerevisiae. These results provide practitioners with guidelines for designing experimental screens that strike a balance between cost and quality of training data, laying the groundwork for wider adoption of deep learning across the biotechnology sector.

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bioRxiv2019

Tunable integrase-mediated differentiation facilitates improved output of burdensome functions in E. coli

Advances in synthetic biology, bioengineering, and computation allow us to rapidly and reliably program cells with increasingly complex and useful functions. However, because the functions we engineer cells to perform are typically unnecessary for cellular survival and burdensome to cell growth, they can be rapidly lost due to the processes of mutation and natural selection. To improve the evolutionary stability of engineered functions in a general manner, we developed an integrase-recombination-based differentiation gene circuit in Escherichia coli. In this system, differentiated cells uniquely carry out burdensome or toxic engineered functions but have limited capacity to grow (terminal differentiation), preventing the propagation of selectively advantageous loss of function mutations that inevitably arise. To experimentally implement terminal differentiation, we co-opted the R6K plasmid system, using differentiation to simultaneously activate T7 RNAP-driven expression of arbitrary engineered functions, and inactivate expression of {pi} protein (an essential factor for R6K plasmid replication), thereby allowing limitation of differentiated cell growth through antibiotic selection. We experimentally demonstrate terminal differentiation increases both duration and magnitude of high-burden T7 RNAP-driven expression, and that its evolutionary stability can be further improved with strategic redundancy. Using burdensome overexpression of a fluorescent protein as a model engineered function, our terminal differentiation circuit results in a ~2.8-fold (single-cassette) and ~4.2-fold (two-cassette) increase of total fluorescent protein produced compared to high-burden naive expression in which all cells inducibly express T7 RNAP. Finally, we demonstrate that differentiation can enable the expression of even toxic functions, a feat not achieved to our knowledge by any other strategy for addressing long-term evolutionary stability. Overall, this study provides an effective generalizable strategy for protecting engineered functions from evolutionary degradation.

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bioRxiv2020

Directed evolution for improved total secretory protein production in Escherichia coli

Production of secretory protein in Gram-negative bacteria simplifies downstream processing in recombinant protein production, accelerates protein engineering, and advances synthetic biology. Signal peptides and secretory carrier proteins are commonly used to effect the secretion of heterologous recombinant protein in Gram-negative bacteria. The Escherichia coli osmotically-inducible protein Y (OsmY) is a carrier protein that secretes a target protein extracellularly, and we have successfully applied it in the Bacterial Extracellular Protein Secretion System (BENNY) to accelerate the directed evolution workflow. In this study, we applied directed evolution on OsmY to enhance its total secretory protein production. After just one round of directed evolution followed by combining the mutations found, OsmY(M3) (L6P, V43A, S154R, V191E) was identified as the best carrier protein. OsmY(M3) produced 3.1 {+/-} 0.3 fold and 2.9 {+/-} 0.8 fold more secretory Tfu0937 {beta}-glucosidase than its wildtype counterpart in E. coli strains BL21(DE3) and C41(DE3), respectively. OsmY(M3) also produced more secretory Tfu0937 at different cultivation temperatures (37 {degrees}C, 30 {degrees}C and 25 {degrees}C). Subcellular fractionation of the expressed protein confirmed the essential role of OsmY in protein secretion. Up to 80.8 {+/-} 12.2% of total soluble protein was secreted after 15 h of cultivation. When fused to a red fluorescent protein or a lipase from Bacillus subtillis, OsmY(M3) also produced more secretory protein compared to the wildtype. This is the first report of applying directed evolution on a carrier protein to enhance total secretory protein production. The methodology can be further extended to evolve other signal peptides or carrier proteins for secretory protein production in E. coli and other bacteria. In this study, OsmY(M3) improved the production of three proteins, originating from diverse organisms and with diverse properties, in secreted form, clearly demonstrating its wide-ranging applications.

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bioRxiv2021

A Method for Cost-Effective and Rapid Characterization of Genetic Parts

Characterizing and cataloging genetic parts are critical to the design of useful genetic circuits. Having well-characterized parts allows for the fine-tuning of genetic circuits, such that their function results in predictable outcomes. With the growth of synthetic biology as a field, there has been an explosion of genetic circuits that have been implemented in microbes to execute functions pertaining to sensing, metabolic alteration, and cellular computing. Here, we show a cost-effective and rapid method for characterizing genetic parts. Our method utilizes cell-free lysate, prepared in-house, as a medium to evaluate parts via the expression of a reporter protein. Template DNA is prepared by PCR-amplification using inexpensive primers to add variant parts to the reporter gene, and the template is added to the reaction as linear DNA without cloning. Parts that can be added in this way include promoters, operators, ribosome binding sites, insulators, and terminators. This approach, combined with the incorporation of an acoustic liquid handler and 384-well plates, allows the user to carry out high-throughput evaluations of genetic parts in a single day. By comparison, cell-based screening approaches require time-consuming cloning and have longer testing times due to overnight culture and culture density normalization steps. Further, working in cell-free lysate allows the user to exact tighter control over the expression conditions through the addition of exogenous components, or by titrating DNA concentrations rather than relying on limited plasmid copy numbers. Because this method retains a cell-like environment, the function of the genetic part will typically mimic its function in whole cells. SUMMARYWell-characterized genetic parts are necessary for the design of novel genetic circuits. Here we describe a cost-effective, high-throughput method for rapidly characterizing genetic parts. Our method reduces cost and time by combining cell-free lysates, linear DNA to avoid cloning, and acoustic liquid handling to increase throughput and reduce reaction volumes.

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