An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models

Transcriptional regulation of living materials via extracellular electron transfer

Engineered living materials combine the advantages of biological and synthetic systems by leveraging genetic and metabolic programming to control material-wide properties. Here, we demonstrate that extracellular electron transfer (EET), a microbial respiration process, can serve as a tunable bridge between live cell metabolism and synthetic material properties. In this system, EET flux from Shewanella oneidensis to a copper catalyst controls hydrogel cross-linking via two distinct chemistries to form living synthetic polymer networks. We first demonstrate that synthetic biology-inspired design rules derived from fluorescence parameterization can be applied toward EET-based regulation of polymer network mechanics. We then program transcriptional Boolean logic gates to govern EET gene expression, which enables design of computational polymer networks that mechanically respond to combinations of molecular inputs. Finally, we control fibroblast morphology using EET as a bridge for programmed material properties. Our results demonstrate how rational genetic circuit design can emulate physiological behavior in engineered living materials.

Rational design of untranslated regions to enhance gene expression

How to improve gene expression by optimizing mRNA structures is a crucial question for various medical and biotechnological applications. Previous efforts focus largely on investigation of the 5' UTR hairpin structures. In this study, we present a rational strategy that enhances mRNA stability and translation by engineering both the 5' and 3' UTR sequences. We have successfully demonstrated this strategy using green fluorescent protein (GFP) as a model in Escherichia coli and across different expression vectors. We further validated it with luciferase and Plasmodium falciparum lactate dehydrogenase (PfLDH). To elucidate the underlying mechanism, we have quantitatively analyzed both protein, mRNA levels and half-life time. We have identified several key aspects of UTRs that significantly influence mRNA stability and protein expression in our system: (1) The optimal length of the single-stranded spacer between the stabilizer hairpin and ribosome binding site (RBS) in the 5' UTR is 25-30 nucleotide (nt) long. An optimal 32% GC content in the spacer yielded the highest levels of GFP protein production. (2) The insertion of a homodimerdizable, G-quadruplex structure containing RNA aptamer, "Corn", in the 3' UTR markedly increased the protein expression. Our findings indicated that the carefully engineered 5' UTR and 3' UTR significantly boosted gene expression. Specifically, the inclusion of 5×Corn in the 3' UTR appeared to facilitate the local aggregation of mRNA, leading to the formation of mRNA condensates. Aside from shedding light on the regulation of mRNA stability and expression, this study is expected to substantially increase biological protein production.

CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria

Foundational techniques in molecular biology-such as cloning genes, tagging biomolecules for purification or identification, and overexpressing recombinant proteins-rely on introducing non-native or synthetic DNA sequences into organisms. These sequences may be recognized by the transcription and translation machinery in their new context in unintended ways. The cryptic gene expression that sometimes results has been shown to produce genetic instability and mask experimental signals. Computational tools have been developed to predict individual types of gene expression elements, but it can be difficult for researchers to contextualize their collective output. Here, we introduce CryptKeeper, a software pipeline that visualizes predictions of bacterial gene expression signals and estimates the translational burden possible from a DNA sequence. We investigate several published examples where cryptic gene expression in E. coli interfered with experiments. CryptKeeper accurately postdicts unwanted gene expression from both eukaryotic virus infectious clones and individual proteins that led to genetic instability. It also identifies off-target gene expression elements that resulted in truncations that confounded protein purification. Incorporating negative design using CryptKeeper into reverse genetics and synthetic biology workflows can help to mitigate cloning challenges and avoid unexplained failures and complications that arise from unintentional gene expression.

"Top-down" overexpression optimization of butelase-1 in Escherichia coli and its application in anti-tumor peptides

Butelase-1, the fastest known Asn/Asp-specific peptide ligase capable of catalyzing peptide ligation and cyclization, holds promising application prospects in the fields of food and biology. However, limited research exists on its recombinant expression and potential applications in peptide drugs. In this study, the activity of recombinantly-produced butelase-1 was enhanced by co-expressing it with a molecular chaperone in the SHuffle T7 strain. By introducing single or multiple synonymous rare codons at the beginning of the coding regions of beta-strand or alpha-helix, in combination with ribosomal binding site engineering, the activity of butelase-1 could be further improved. Consequently, the butelase-1 with a specific activity of 386.93 U/mg and a catalytic efficiency of 11,048 M-1 s-1 was successfully prepared in E. coli, resulting in a total activity of 8183.54 U/L and a yield of about 100 mg/L. This optimized butelase-1 was then used to efficiently cyclize the redesigned anti-cancer peptide lunasin, leading to enhanced bioavailability and anti-cancer effects. Overall, this study not only provided valuable biotechnology strategies for improving the recombinant expression of butelase-1 but also demonstrated a successful application for enhancing the biological efficacy of anti-cancer peptides.

Establishing a High-Yield Chloroplast Cell-Free System for Prototyping Genetic Parts

Plastid engineering offers the potential to carry multigene traits in plants; however, it requires reliable genetic parts to balance expression. The difficulty of chloroplast transformation and slow plant growth makes it challenging to build plants just to characterize genetic parts. To address these limitations, we developed a high-yield cell-free system from Nicotiana tabacum chloroplast extracts for prototyping genetic parts. Our cell-free system uses combined transcription and translation driven by T7 RNA polymerase and works with plasmid or linear template DNA. To develop our system, we optimized lysis, extract preparation procedures (e.g., runoff reaction, centrifugation, and dialysis), and the physiochemical reaction conditions. Our cell-free system can synthesize 34 ± 1 μg/mL luciferase in batch reactions and 60 ± 4 μg/mL in semicontinuous reactions. We apply our batch reaction system to test a library of 103 ribosome binding site (RBS) variants and rank them based on cell-free gene expression. We observe a 1300-fold dynamic range of luciferase expression when normalized by maximum mRNA expression, as assessed by the malachite green aptamer. We also find that the observed normalized gene expression in chloroplast extracts and the predictions made by the RBS Calculator are correlated. We anticipate that chloroplast cell-free systems will increase the speed and reliability of building genetic systems in plant chloroplasts for diverse applications.

Heterologous Biosynthesis of Methanobactin from Methylocystis sp. Strain SB2 in Methylosinus trichosporium OB3b

Aerobic methanotrophs, or methane-consuming microbes, are strongly dependent on copper for their activity. To satisfy this requirement, some methanotrophs produce a copper-binding compound, or chalkophore, called methanobactin (MB). In addition to playing a critical role in methanotrophy, MB has also been shown to have great promise in treating copper-related human diseases, perhaps most significantly Wilson's disease. In this congenital disorder, copper builds up in the liver, leading to irreversible damage and, in severe cases, complete organ failure. Remarkably, MB has been shown to reverse such damage in animal models, and there is a great deal of interest in upscaling MB production for expanded clinical trials. Such efforts, however, are currently hampered as (1) the natural rate of MB production rate by methanotrophs is low, (2) the use of methane as a substrate for MB production is problematic as it is explosive in air, (3) there is limited understanding of the entire pathway of MB biosynthesis, and (4) the most attractive form of MB is produced by Methylocystis sp. strain SB2, a methanotroph that is genetically intractable. Herein, we report heterologous biosynthesis of MB from Methylocystis sp. strain SB2 in an alternative methanotroph, Methylosinus trichosporium OB3b, not only on methane but also on methanol. As a result, the strategy described herein not only facilitates enhanced MB production but also provides opportunities to construct various mutants to delineate the entire pathway of MB biosynthesis, as well as the creation of modified forms of MB that may have enhanced therapeutic value.

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Escape mutants that alleviate this burden can rapidly evolve and take over cell populations, making genetic engineering less reliable and predictable. Synthetic biologists often use genetic parts encoded on plasmids, but their burden is rarely characterized. We measured how 301 BioBrick plasmids affected Escherichia coli growth and found that 59 (19.6%) were burdensome, primarily because they depleted the limited gene expression resources of host cells. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be unclonable. We made this model available online for education ( https://barricklab.org/burden-model ) and added our burden measurements to the iGEM Registry. Our results establish a fundamental limit on what DNA constructs and genetic modifications can be successfully engineered into cells.

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Evolution and engineering of pathways for aromatic O-demethylation in Pseudomonas putida KT2440

Biological conversion of lignin from biomass offers a promising strategy for sustainable production of fuels and chemicals. However, aromatic compounds derived from lignin commonly contain methoxy groups, and O-demethylation of these substrates is often a rate-limiting reaction that influences catabolic efficiency. Several enzyme families catalyze aromatic O-demethylation, but they are rarely compared in vivo to determine an optimal biocatalytic strategy. Here, two pathways for aromatic O-demethylation were compared in Pseudomonas putida KT2440. The native Rieske non-heme iron monooxygenase (VanAB) and, separately, a heterologous tetrahydrofolate-dependent demethylase (LigM) were constitutively expressed in P. putida, and the strains were optimized via adaptive laboratory evolution (ALE) with vanillate as a model substrate. All evolved strains displayed improved growth phenotypes, with the evolved strains harboring the native VanAB pathway exhibiting growth rates ∼1.8x faster than those harboring the heterologous LigM pathway. Enzyme kinetics and transcriptomics studies investigated the contribution of selected mutations toward enhanced utilization of vanillate. The VanAB-overexpressing strains contained the most impactful mutations, including those in VanB, the reductase for vanillate O-demethylase, PP_3494, a global regulator of vanillate catabolism, and fghA, involved in formaldehyde detoxification. These three mutations were combined into a single strain, which exhibited approximately 5x faster vanillate consumption than the wild-type strain in the first 8 h of cultivation. Overall, this study illuminates the details of vanillate catabolism in the context of two distinct enzymatic mechanisms, yielding a platform strain for efficient O-demethylation of lignin-related aromatic compounds to value-added products.

Bacteroides expand the functional versatility of a universal transcription factor and transcribed DNA to program capsule diversity

Human gut Bacteroides species encode numerous (eight or more) tightly regulated capsular polysaccharides (CPS). Specialized paralogs of the universal transcription elongation factor NusG, called UpxY (Y), and an anti-Y UpxZ (Z) are encoded by the first two genes of each CPS operon. The Y-Z regulators combine with promoter inversions to limit CPS transcription to a single operon in most cells. Y enhances transcript elongation whereas Z inhibits noncognate Ys. How Y distinguishes among cognate CPS operons and how Z inhibits only noncognate Ys are unknown. Using in-vivo nascent-RNA sequencing and promoter-less in vitro transcription (PIVoT), we establish that Y recognizes a paused RNA polymerase via sequences in both the exposed non-template DNA and the upstream duplex DNA. Y association is aided by novel 'pause-then-escape' nascent RNA hairpins. Z binds non-cognate Ys to directly inhibit Y association. This Y-Z hierarchical regulatory program allows Bacteroides to create CPS subpopulations for optimal fitness.

Synthetic genomes unveil the effects of synonymous recoding

Engineering the genetic code of an organism provides the basis for (i) making any organism safely resistant to natural viruses and (ii) preventing genetic information flow into and out of genetically modified organisms while (iii) allowing the biosynthesis of genetically encoded unnatural polymers1-4. Achieving these three goals requires the reassignment of multiple of the 64 codons nature uses to encode proteins. However, synonymous codon replacement-recoding-is frequently lethal, and how recoding impacts fitness remains poorly explored. Here, we explore these effects using whole-genome synthesis, multiplexed directed evolution, and genome-transcriptome-translatome-proteome co-profiling on multiple recoded genomes. Using this information, we assemble a synthetic Escherichia coli genome in seven sections using only 57 codons to encode proteins. By discovering the rules responsible for the lethality of synonymous recoding and developing a data-driven multi-omics-based genome construction workflow that troubleshoots synthetic genomes, we overcome the lethal effects of 62,007 synonymous codon swaps and 11,108 additional genomic edits. We show that synonymous recoding induces transcriptional noise including new antisense RNAs, leading to drastic transcriptome and proteome perturbation. As the elimination of select codons from an organism's genetic code results in the widespread appearance of cryptic promoters, we show that synonymous codon choice may naturally evolve to minimize transcriptional noise. Our work provides the first genome-scale description of how synonymous codon changes influence organismal fitness and paves the way for the construction of functional genomes that provide genetic firewalls from natural ecosystems and safely produce biopolymers, drugs, and enzymes with an expanded chemistry.

A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution

Monitoring changes of signaling molecules and metabolites with high temporal resolution is key to understanding dynamic biological systems. Here, we use directed evolution to develop a genetically encoded ratiometric biosensor for c-di-GMP, a ubiquitous bacterial second messenger regulating important biological processes like motility, surface attachment, virulence and persistence. The resulting biosensor, cdGreen2, faithfully tracks c-di-GMP in single cells and with high temporal resolution over extended imaging times, making it possible to resolve regulatory networks driving bimodal developmental programs in different bacterial model organisms. We further adopt cdGreen2 as a simple tool for in vitro studies, facilitating high-throughput screens for compounds interfering with c-di-GMP signaling and biofilm formation. The sensitivity and versatility of cdGreen2 could help reveal c-di-GMP dynamics in a broad range of microorganisms with high temporal resolution. Its design principles could also serve as a blueprint for the development of similar, orthogonal biosensors for other signaling molecules, metabolites and antibiotics.

Genome engineering of the human gut microbiome

The human gut microbiome, a complex ecosystem, significantly influences host health, impacting crucial aspects such as metabolism and immunity. To enhance our comprehension and control of the molecular mechanisms orchestrating the intricate interplay between gut commensal bacteria and human health, the exploration of genome engineering for gut microbes is a promising frontier. Nevertheless, the complexities and diversities inherent in the gut microbiome pose substantial challenges to the development of effective genome engineering tools for human gut microbes. In this comprehensive review, we provide an overview of the current progress and challenges in genome engineering of human gut commensal bacteria, whether executed in vitro or in situ. A specific focus is directed towards the advancements and prospects in cargo DNA delivery and high-throughput techniques. Additionally, we elucidate the immense potential of genome engineering methods to enhance our understanding of the human gut microbiome and engineer the microorganisms to enhance human health.

Modifications of the 5' region of the CASPONTM tag's mRNA further enhance soluble recombinant protein production in Escherichia coli

BACKGROUND

Escherichia coli is one of the most commonly used host organisms for the production of biopharmaceuticals, as it allows for cost-efficient and fast recombinant protein expression. However, challenging proteins are often produced with low titres or as inclusion bodies, and the manufacturing process needs to be developed individually for each protein. Recently, we developed the CASPONTM technology, a generic fusion tag-based platform process for high-titer soluble expression including a standardized downstream processing and highly specific enzymatic cleavage of the fusion tag. To assess potential strategies for further improvement of the N-terminally fused CASPONTM tag, we modified the 5'UTR and 5' region of the tag-coding mRNA to optimize the ribosome-mRNA interactions.

RESULTS

In the present work, we found that by modifying the 5'UTR sequence of a pET30acer plasmid-based system, expression of the fusion protein CASPONTM-tumour necrosis factor α was altered in laboratory-scale carbon-limited fed-batch cultivations, but no significant increase in expression titre was achieved. Translation efficiency was highest for a construct carrying an expression enhancer element and additionally possessing a very favourable interaction energy between ribosome and mRNA (∆Gtotal). However, a construct with comparatively low transcriptional efficiency, which lacked the expression enhancer sequence and carried the most favourable ∆Gtotal tested, led to the highest recombinant protein formation alongside the reference pET30a construct. Furthermore, we found, that by introducing synonymous mutations within the nucleotide sequence of the T7AC element of the CASPONTM tag, utilizing a combination of rare and non-rare codons, the free folding energy of the nucleotides at the 5' end (-4 to + 37) of the transcript encoding the CASPONTM tag increased by 6 kcal/mol. Surprisingly, this new T7ACrare variant led to improved recombinant protein titres by 1.3-fold up to 5.3-fold, shown with three industry-relevant proteins in lab-scale carbon limited fed-batch fermentations under industrially relevant conditions.

CONCLUSIONS

This study reveals some of the complex interdependencies between the ribosome and mRNA that govern recombinant protein expression. By modifying the 5'UTR to obtain an optimized interaction energy between the mRNA and the ribosome (ΔGtotal), transcript levels were changed, highlighting the different translation efficiencies of individual transcripts. It was shown that the highest recombinant titre was not obtained by the construct with the most efficient translation but by a construct with a generally high transcript amount coupled with a favourable ΔGtotal. Furthermore, an unexpectedly high potential to enhance expression by introducing silent mutations including multiple rare codons into the 5'end of the CAPONTM tag's mRNA was identified. Although the titres of the fusion proteins were dramatically increased, no formation of inclusion bodies or negative impact on cell growth was observed. We hypothesize that the drastic increase in titre is most likely caused by better ribosomal binding site accessibility. Our study, which demonstrates the influence of changes in ribosome-mRNA interactions on protein expression under industrially relevant production conditions, opens the door to the applicability of the new T7ACrare tag in biopharmaceutical industry using the CASPONTM platform process.

Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal

Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient synthesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements.

Rediversification following ecotype isolation reveals hidden adaptive potential

Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about whether communities can regenerate ecological diversity following ecotype removal or extinction and how the rediversified communities would compare to the original ones. Here, we show that simple two-ecotype communities from the E. coli long-term evolution experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different from the original community in ways relevant to the mechanism of ecotype coexistence-for example, in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, although the rediversified community showed unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species where there are even more potential niches, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.

Standardized Iterative Genome Editing Method for Escherichia coli Based on CRISPR-Cas9

The introduction of complex biosynthetic pathways into the hosts' chromosomes is gaining attention with the development of synthetic biology. While CRISPR-Cas9 has been widely employed for gene knock-in, the process of multigene insertion remains cumbersome due to laborious and empirical gene cloning procedures. To address this, we devised a standardized iterative genome editing system for Escherichia coli, harnessing the power of CRISPR-Cas9 and MetClo assembly. This comprehensive toolkit comprises two fundamental elements based on the Golden Gate standard for modular assembly of sgRNA or CRISPR arrays and donor DNAs. We achieved a gene insertion efficiency of up to 100%, targeting a single locus. Expression of tracrRNA using a strong promoter enhances multiplex genomic insertion efficiency to 7.3%, compared with 0.76% when a native promoter is used. To demonstrate the robust capabilities of this genome editing toolbox, we successfully integrated 5-10 genes from the coenzyme B12 biosynthetic pathway ranging from 5.3 to 8 Kb in length into the chromosome of E. coli chassis cells, resulting in 14 antibiotic-free, plasmid-free producers. Following an extensive screening process involving genes from diverse sources, cistronic design modifications, and chromosome repositioning, we obtained a recombinant strain yielding 1.49 mg L-1 coenzyme B12, the highest known titer achieved by using E. coli as the producer. Illuminating its user-friendliness, this genome editing system is an exceedingly versatile tool for expediently integrating complex biosynthetic pathway genes into hosts' genomes, thus facilitating pathway optimization for chemical production.

Degron-Controlled Protein Degradation in Escherichia coli: New Approaches and Parameters

Protein degron tags have proven to be uniquely useful for the characterization of gene function. Degrons can mediate quick depletion, usually within minutes, of a protein of interest, allowing researchers to characterize cellular responses to the loss of function. To develop a general-purpose degron tool in Escherichia coli, we sought to build upon a previously characterized system of SspB-dependent inducible protein degradation. For this, we created a family of expression vectors containing a destabilized allele of SspB, capable of a rapid and nearly perfect "off-to-on" induction response. Using this system, we demonstrated excellent control over several DNA metabolism enzymes. However, other substrates did not respond to degron tagging in such an ideal manner, indicating the apparent limitations of SspB-dependent systems. Several degron-tagged proteins were degraded too slowly to be completely depleted during active growth, whereas others appeared to be completely refractory to degron-promoted degradation. Thus, only a minority of our, admittedly biased, selection of degron substrates proved to be amenable to efficient SspB-catalyzed degradation. We also uncovered an apparent stalling and/or disengagement of ClpXP from a degron-tagged allele of beta-galactosidase (beta-gal). While a degron-containing fusion peptide attached to the carboxy-terminus of beta-gal was degraded quantitatively, no reductions in beta-gal activity or concentration were detected, demonstrating an apparently novel mechanism of protease resistance. We conclude that substrate-dependent effects of the SspB system present a continued challenge to the widespread adoption of this degron system. For substrates that prove to be degradable, we provide a series of titratable SspB-expression vehicles.

Pre-optimization and one-step preparation of cascade enzymes system with broad substrates by model guidance: Application of chiral L-norvaline and L-phenylglycine biosynthesis

Cascade biocatalyst systems with catalytic promiscuity can be used for synthesis of a class of chiral chemicals but the optimization of these systems by model guidance is poorly explored. In this study, a cascade system with broad substrate spectrum was characterized and simulated by kinetic model with substrates of DL-Norvaline (DL-Nor) and DL-Phenylglycine (DL-Phg) as examples. To evaluate the optimal cascade system, maximum accumulation of intermediate products and conversion rate in the process were investigated by simultaneous solution of the rate equations for varying enzyme quantities. According to the simulation results, the cascade system was optimized by regulating the expression of D-amino acid oxidase and formate dehydrogenase and was prepared by one-step. The conversion efficiency of DL-Nor and DL-Phg have been significantly improved compared with that of before optimization. Moreover, the total of L-Nor and L-Phg were reached 498.2 mM and 79.5 mM through a gradient fed-batch conversion strategy, respectively.

Intermediate product control in cascade reaction for one-pot production of ε-caprolactone by Escherichia coli

ε-Caprolactone is an important non-toxic compound for polymer synthesis like polycaprolactone which has been widely used in drug delivery and degradable plastics. To meet the demand for a green economy, a bi-enzymatic cascade, consisting of an alcohol dehydrogenase (ADH) and a cyclohexanone monooxygenase (CHMO), was designed and introduced into Escherichia coli to synthesize ε-caprolactone from cyclohexanol with a self-sufficient NADPH-cofactor regeneration system. To further improve the catalytic efficiency, a carbonyl group-dependent colorimetric method using inexpensive 2,4-dinitrophenylhydrazine (DNPH) was developed for assay of cyclohexanone, an intermediate production of cascade reaction. It can be used to screen mutant strains with high catalytic efficiency from high-throughput library by detecting the absorbance value in microtiter plates (MTP) instead of gas chromatography (GC) analysis. Moreover, an RBS combinatorial library was constructed for balancing the expression of ADH and CHMO from two independent transcriptional units. After the high-throughput screening based on intermediate product control, an optimal variant with higher substrate tolerance and long-term stability was obtained from RBS combinatorial library. Through a fed-batch process, ε-caprolactone production reached 148.2 mM after 70 h of reaction under the optimized conditions, which was the highest yield achieved to date.

Lipid membrane remodeling and metabolic response during isobutanol and ethanol exposure in Zymomonas mobilis

BACKGROUND

Recent engineering efforts have targeted the ethanologenic bacterium Zymomonas mobilis for isobutanol production. However, significant hurdles remain due this organism's vulnerability to isobutanol toxicity, adversely affecting its growth and productivity. The limited understanding of the physiological impacts of isobutanol on Z. mobilis constrains our ability to overcome these production barriers.

RESULTS

We utilized a systems-level approach comprising LC-MS/MS-based lipidomics, metabolomics, and shotgun proteomics, to investigate how exposure to ethanol and isobutanol impact the lipid membrane composition and overall physiology of Z. mobilis. Our analysis revealed significant and distinct alterations in membrane phospholipid and fatty acid composition resulting from ethanol and isobutanol exposure. Notably, ethanol exposure increased membrane cyclopropane fatty acid content and expression of cyclopropane fatty acid (CFA) synthase. Surprisingly, isobutanol decreased cyclopropane fatty acid content despite robust upregulation of CFA synthase. Overexpression of the native Z. mobilis' CFA synthase increased cyclopropane fatty acid content in all phospholipid classes and was associated with a significant improvement in growth rates in the presence of added ethanol and isobutanol. Heterologous expression of CFA synthase from Clostridium acetobutylicum resulted in a near complete replacement of unsaturated fatty acids with cyclopropane fatty acids, affecting all lipid classes. However, this did not translate to improved growth rates under isobutanol exposure. Correlating with its greater susceptibility to isobutanol, Z. mobilis exhibited more pronounced alterations in its proteome, metabolome, and overall cell morphology-including cell swelling and formation of intracellular protein aggregates -when exposed to isobutanol compared to ethanol. Isobutanol triggered a broad stress response marked by the upregulation of heat shock proteins, efflux transporters, DNA repair systems, and the downregulation of cell motility proteins. Isobutanol also elicited widespread dysregulation of Z. mobilis' primary metabolism evidenced by increased levels of nucleotide degradation intermediates and the depletion of biosynthetic and glycolytic intermediates.

CONCLUSIONS

This study provides a comprehensive, systems-level evaluation of the impact of ethanol and isobutanol exposure on the lipid membrane composition and overall physiology of Z. mobilis. These findings will guide engineering of Z. mobilis towards the creation of isobutanol-tolerant strains that can serve as robust platforms for the industrial production of isobutanol from lignocellulosic sugars.

Toolbox of Characterized Genetic Parts for Staphylococcus aureus

Staphylococcus aureus is an important clinical bacterium prevalent in human-associated microbiomes and the cause of many diseases. However, S. aureus has been intractable to synthetic biology approaches due to limited characterized genetic parts for this nonmodel Gram-positive bacterium. Moreover, genetic manipulation of S. aureus has relied on cumbersome and inefficient cloning strategies. Here, we report the first standardized genetic parts toolbox for S. aureus, which includes characterized promoters, ribosome binding sites, terminators, and plasmid replicons from a variety of bacteria for precise control of gene expression. We established a standard relative expression unit (REU) for S. aureus using a plasmid reference and characterized genetic parts in standardized REUs using S. aureus ATCC 12600. We constructed promoter and terminator part plasmids that are compatible with an efficient Type IIS DNA assembly strategy to effectively build multipart DNA constructs. A library of 24 constitutive promoters was built and characterized in S. aureus, which showed a 380-fold activity range. This promoter library was also assayed in Bacillus subtilis (122-fold activity range) to demonstrate the transferability of the constitutive promoters between these Gram-positive bacteria. By applying an iterative design-build-test-learn cycle, we demonstrated the use of our toolbox for the rational design and engineering of a tetracycline sensor in S. aureus using the PXyl-TetO aTc-inducible promoter that achieved 25.8-fold induction. This toolbox greatly expands the growing number of genetic parts for Gram-positive bacteria and will allow researchers to leverage synthetic biology approaches to study and engineer cellular processes in S. aureus.

Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators

A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.

Using design of experiments to guide genetic optimization of engineered metabolic pathways

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

ONE-SENTENCE SUMMARY

This is a review of literature related to applying Design of Experiments for genetic optimization.

Chemical Reaction Models in Synthetic Promoter Design in Bacteria

We discuss the formalism of chemical reaction networks (CRNs) as a computer-aided design interface for using formal methods in engineering living technologies. We set out by reviewing formal methods within a broader view of synthetic biology. Based on published results, we illustrate, step by step, how mathematical and computational techniques on CRNs can be used to study the structural and dynamic properties of the designed systems. As a case study, we use an E. coli two-component system that relays the external inorganic phosphate concentration signal to genetic components. We show how CRN models can scan and explore phenotypic regimes of synthetic promoters with varying detection thresholds, thereby providing a means for fine-tuning the promoter strength to match the specification.

Design of an effective small expression tag to enhance GPCR production in E. coli-based cell-free and whole cell expression systems

G protein-coupled receptors (GPCRs) play crucial roles in sensory, immune, and tumor metastasis processes, making them valuable targets for pharmacological and sensing applications in various industries. However, most GPCRs have low production yields in Escherichia coli (E. coli) expression systems. To overcome this limitation, we introduced AT10 tag, an effective fusion tag that could significantly enhance expression levels of various GPCRs in E. coli and its derived cell-free protein synthesis (CFPS) system. This AT10 tag consisted of an A/T-rich gene sequence designed via optimization of translation initiation rate. It is translated into a short peptide sequence of 10 amino acids at the N-terminus of GPCRs. Additionally, effector proteins could be utilized to suppress cytotoxicity caused by membrane protein expression, further boosting GPCR production in E. coli. Enhanced expression of various GPCRs using this AT10 tag is a promising approach for large-scale production of functional GPCRs in E. coli-based CFPS and whole cell systems, enabling their potential utilization across a wide range of industrial applications.

Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide

The acquisition of antimicrobial resistance (AR) genes has rendered important pathogens nearly or fully unresponsive to antibiotics. It has been suggested that pathogens acquire AR traits from the gut microbiota, which collectively serve as a global reservoir for AR genes conferring resistance to all classes of antibiotics. However, only a subset of AR genes confers resistance to clinically relevant antibiotics, and, although these AR gene profiles are well-characterized for common pathogens, less is known about their taxonomic associations and transfer potential within diverse members of the gut microbiota. We examined a collection of 14,850 human metagenomes and 1666 environmental metagenomes from 33 countries, in addition to nearly 600,000 isolate genomes, to gain insight into the global prevalence and taxonomic range of clinically relevant AR genes. We find that several of the most concerning AR genes, such as those encoding the cephalosporinase CTX-M and carbapenemases KPC, IMP, NDM, and VIM, remain taxonomically restricted to Proteobacteria. Even cfiA, the most common carbapenemase gene within the human gut microbiome, remains tightly restricted to Bacteroides, despite being found on a mobilizable plasmid. We confirmed these findings in gut microbiome samples from India, Honduras, Pakistan, and Vietnam, using a high-sensitivity single-cell fusion PCR approach. Focusing on a set of genes encoding carbapenemases and cephalosporinases, thus far restricted to Bacteroides species, we find that few mutations are required for efficacy in a different phylum, raising the question of why these genes have not spread more widely. Overall, these data suggest that globally prevalent, clinically relevant AR genes have not yet established themselves across diverse commensal gut microbiota.

Degron-controlled protein degradation in Escherichia coli: New Approaches and Parameters

Protein degron tags have proven uniquely useful for characterization of gene function. Degrons mediate quick depletion, usually within minutes, of a protein of interest - allowing researchers to characterize cellular responses to the loss of function. To develop a general purpose degron tool in E. coli, we sought to build upon a previously characterized system of SspB-dependent inducible protein degradation. For this, we created a family of expression vectors containing a destabilized allele of SspB, capable of a rapid and nearly perfect "off-to-on" induction response. Using this system, we demonstrated control over several enzymes of DNA metabolism, but also found with other substates apparent limitations of a SspB-dependent system. Several degron target proteins were degraded too slowly to affect their complete depletion during active growth, whereas others appeared completely refractory to degron-promoted degradation. We demonstrated that a model substrate, beta-galactosidase, was positively recognized as a degron substrate, but failed to be degraded by the ClpXP protease - demonstrating an apparently unknown mechanism of protease resistance. Thus, only a minority of our, admittedly biased, selection of degron substates proved amenable to rapid SspB-catalyzed degradation. We conclude that substrate-dependence of the SspB system remains a critical factor for the success of this degron system. For substrates that prove degradable, we provide a series of titratable SspB-expression vehicles.

High-Level Production of Lacto-N-neotetraose in Escherichia coli by Stepwise Optimization of the Biosynthetic Pathway

Lacto-N-neotetraose (LNnT), an abundant human milk oligosaccharide (HMO), has been approved as a novel functional additive for infant formulas. Therefore, LNnT biosynthesis has attracted extensive attention. Here, a high LNnT-producing, low lacto-N-triose II (LNT II)-residue Escherichia coli strain was constructed. First, an initial LNnT-producing chassis strain was constructed by blocking lactose, UDP-N-acetylglucosamine, and UDP-galactose competitive consumption pathways and introducing β-1,3-N-acetylglucosaminyltransferase LgtA and β-1,4-galactosyltransferase LgtB. Subsequently, the supply of LNnT precursors was increased by enhancing UDP-N-acetylglucosamine and UDP-galactose synthesis, inactivating LNT II extracellular transporter SetA, and improving UTP synthesis. Then, modular engineering strategy was used to optimize LNnT biosynthetic pathway fluxes. Moreover, pathway fluxes were fine-tuned by modulating translation initiation strength of essential genes lgtB, prs, and lacY. Finally, LNnT production reached 6.70 g/L in a shake flask and 19.40 g/L in a 3 L bioreactor with 0.47 g/(L h) productivity, with 1.79 g/L LNT II residue, highest productivity level, and lowest LNT II residue thus far.

Development of an aminotransferase-driven biocatalytic cascade for deracemization of d,l-phosphinothricin

2-oxo-4-[(hydroxy)(methyl)phosphinoyl]butyric acid (PPO) is the essential precursor keto acid for the asymmetric biosynthesis of herbicide l-phosphinothricin (l-PPT). Developing a biocatalytic cascade for PPO production with high efficiency and low cost is highly desired. Herein, a d-amino acid aminotransferase from Bacillus sp. YM-1 (Ym DAAT) with high activity (48.95 U/mg) and affinity (Km  = 27.49 mM) toward d-PPT was evaluated. To circumvent the inhibition of by-product d-glutamate (d-Glu), an amino acceptor (α-ketoglutarate) regeneration cascade was constructed as a recombinant Escherichia coli (E. coli D), by coupling Ym d-AAT, d-aspartate oxidase from Thermomyces dupontii (TdDDO) and catalase from Geobacillus sp. CHB1. Moreover, the regulation of the ribosome binding site was employed to overcome the limiting step of expression toxic protein TdDDO in E. coli BL21(DE3). The aminotransferase-driven whole-cell biocatalytic cascade (E. coli D) showed superior catalytic efficiency for the synthesis of PPO from d,l-phosphinothricin (d,l-PPT). It revealed the production of PPO exhibited high space-time yield (2.59 g L-1  h-1 ) with complete conversion of d-PPT to PPO at high substrate concentration (600 mM d,l-PPT) in 1.5 L reaction system. This study first provides the synthesis of PPO from d,l-PPT employing an aminotransferase-driven biocatalytic cascade.

Protein engineering and iterative multimodule optimization for vitamin B6 production in Escherichia coli

Vitamin B6 is an essential nutrient with extensive applications in the medicine, food, animal feed, and cosmetics industries. Pyridoxine (PN), the most common commercial form of vitamin B6, is currently chemically synthesized using expensive and toxic chemicals. However, the low catalytic efficiencies of natural enzymes and the tight regulation of the metabolic pathway have hindered PN production by the microbial fermentation process. Here, we report an engineered Escherichia coli strain for PN production. Parallel pathway engineering is performed to decouple PN production and cell growth. Further, protein engineering is rationally designed including the inefficient enzymes PdxA, PdxJ, and the initial enzymes Epd and Dxs. By the iterative multimodule optimization strategy, the final strain produces 1.4 g/L of PN with productivity of 29.16 mg/L/h by fed-batch fermentation. The strategies reported here will be useful for developing microbial strains for the production of vitamins and other bioproducts having inherently low metabolic fluxes.

Next generation synthetic memory via intercepting recombinase function

Here we present a technology to facilitate synthetic memory in a living system via repurposing Transcriptional Programming (i.e., our decision-making technology) parts, to regulate (intercept) recombinase function post-translation. We show that interception synthetic memory can facilitate programmable loss-of-function via site-specific deletion, programmable gain-of-function by way of site-specific inversion, and synthetic memory operations with nested Boolean logical operations. We can expand interception synthetic memory capacity more than 5-fold for a single recombinase, with reconfiguration specificity for multiple sites in parallel. Interception synthetic memory is ~10-times faster than previous generations of recombinase-based memory. We posit that the faster recombination speed of our next-generation memory technology is due to the post-translational regulation of recombinase function. This iteration of synthetic memory is complementary to decision-making via Transcriptional Programming - thus can be used to develop intelligent synthetic biological systems for myriad applications.

Periplasmic stress contributes to a trade-off between protein secretion and cell growth in Escherichia coli Nissle 1917

Maximizing protein secretion is an important target in the design of engineered living systems. In this paper, we characterize a trade-off between cell growth and per-cell protein secretion in the curli biofilm secretion system of Escherichia coli Nissle 1917. Initial characterization using 24-h continuous growth and protein production monitoring confirms decreased growth rates at high induction, leading to a local maximum in total protein production at intermediate induction. Propidium iodide (PI) staining at the endpoint indicates that cellular death is a dominant cause of growth reduction. Assaying variants with combinatorial constructs of inner and outer membrane secretion tags, we find that diminished growth at high production is specific to secretory variants associated with periplasmic stress mediated by outer membrane secretion and periplasmic accumulation of protein containing the outer membrane transport tag. RNA sequencing experiments indicate upregulation of known periplasmic stress response genes in the highly secreting variant, further implicating periplasmic stress in the growth-secretion trade-off. Overall, these results motivate additional strategies for optimizing total protein production and longevity of secretory engineered living systems Graphical Abstract.

Genetic Engineering of Resident Bacteria in the Gut Microbiome

Techniques by which to genetically manipulate members of the microbiota enable both the evaluation of host-microbe interactions and an avenue by which to monitor and modulate human physiology. Genetic engineering applications have traditionally focused on model gut residents, such as Escherichia coli and lactic acid bacteria. However, emerging efforts by which to develop synthetic biology toolsets for "nonmodel" resident gut microbes could provide an improved foundation for microbiome engineering. As genome engineering tools come online, so too have novel applications for engineered gut microbes. Engineered resident gut bacteria facilitate investigations of the roles of microbes and their metabolites on host health and allow for potential live microbial biotherapeutics. Due to the rapid pace of discovery in this burgeoning field, this minireview highlights advancements in the genetic engineering of all resident gut microbes.

Quintuply orthogonal pyrrolysyl-tRNA synthetase/tRNAPyl pairs

Mutually orthogonal aminoacyl transfer RNA synthetase/transfer RNA pairs provide a foundation for encoding non-canonical amino acids into proteins, and encoded non-canonical polymer and macrocycle synthesis. Here we discover quintuply orthogonal pyrrolysyl-tRNA synthetase (PylRS)/pyrrolysyl-tRNA (tRNAPyl) pairs. We discover empirical sequence identity thresholds for mutual orthogonality and use these for agglomerative clustering of PylRS and tRNAPyl sequences; this defines numerous sequence clusters, spanning five classes of PylRS/tRNAPyl pairs (the existing classes +N, A and B, and newly defined classes C and S). Most of the PylRS clusters belong to classes that were unexplored for orthogonal pair generation. By testing pairs from distinct clusters and classes, and pyrrolysyl-tRNAs with unusual structures, we resolve 80% of the pairwise specificities required to make quintuply orthogonal PylRS/tRNAPyl pairs; we control the remaining specificities by engineering and directed evolution. Overall, we create 924 mutually orthogonal PylRS/tRNAPyl pairs, 1,324 triply orthogonal pairs, 128 quadruply orthogonal pairs and 8 quintuply orthogonal pairs. These advances may provide a key foundation for encoded polymer synthesis.

Engineered nickel bioaccumulation in Escherichia coli by NikABCDE transporter and metallothionein overexpression

Mine wastewater often contains dissolved metals at concentrations too low to be economically extracted by existing technologies, yet too high for environmental discharge. The most common treatment is chemical precipitation of the dissolved metals using limestone and subsequent disposal of the sludge in tailing impoundments. While it is a cost-effective solution to meet regulatory standards, it represents a lost opportunity. In this study, we engineered Escherichia coli to overexpress its native NikABCDE transporter and a heterologous metallothionein to capture nickel at concentrations in local effluent streams. We found the engineered strain had a 7-fold improvement in the bioaccumulation performance for nickel compared to controls, but also observed a drastic decrease in cell viability due to metabolic burden or inducer (IPTG) toxicity. Growth kinetic analysis revealed the IPTG concentrations used based on past studies lead to growth inhibition, thus delineating future avenues for optimization of the engineered strain and its growth conditions to perform in more complex environments.

The current role and evolution of X-ray crystallography in drug discovery and development

INTRODUCTION

Macromolecular X-ray crystallography and cryo-EM are currently the primary techniques used to determine the three-dimensional structures of proteins, nucleic acids, and viruses. Structural information has been critical to drug discovery and structural bioinformatics. The integration of artificial intelligence (AI) into X-ray crystallography has shown great promise in automating and accelerating the analysis of complex structural data, further improving the efficiency and accuracy of structure determination.

AREAS COVERED

This review explores the relationship between X-ray crystallography and other modern structural determination methods. It examines the integration of data acquired from diverse biochemical and biophysical techniques with those derived from structural biology. Additionally, the paper offers insights into the influence of AI on X-ray crystallography, emphasizing how integrating AI with experimental approaches can revolutionize our comprehension of biological processes and interactions.

EXPERT OPINION

Investing in science is crucially emphasized due to its significant role in drug discovery and advancements in healthcare. X-ray crystallography remains an essential source of structural biology data for drug discovery. Recent advances in biochemical, spectroscopic, and bioinformatic methods, along with the integration of AI techniques, hold the potential to revolutionize drug discovery when effectively combined with robust data management practices.

Characterizing a Propionate Sensor in E. coli Nissle 1917

Short-chain fatty acids (SCFAs) are commonly found in the large intestine, but generally not in the small intestine, and influence microbiome composition and host physiology. Thus, synthetic biologists are interested in developing engineered probiotics capable of in situ detection of SCFAs as biogeography or disease sensors. One SCFA, propionate, is both sensed and consumed by E. coli. Here, we utilize the E. coli transcription factor PrpR, sensitive to the propionate-derived metabolite (2S,3S)-2-methylcitrate, and its cognate promoter PprpBCDE to detect extracellular propionate with the probiotic chassis bacterium E. coli Nissle 1917. We identify that PrpR-PprpBCDE displays stationary phase leakiness and transient bimodality, and we explain these observations through evolutionary rationales and deterministic modeling, respectively. Our results will help researchers build biogeographically sensitive genetic circuits.

Interrogating the Function of Bicistronic Translational Control Elements to Improve Consistency of Gene Expression

Context independent gene expression is required for genetic circuits to maintain consistent and predicable behavior. Previous efforts to develop context independent translation have leveraged the helicase activity of translating ribosomes via bicistronic design translational control elements (BCDs) located within an efficiently translated leader peptide. We have developed a series of bicistronic translational control elements with strengths that span several orders of magnitude, maintain consistent expression levels across diverse sequence contexts, and are agnostic to common ligation sequences used in modular cloning systems. We have used this series of BCDs to investigate several features of this design, including the spacing of the start and stop codons, the nucleotide identity upstream of the start codon, and factors affecting translation of the leader peptide. To demonstrate the flexibility of this architecture and their value as a generic modular expression control cassette for synthetic biology, we have developed a set of robust BCDs for use in several Rhodococcus species.

Genetic circuitry boosts cell longevity

Reprogramming cellular dynamics is used to study and delay the onset of aging in yeast.

Automated design of protein-binding riboswitches for sensing human biomarkers in a cell-free expression system

Cell-free genetically encoded biosensors have been developed to detect small molecules and nucleic acids, but they have yet to be reliably engineered to detect proteins. Here we develop an automated platform to convert protein-binding RNA aptamers into riboswitch sensors that operate within low-cost cell-free assays. We demonstrate the platform by engineering 35 protein-sensing riboswitches for human monomeric C-reactive protein, human interleukin-32γ, and phage MS2 coat protein. The riboswitch sensors regulate output expression levels by up to 16-fold with input protein concentrations within the human serum range. We identify two distinct mechanisms governing riboswitch-mediated regulation of translation rates and leverage computational analysis to refine the protein-binding aptamer regions, improving design accuracy. Overall, we expand the cell-free sensor toolbox and demonstrate how computational design is used to develop protein-sensing riboswitches with future applications as low-cost medical diagnostics.

Protocol to develop a synthetic biology toolkit for the non-model bacterium R. palustris

Numerous biology tools are developed to work for model organisms, which, however, do not work effectively in non-model organisms. Here, we present a protocol for developing a synthetic biology toolkit for Rhodopseudomonas palustris CGA009, a non-model bacterium with unique metabolic properties. We describe steps for introducing and characterizing biological devices in non-model bacteria, such as the utilization of fluorescence markers and RT-qPCR. This protocol may also be applicable for other non-model organisms. For complete details on the use and execution of this protocol, please refer to Immethun et al..¹.

New Aequorea Fluorescent Proteins for Cell-Free Bioengineering

Recently, a new subset of fluorescent proteins has been identified from the Aequorea species of jellyfish. These fluorescent proteins were characterized in vivo; however, there has not been validation of these proteins within cell-free systems. Cell-free systems and technology development is a rapidly expanding field, encompassing foundational research, synthetic cells, bioengineering, biomanufacturing, and drug development. Cell-free systems rely heavily on fluorescent proteins as reporters. Here we characterize and validate this new set of Aequorea proteins for use in a variety of cell-free and synthetic cell expression platforms.

Metabolic engineering of Escherichia coli for high-level production of free lipoic acid

L-Lipoic acid (LA) is an important antioxidant with various industrial applications as a nutraceutical and therapeutic. Currently, LA is produced by chemical synthesis. Cell factory development is complex as LA and its direct precursors only occur naturally in protein-bound forms. Here we report a rationally engineered LA cell factory and demonstrate de novo free LA production from glucose for the first time in E. coli. The pathway represents a significant challenge as the three key enzymes, native Octanoyltransferase (LipB) and Lipoyl Synthase (LipA), and heterologous Lipoamidase (LpA), are all toxic to overexpress in E. coli. To overcome the toxicity of LipB, functional metagenomic selection was used to identify a highly active and non-toxic LipB and LipA from S. liquefaciens. Using high throughput screening, we balanced translation initiation rates and dual, orthogonal induction systems for the toxic genes, LipA and LpA. The optimized strain yielded 2.5 mg free LA per gram of glucose in minimal media, expressing carefully balanced LipB and LipA, Enterococcus faecalis LpA, and a truncated, native, Dihydrolipoyllysine-residue acetyltransferase (AceF) lipoylation domain. When the optimized cell factory strain was cultivated in a fed-batch fermentation, a titer of 87 mg/L free LA in the supernatant was reached after 48 h. This titer is ∼3000-fold higher than previously reported free LA titer and ∼8-fold higher than the previous best total, protein-bound LA titer. The strategies presented here could be helpful in designing, constructing and balancing biosynthetic pathways that harbor toxic enzymes with protein-bound intermediates or products.

Recombinant Protein Expression Chassis Library of Vibrio natriegens by Fine-Tuning the Expression of T7 RNA Polymerase

Vibrio natriegens is the fastest-growing bacteria, and its doubling time is less than 10 min. At present, the T7 expression system has been introduced into V. natriegens for heterologous protein expression, including the commercial strain V_max¹ and the variant VnDX,² which is a backup expression chassis of Escherichia coli BL21(DE3). However, the strength of the existing T7 expression system is not optimal for every recombinant protein. The different expression strengths of T7 RNA polymerase (T7 RNAP) can be obtained by changing the promoter and ribosome binding site (RBS) sequences of T7 RNAP at different transcription and translation levels. In this work, we obtained a robust VnDX variant library with the fine-tuning T7 RNAP using the industrially used enzyme glucose dehydrogenase (GDH) as the reporter protein. Among this library, the variant VnDX-tet, whose promoter of T7 RNAP was changed from P_lacUV5 to P_tet, showed that the reporter enzyme GDH activity was increased by 109% by the T7 expression system. Similarly, variants with different T7 RNAP translation levels were obtained by changing RBS sequences upstream of T7 RNAP, and the results showed that the variant VnDX-RBS12/pGDH had the highest GDH activity, which increased by 12.6%. The VnDX variant library constructed in this study with different T7 expression strengths provides a choice for expressing various recombinant proteins, greatly expanding the application of V. natriegens.

Improved Bioproduction of the Nylon 12 Monomer by Combining the Directed Evolution of P450 and Enhancing Heme Synthesis

The nylon 12 (PA12) monomer ω-aminododecanoic acid (ω-AmDDA) could be synthesized from lauric acid (DDA) through multi-enzyme cascade transformation using engineered E. coli, with the P450 catalyzing terminal hydroxylation of DDA as a rate-limiting enzyme. Its activity is jointly determined by the heme domain and the reductase domain. To obtain a P450 mutant with higher activity, directed evolution was conducted using a colorimetric high-throughput screening (HTS) system with DDA as the real substrate. After two rounds of directed evolution, a positive double-site mutant (R14R/D629G) with 90.3% higher activity was obtained. Molecular docking analysis, kinetic parameter determination and protein electrophoresis suggested the improved soluble expression of P450 resulting from the synonymous mutation near the N-terminus and the shortened distance of the electron transfer between FMN and FAD caused by D629G mutation as the major reasons for activity improvement. The significantly increased k_cat and unchanged K_m provided further evidence for the increase in electron transfer efficiency. Considering the important role of heme in P450, its supply was strengthened by the metabolic engineering of the heme synthesis pathway. By combining P450-directed evolution and enhancing heme synthesis, 2.02 ± 0.03 g/L of ω-AmDDA was produced from 10 mM DDA, with a yield of 93.6%.

Creating De Novo Overlapped Genes

Future applications of synthetic biology will rely on deploying engineered cells outside of lab environments for long periods of time. Currently, a significant roadblock to this application is the potential for deactivating mutations in engineered genes. A recently developed method to protect engineered coding sequences from mutation is called Constraining Adaptive Mutations using Engineered Overlapping Sequences (CAMEOS). In this chapter we provide a workflow for utilizing CAMEOS to create synthetic overlaps between two genes, one essential (infA) and one non-essential (aroB), to protect the non-essential gene from mutation and loss of protein function. In this workflow we detail the methods to collect large numbers of related protein sequences, produce multiple sequence alignments (MSAs), use the MSAs to generate hidden Markov models and Markov random field models, and finally generate a library of overlapping coding sequences through CAMEOS scripts. To assist practitioners with basic coding skills to try out the CAMEOS method, we have created a virtual machine containing all the required packages already installed that can be downloaded and run locally.

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.