Application of combinatorial optimization strategies in synthetic biology

Fine-Tuning Genetic Circuits via Host Context and RBS Modulation

The choice of organism to host a genetic circuit, the chassis, is often defaulted to model organisms due to their amenability. The chassis-design space has therefore remained underexplored as an engineering variable. In this work, we explored the design space of a genetic toggle switch through variations in nine ribosome binding site compositions and three host contexts, creating 27 circuit variants. Characterization of performance metrics in terms of toggle switch output and host growth dynamics unveils a spectrum of performance profiles from our circuit library. We find that changes in host context cause large shifts in overall performance, while modulating ribosome binding sites leads to more incremental changes. We find that a combined ribosome binding site and host context modulation approach can be used to fine-tune the properties of a toggle switch according to user-defined specifications, such as toward greater signaling strength, inducer sensitivity, or both. Other auxiliary properties, such as inducer tolerance, are also exclusively accessed through changes in the host context. We demonstrate here that exploration of the chassis-design space can offer significant value, reconceptualizing the chassis organism as an important part in the synthetic biologist's toolbox with important implications for the field of synthetic biology.

Versatile xylose and arabinose genetic switches development for yeasts

Inducible transcription systems are essential tools in genetic engineering, where tight control, strong inducibility and fast response with cost-effective inducers are highly desired. However, existing systems in yeasts are rarely used in large-scale fermentations due to either cost-prohibitive inducers or incompatible performance. Here, we developed powerful xylose and arabinose induction systems in Saccharomyces cerevisiae, utilizing eukaryotic activators XlnR and AraRA from Aspergillus species and bacterial repressors XylR and AraRR. By integrating these signals into a highly-structured synthetic promoter, we created dual-mode systems with strong outputs and minimal leakiness. These systems demonstrated over 4000- and 300-fold regulation with strong activation and rapid response. The dual-mode xylose system was fully activated by xylose-rich agricultural residues like corncob hydrolysate, outperforming existing systems in terms of leakiness, inducibility, dynamic range, induction rate, and growth impact on host. We validated their utility in metabolic engineering with high-titer linalool production and demonstrated the transferability of the XlnR-based xylose induction system to Pichia pastoris, Candida glabrata and Candida albicans. This work provides robust genetic switches for yeasts and a general strategy for integrating activation-repression signals into synthetic promoters to achieve optimal performance.

High-throughput optimisation of protein secretion in yeast via an engineered biosensor

Secretion of high-value proteins and enzymes is fundamental to the synthetic biology economy, allowing continuous fermentation during production and protein purification without cell lysis. Most eukaryotic protein secretion is encoded by an N-terminal signal peptide (SP); however, the strong impact of SP sequence variation on the secretion efficiency of a given protein is not well defined. Despite high natural SP sequence diversity, most recombinant protein secretion systems use only a few well-characterised SPs. Additionally, the selection of promoters and terminators can significantly affect secretion efficiency, yet screening numerous genetic constructs for optimal sequences remains inefficient. Here, we adapted a yeast G-protein-coupled receptor (GPCR) biosensor, to measure the concentration of a peptide tag that is co-secreted with any protein of interest (POI). Thus, protein secretion efficiency can be quantified via induction of a fluorescent reporter that is upregulated downstream of receptor activation. This enabled high-throughput screening of over 6000 combinations of promoters, SPs, and terminators, assembled using one-pot Combinatorial Golden Gate cloning. We demonstrate this biosensor can quickly identify best combinations for secretion and quantify secretion levels. Our results highlight the importance of SP optimisation as an initial step in designing heterologous protein expression strategies, demonstrating the value of high-throughput screening (HTS) approaches for maximising secretion efficiency.

Dual-Plasmid Mini-Tn5 System to Stably Integrate Multicopy of Target Genes in Escherichia coli

The efficiency of valuable metabolite production by engineered microorganisms underscores the importance of stable and controllable gene expression. While plasmid-based methods offer flexibility, integrating genes into host chromosomes can establish stability without selection pressure. However, achieving site-directed multicopy integration presents challenges, including site selection and stability. We introduced a stable multicopy integration method by using a novel dual-plasmid mini-Tn5 system to insert genes into Escherichia coli's genome. The gene of interest was combined with a removable antibiotic resistance gene. After the selection of bacteria with inserted genes, the antibiotic resistance gene was removed. Optimizations yielded an integration efficiency of approximately 5.5 × 10-3 per recipient cell in a single round. Six rounds of integration resulted in 19 and 5 copies of the egfp gene in the RecA+ strain MG1655 and the RecA- strain XL1-Blue MRF', respectively. Additionally, we integrated a polyhydroxybutyrate (PHB) synthesis gene cluster into E. coli MG1655, yielding an 8-copy integration strain producing more PHB than strains with the cluster on a high-copy plasmid. The method was efficient in generating gene insertions in various E. coli strains, and the inserted genes were stable after extended culture. This stable, high-copy integration tool offers potential for diverse applications in synthetic biology.

Engineering Bacterial Chitinases for Industrial Application: From Protein Engineering to Bacterial Strains Mutation! A Comprehensive Review of Physical, Molecular, and Computational Approaches

Bacterial chitinases are integral in breaking down chitin, the natural polymer in crustacean and insect exoskeletons. Their increasing utilization across various sectors such as agriculture, waste management, biotechnology, food processing, and pharmaceutical industries highlights their significance as biocatalysts. The current review investigates various scientific strategies to maximize the efficiency and production of bacterial chitinases for industrial use. Our goal is to optimize the heterologous production process using physical, molecular, and computational tools. Physical methods focus on isolating, purifying, and characterizing chitinases from various sources to ensure optimal conditions for maximum enzyme activity. Molecular techniques involve gene cloning, site-directed mutation, and CRISPR-Cas9 gene editing as an approach for creating chitinases with improved catalytic activity, substrate specificity, and stability. Computational approaches use molecular modeling, docking, and simulation techniques to accurately predict enzyme-substrate interactions and enhance chitinase variants' design. Integrating multidisciplinary strategies enables the development of highly efficient chitinases tailored for specific industrial applications. This review summarizes current knowledge and advances in chitinase engineering to serve as an indispensable guideline for researchers and industrialists seeking to optimize chitinase production for various uses.

Strangers in a foreign land: 'Yeastizing' plant enzymes

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

A deep reinforcement learning algorithm framework for solving multi-objective traveling salesman problem based on feature transformation

As a special type of multi-objective combinatorial optimization problems (MOCOPs), the multi-objective traveling salesman problem (MOTSP) plays an important role in practical fields such as transportation and robot control. However, due to the complexity of its solution space and the conflicts between different objectives, it is difficult to obtain satisfactory solutions in a short time. This paper proposes an end-to-end algorithm framework for solving MOTSP based on deep reinforcement learning (DRL). By decomposing strategies, solving MOTSP is transformed into solving multiple single-objective optimization subproblems. Through linear transformation, the features of the MOTSP are combined with the weights of the objective function. Subsequently, a modified graph pointer network (GPN) model is used to solve the decomposed subproblems. Compared with the previous DRL model, the proposed algorithm can solve all the subproblems using only one model without adding weight information as input features. Furthermore, our algorithm can output a corresponding solution for each weight, which increases the diversity of solutions. In order to verify the performance of our proposed algorithm, it is compared with four classical evolutionary algorithms and two DRL algorithms on several MOTSP instances. The comparison shows that our proposed algorithm outperforms the compared algorithms both in terms of training time and the quality of the resulting solutions.

PEGA: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization

Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement EAs for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solution.

Living Synthelectronics: A New Era for Bioelectronics Powered by Synthetic Biology

Bioelectronics, which converges biology and electronics, has attracted great attention due to their vital applications in human-machine interfaces. While traditional bioelectronic devices utilize nonliving organic and/or inorganic materials to achieve flexibility and stretchability, a biological mismatch is often encountered because human tissues are characterized not only by softness and stretchability but also by biodynamic and adaptive properties. Recently, a notable paradigm shift has emerged in bioelectronics, where living cells, and even viruses, modified via gene editing within synthetic biology, are used as core components in a new hybrid electronics paradigm. These devices are defined as "living synthelectronics," and they offer enhanced potential for interfacing with human tissues at informational and substance exchange levels. In this Perspective, the recent advances in living synthelectronics are summarized. First, opportunities brought to electronics by synthetic biology are briefly introduced. Then, strategic approaches to designing and making electronic devices using living cells/viruses as the building blocks, sensing components, or power sources are reviewed. Finally, the challenges faced by living synthelectronics are raised. It is believed that this paradigm shift will significantly contribute to the real integration of bioelectronics with human tissues.

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.

Ferroelectric compute-in-memory annealer for combinatorial optimization problems

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications. Various digital annealers, dynamical Ising machines, and quantum/photonic systems have been developed for solving COPs, but they still suffer from the memory access issue, scalability, restricted applicability to certain types of COPs, and VLSI-incompatibility, respectively. Here we report a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer for solving larger-scale COPs efficiently. Our CiM annealer converts COPs into quadratic unconstrained binary optimization (QUBO) formulations, and uniquely accelerates in-situ the core vector-matrix-vector (VMV) multiplication operations of QUBO formulations in a single step. Specifically, the three-terminal FeFET structure allows for lossless compression of the stored QUBO matrix, achieving a remarkably 75% chip size saving when solving Max-Cut problems. A multi-epoch simulated annealing (MESA) algorithm is proposed for efficient annealing, achieving up to 27% better solution and ~ 2X speedup than conventional simulated annealing. Experimental validation is performed using the first integrated FeFET chip on 28nm HKMG CMOS technology, indicating great promise of FeFET CiM array in solving general COPs.

Design and Model-Driven Analysis of Synthetic Circuits with the Staphylococcus aureus Dead-Cas9 (sadCas9) as a Programmable Transcriptional Regulator in Bacteria

Synthetic circuit design is crucial for engineering microbes that process environmental cues and provide biologically relevant outputs. To reliably scale-up circuit complexity, the availability of parts toolkits is central. Streptococcus pyogenes (sp)-derived CRISPR interference/dead-Cas9 (CRISPRi/spdCas9) is widely adopted for implementing programmable regulations in synthetic circuits, and alternative CRISPRi systems will further expand our toolkits of orthogonal components. Here, we showcase the potential of CRISPRi using the engineered dCas9 from Staphylococcus aureus (sadCas9), not previously used in bacterial circuits, that is attractive for its low size and high specificity. We designed a collection of ∼20 increasingly complex circuits and variants in Escherichia coli, including circuits with static function like one-/two-input logic gates (NOT, NAND), circuits with dynamic behavior like incoherent feedforward loops (iFFLs), and applied sadCas9 to fix a T7 polymerase-based cascade. Data demonstrated specific and efficient target repression (100-fold) and qualitatively successful functioning for all circuits. Other advantageous features included low sadCas9-borne cell load and orthogonality with spdCas9. However, different circuit variants showed quantitatively unexpected and previously unreported steady-state responses: the dynamic range, switch point, and slope of NOT/NAND gates changed for different output promoters, and a multiphasic behavior was observed in iFFLs, differing from the expected bell-shaped or sigmoidal curves. Model analysis explained the observed curves by complex interplays among components, due to reporter gene-borne cell load and regulator competition. Overall, CRISPRi/sadCas9 successfully expanded the available toolkit for bacterial engineering. Analysis of our circuit collection depicted the impact of generally neglected effects modulating the shape of component dose-response curves, to avoid drawing wrong conclusions on circuit functioning.

Orthogonal LoxPsym sites allow multiplexed site-specific recombination in prokaryotic and eukaryotic hosts

Site-specific recombinases such as the Cre-LoxP system are routinely used for genome engineering in both prokaryotes and eukaryotes. Importantly, recombinases complement the CRISPR-Cas toolbox and provide the additional benefit of high-efficiency DNA editing without generating toxic DNA double-strand breaks, allowing multiple recombination events at the same time. However, only a handful of independent, orthogonal recombination systems are available, limiting their use in more complex applications that require multiple specific recombination events, such as metabolic engineering and genetic circuits. To address this shortcoming, we develop 63 symmetrical LoxP variants and test 1192 pairwise combinations to determine their cross-reactivity and specificity upon Cre activation. Ultimately, we establish a set of 16 orthogonal LoxPsym variants and demonstrate their use for multiplexed genome engineering in both prokaryotes (E. coli) and eukaryotes (S. cerevisiae and Z. mays). Together, this work yields a significant expansion of the Cre-LoxP toolbox for genome editing, metabolic engineering and other controlled recombination events, and provides insights into the Cre-LoxP recombination process.

Applications of synthetic yeast consortia for the production of native and non-native chemicals

The application of microbial consortia is a new approach in synthetic biology. Synthetic yeast consortia, simple or complex synthetic mixed cultures, have been used for the production of various metabolites. Cooperation between the members of a consortium and cross-feeding can be applied to create stable microbial communication. These consortia can: consume a variety of substrates, perform more complex functions, produce metabolites in high titer, rate, and yield (TRY), and show higher stability during industrial fermentations. Due to the new research context of synthetic consortia, few yeasts were used to build these consortia, including Saccharomyces cerevisiae, Pichia pastoris, and Yarrowia lipolytica. Here, application of the yeasts for design of synthetic microbial consortia and their advantages and bottlenecks for effective and robust production of valuable metabolites from bioresource, including: cellulose, xylose, glycerol and so on, have been reviewed. Key trends and challenges are also discussed for the future development of synthetic yeast consortia.

Integration of Yeast Episomal/Integrative Plasmid Causes Genotypic and Phenotypic Diversity and Improved Sesquiterpene Production in Metabolically Engineered Saccharomyces cerevisiae

The variability in phenotypic outcomes among biological replicates in engineered microbial factories presents a captivating mystery. Establishing the association between phenotypic variability and genetic drivers is important to solve this intricate puzzle. We applied a previously developed auxin-inducible depletion of hexokinase 2 as a metabolic engineering strategy for improved nerolidol production in Saccharomyces cerevisiae, and biological replicates exhibit a dichotomy in nerolidol production of either 3.5 or 2.5 g L-1 nerolidol. Harnessing Oxford Nanopore's long-read genomic sequencing, we reveal a potential genetic cause─the chromosome integration of a 2μ sequence-based yeast episomal plasmid, encoding the expression cassettes for nerolidol synthetic enzymes. This finding was reinforced through chromosome integration revalidation, engineering nerolidol and valencene production strains, and generating a diverse pool of yeast clones, each uniquely fingerprinted by gene copy numbers, plasmid integrations, other genomic rearrangements, protein expression levels, growth rate, and target product productivities. Τhe best clone in two strains produced 3.5 g L-1 nerolidol and ∼0.96 g L-1 valencene. Comparable genotypic and phenotypic variations were also generated through the integration of a yeast integrative plasmid lacking 2μ sequences. Our work shows that multiple factors, including plasmid integration status, subchromosomal location, gene copy number, sesquiterpene synthase expression level, and genome rearrangement, together play a complicated determinant role on the productivities of sesquiterpene product. Integration of yeast episomal/integrative plasmids may be used as a versatile method for increasing the diversity and optimizing the efficiency of yeast cell factories, thereby uncovering metabolic control mechanisms.

Improving and Streamlining Gene Editing in Yarrowia lipolytica via Integration of Engineered Cas9 Protein

The oleaginous yeast Yarrowia lipolytica is a prominent subject of biorefinery research due to its exceptional performance in oil production, exogenous protein secretion, and utilization of various inexpensive carbon sources. Many CRISPR/Cas9 genome-editing systems have been developed for Y. lipolytica to meet the high demand for metabolic engineering studies. However, these systems often necessitate an additional outgrowth step to achieve high gene editing efficiency. In this study, we introduced the eSpCas9 protein, derived from the Streptococcus pyogenes Cas9(SpCas9) protein, into the Y. lipolytica genome to enhance gene editing efficiency and fidelity, and subsequently explored the optimal expression level of eSpCas9 gene by utilizing different promoters and selecting various growth periods for yeast transformation. The results demonstrated that the integrated eSpCas9 gene editing system significantly enhanced gene editing efficiency, increasing from 16.61% to 86.09% on TRP1 and from 33.61% to 95.19% on LIP2, all without the need for a time-consuming outgrowth step. Furthermore, growth curves and dilution assays indicated that the consistent expression of eSpCas9 protein slightly suppressed the growth of Y. lipolytica, revealing that strong inducible promoters may be a potential avenue for future research. This work simplifies the gene editing process in Y. lipolytica, thus advancing its potential as a natural product synthesis chassis and providing valuable insights for other comparable microorganisms.

Enabling pathway design by multiplex experimentation and machine learning

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.

Beneficial effect of optimizing the expression balance of the mevalonate pathway introduced into the mitochondria on terpenoid production in Saccharomyces cerevisiae

Terpenoids are used in various industries, and Saccharomyces cerevisiae is a promising microorganism for terpenoid production. Introducing the mevalonate (MVA) pathway into the mitochondria of a strain with an augmented inherent cytosolic MVA pathway increased terpenoid production but also led to the accumulation of toxic pyrophosphate intermediates that negatively affected terpenoid production. We first engineered the inherent MVA pathway in the cytosol and then introduced the MVA pathway into the mitochondria using several promoter combinations, considering the toxicity of pyrophosphate intermediates. However, the highest titer, 183 mg/L, tends to be only 5% higher than that of the strain that only augmented the inherent MVA pathway (SYCM1; 174 mg/L). Next, we hypothesized that, in addition to the toxicity of pyrophosphate, other compounds in the MVA pathway could affect the squalene titer. Thus, we constructed a combinatorial strain library expressing MVA pathway enzymes in the mitochondria with various promoter combinations. The highest squalene titer (230 mg/L) was 32% higher than that of SYCM1. The promoter set revealed that mitigation of mono- and pyrophosphate compound accumulation was important for mitochondrial usage. This study demonstrated that a combinatorial strain library is useful for discovering the optimal gene expression balance in engineering yeast.

Available strategies for improving the biosynthesis of surfactin: a review

Surfactin is an excellent biosurfactant with a wide range of application prospects in many industrial fields. However, its low productivity and high cost have largely limited its commercial applications. In this review, the pathways for surfactin synthesis in Bacillus strains are summarized and discussed. Further, the latest strategies for improving surfactin production, including: medium optimization, genome engineering methods (rational genetic engineering, genome reduction, and genome shuffling), heterologous synthesis, and the use of synthetic biology combined with metabolic engineering approaches to construct high-quality artificial cells for surfactin production using xylose, are described. Finally, the prospects for improving surfactin synthesis are discussed in detail.

Combinatorial optimization and spatial remodeling of CYPs to control product profile

Activating inert substrates is a challenge in nature and synthetic chemistry, but essential for creating functionally active molecules. In this work, we used a combinatorial optimization approach to assemble cytochrome P450 monooxygenases (CYPs) and reductases (CPRs) to achieve a target product profile. By creating 110 CYP-CPR pairs and iteratively screening different pairing libraries, we demonstrated a framework for establishing a CYP network that catalyzes six oxidation reactions at three different positions of a chemical scaffold. Target product titer was improved by remodeling endoplasmic reticulum (ER) size and spatially controlling the CYPs' configuration on the ER. Out of 47 potential products that could be synthesized, 86% of the products synthesized by the optimized network was our target compound quillaic acid (QA), the aglycone backbone of many pharmaceutically important saponins, and fermentation achieved QA titer 2.23 g/L.

Biosensors in microalgae: A roadmap for new opportunities in synthetic biology and biotechnology

Biosensors are powerful tools to investigate, phenotype, improve and prototype microbial strains, both in fundamental research and in industrial contexts. Genetic and biotechnological developments now allow the implementation of synthetic biology approaches to novel different classes of microbial hosts, for example photosynthetic microalgae, which offer unique opportunities. To date, biosensors have not yet been implemented in phototrophic eukaryotic microorganisms, leaving great potential for novel biological and technological advancements untapped. Here, starting from selected biosensor technologies that have successfully been implemented in heterotrophic organisms, we project and define a roadmap on how these could be applied to microalgae research. We highlight novel opportunities for the development of new biosensors, identify critical challenges, and finally provide a perspective on the impact of their eventual implementation to tackle research questions and bioengineering strategies. From studying metabolism at the single-cell level to genome-wide screen approaches, and assisted laboratory evolution experiments, biosensors will greatly impact the pace of progress in understanding and engineering microalgal metabolism. We envision how this could further advance the possibilities for unraveling their ecological role, evolutionary history and accelerate their domestication, to further drive them as resource-efficient production hosts.

A versatile regulatory toolkit of arabinose-inducible artificial transcription factors for Enterobacteriaceae

The Gram-negative bacteria Salmonella enterica and Escherichia coli are important model organisms, powerful prokaryotic expression platforms for biotechnological applications, and pathogenic strains constitute major public health threats. To facilitate new approaches for research and biotechnological applications, we here develop a set of arabinose-inducible artificial transcription factors (ATFs) using CRISPR/dCas9 and Arabidopsis-derived DNA-binding proteins to control gene expression in E. coli and Salmonella over a wide inducer concentration range. The transcriptional output of the different ATFs, in particular when expressed in Salmonella rewired for arabinose catabolism, varies over a wide spectrum (up to 35-fold gene activation). As a proof-of-concept, we use the developed ATFs to engineer a Salmonella two-input biosensor strain, SALSOR 0.2 (SALmonella biosenSOR 0.2), which detects and quantifies alkaloid drugs through a measurable fluorescent output. Moreover, we use plant-derived ATFs to regulate β-carotene biosynthesis in E. coli, resulting in ~2.1-fold higher β-carotene production compared to expression of the biosynthesis pathway using a strong constitutive promoter.

Visioning synthetic futures for yeast research within the context of current global techno-political trends

Yeast research is entering into a new period of scholarship, with new scientific tools, new questions to ask and new issues to consider. The politics of emerging and critical technology can no longer be separated from the pursuit of basic science in fields, such as synthetic biology and engineering biology. Given the intensifying race for technological leadership, yeast research is likely to attract significant investment from government, and that it offers huge opportunities to the curious minded from a basic research standpoint. This article provides an overview of new directions in yeast research with a focus on Saccharomyces cerevisiae, and places these trends in their geopolitical context. At the highest level, yeast research is situated within the ongoing convergence of the life sciences with the information sciences. This convergent effect is most strongly pronounced in areas of AI-enabled tools for the life sciences, and the creation of synthetic genomes, minimal genomes, pan-genomes, neochromosomes and metagenomes using computer-assisted design tools and methodologies. Synthetic yeast futures encompass basic and applied science questions that will be of intense interest to government and nongovernment funding sources. It is essential for the yeast research community to map and understand the context of their research to ensure their collaborations turn global challenges into research opportunities.

Recent Advances on Key Enzymes of Microbial Origin in the Lycopene Biosynthesis Pathway

Lycopene is a common carotenoid found mainly in ripe red fruits and vegetables that is widely used in the food industry due to its characteristic color and health benefits. Microbial synthesis of lycopene is gradually replacing the traditional methods of plant extraction and chemical synthesis as a more economical and productive manufacturing strategy. The biosynthesis of lycopene is a typical multienzyme cascade reaction, and it is important to understand the characteristics of each key enzyme involved and how they are regulated. In this paper, the catalytic characteristics of the key enzymes involved in the lycopene biosynthesis pathway and related studies are first discussed in detail. Then, the strategies applied to the key enzymes of lycopene synthesis, including fusion proteins, enzyme screening, combinatorial engineering, CRISPR/Cas9-based gene editing, DNA assembly, and scaffolding technologies are purposefully illustrated and compared in terms of both traditional and emerging multienzyme regulatory strategies. Finally, future developments and regulatory options for multienzyme synthesis of lycopene and similar secondary metabolites are also discussed.

Engineering low-salt growth Halomonas Bluephagenesis for cost-effective bioproduction combined with adaptive evolution

Halophilic Halomonas bluephagenesis has been engineered to produce various added-value bio-compounds with reduced costs. However, the salt-stress regulatory mechanism remained unclear. H. bluephagenesis was randomly mutated to obtain low-salt growing mutants via atmospheric and room temperature plasma (ARTP). The resulted H. bluephagenesis TDH4A1B5 was constructed with the chromosomal integration of polyhydroxyalkanoates (PHA) synthesis operon phaCAB and deletion of phaP1 gene encoding PHA synthesis associated protein phasin, forming H. bluephagenesis TDH4A1B5P, which led to increased production of poly(3-hydroxybutyrate) (PHB) and poly(3-hydroxybutyrate-co-4-hydrobutyrate) (P34HB) by over 1.4-fold. H. bluephagenesis TDH4A1B5P also enhanced production of ectoine and threonine by 50% and 77%, respectively. A total 101 genes related to salinity tolerance was identified and verified via comparative genomic analysis among four ARTP mutated H. bluephagenesis strains. Recombinant H. bluephagenesis TDH4A1B5P was further engineered for PHA production utilizing sodium acetate or gluconate as sole carbon source. Over 33% cost reduction of PHA production could be achieved using recombinant H. bluephagenesis TDH4A1B5P. This study successfully developed a low-salt tolerant chassis H. bluephagenesis TDH4A1B5P and revealed salt-stress related genes of halophilic host strains.

Modularized synthetic biology enabled intelligent biosensors

Biosensors that sense the concentration of a specified target and produce a specific signal output have become important technology for biological analysis. Recently, intelligent biosensors have received great interest due to their adaptability to meet sophisticated demands. Advances in developing standard modules and carriers in synthetic biology have shed light on intelligent biosensors that can implement advanced analytical processing to better accommodate practical applications. This review focuses on intelligent synthetic biology-enabled biosensors (SBBs). First, we illustrate recent progress in intelligent SBBs with the capability of computation, memory storage, and self-calibration. Then, we discuss emerging applications of SBBs in point-of-care testing (POCT) and wearable monitoring. Finally, future perspectives on intelligent SBBs are proposed.

"In vivo biosynthesis of N,N-dimethyltryptamine, 5-MeO-N,N-dimethyltryptamine, and bufotenine in E.coli"

N,N-dimethyltryptamine (DMT), 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) and 5-hydroxy-N,N-dimethyltryptamine (bufotenine) are psychedelic tryptamines found naturally in both plants and animals and have shown clinical potential to help treat mental disorders, such as anxiety and depression. Advances in both metabolic and genetic engineering make it possible to engineer microbes as cell factories to produce DMT and its aforementioned derivatives to meet demand for ongoing clinical study. Here, we present the development of a biosynthetic production pathway for DMT, 5-MeO-DMT, and bufotenine in the model microbe Escherichia coli. Through the application of genetic optimization techniques and process optimization in benchtop fermenters, the in vivo production of DMT in E. coli was observed. DMT production with tryptophan supplementation reached maximum titers of 74.7 ± 10.5 mg/L under fed batch conditions in a 2-L bioreactor. Additionally, we show the first reported case of de novo production of DMT (from glucose) in E. coli at a maximum titer of 14.0 mg/L and report the first example of microbial 5-MeO-DMT and bufotenine production in vivo. This work provides a starting point for further genetic and fermentation optimization studies with the goal to increase methylated tryptamine production metrics to industrially competitive levels.

A roadmap to establish a comprehensive platform for sustainable manufacturing of natural products in yeast

Secondary natural products (NPs) are a rich source for drug discovery. However, the low abundance of NPs makes their extraction from nature inefficient, while chemical synthesis is challenging and unsustainable. Saccharomyces cerevisiae and Pichia pastoris are excellent manufacturing systems for the production of NPs. This Perspective discusses a comprehensive platform for sustainable production of NPs in the two yeasts through system-associated optimization at four levels: genetics, temporal controllers, productivity screening, and scalability. Additionally, it is pointed out critical metabolic building blocks in NP bioengineering can be identified through connecting multilevel data of the optimized system using deep learning.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

High-Level Productivity of Lacto-N-neotetraose in Escherichia coli by Systematic Metabolic Engineering

Lacto-N-neotetraose (LNnT) is a critical component of human milk oligosaccharides. This study introduces a systems metabolic engineering method to produce LNnT in Escherichia coli. First, 12 target genes contributing to LNnT production were identified using a double-plasmid system. Subsequently, combinatorial optimization of the copy number was performed to tune the target gene expression strength. Next, the CRISPR/Cas9 system was used to block the UDP-Gal and UDP-GlcNAc competitive pathways, and the titer of LNnT reached 1.16 g/L (E27). Moreover, the lactoylglutathione lyase (GloA) was deleted to block the competing metabolite pathway from glycerol to lactate, and the titer of LNnT (1.46 g/L) was 26% higher than that of strain E27. Finally, the LNnT productivity was increased to 0.34 g/L/h in a 3 L bioreactor, which was 36% higher than the recently reported LNnT productivity. This research work opens an innovative framework for the effective production of LNnT.

A dynamic kinetic model captures cell-free metabolism for improved butanol production

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 h. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

CRISPR-dCas12a-mediated genetic circuit cascades for multiplexed pathway optimization

The production efficiency of microbial cell factories is sometimes limited by the lack of effective methods to regulate multiple targets in a coordinated manner. Here taking the biosynthesis of glucosamine-6-phosphate (GlcN6P) in Bacillus subtilis as an example, a 'design-build-test-learn' framework was proposed to achieve efficient multiplexed optimization of metabolic pathways. A platform strain was built to carry biosensor signal-amplifying circuits and two genetic regulation circuits. Then, a synthetic CRISPR RNA array blend for boosting and leading (ScrABBLE) device was integrated into the platform strain, which generated 5,184 combinatorial assemblies targeting three genes. The best GlcN6P producer was screened and engineered for the synthesis of valuable pharmaceuticals N-acetylglucosamine and N-acetylmannosamine. The N-acetylglucosamine titer reached 183.9 g liter-1 in a 15-liter bioreactor. In addition, the potential generic application of the ScrABBLE device was also verified using three fluorescent proteins as a case study.

Characterization of Integrase and Excisionase Activity in a Cell-Free Protein Expression System Using a Modeling and Analysis Pipeline

We present a full-stack modeling, analysis, and parameter identification pipeline to guide the modeling and design of biological systems starting from specifications to circuit implementations and parametrizations. We demonstrate this pipeline by characterizing the integrase and excisionase activity in a cell-free protein expression system. We build on existing Python tools─BioCRNpyler, AutoReduce, and Bioscrape─to create this pipeline. For enzyme-mediated DNA recombination in a cell-free system, we create detailed chemical reaction network models from simple high-level descriptions of the biological circuits and their context using BioCRNpyler. We use Bioscrape to show that the output of the detailed model is sensitive to many parameters. However, parameter identification is infeasible for this high-dimensional model; hence, we use AutoReduce to automatically obtain reduced models that have fewer parameters. This results in a hierarchy of reduced models under different assumptions to finally arrive at a minimal ODE model for each circuit. Then, we run sensitivity analysis-guided Bayesian inference using Bioscrape for each circuit to identify the model parameters. This process allows us to quantify integrase and excisionase activity in cell extracts enabling complex-circuit designs that depend on accurate control over protein expression levels through DNA recombination. The automated pipeline presented in this paper opens up a new approach to complex circuit design, modeling, reduction, and parametrization.

Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation

The passage of proteins across biological membranes via the general secretory (Sec) pathway is a universally conserved process with critical functions in cell physiology and important industrial applications. Proteins are directed into the Sec pathway by a signal peptide at their N-terminus. Estimating the impact of physicochemical signal peptide features on protein secretion levels has not been achieved so far, partially due to the extreme sequence variability of signal peptides. To elucidate relevant features of the signal peptide sequence that influence secretion efficiency, an evaluation of ∼12,000 different designed signal peptides was performed using a novel miniaturized high-throughput assay. The results were used to train a machine learning model, and a post-hoc explanation of the model is provided. By describing each signal peptide with a selection of 156 physicochemical features, it is now possible to both quantify feature importance and predict the protein secretion levels directed by each signal peptide. Our analyses allow the detection and explanation of the relevant signal peptide features influencing the efficiency of protein secretion, generating a versatile tool for the de novo design and in silico evaluation of signal peptides.

Radionuclide Reduction by Combinatorial Optimization of Microbial Extracellular Electron Transfer with a Physiologically Adapted Regulatory Platform

Microbial extracellular electron transfer (EET) is the basis for many microbial processes involved in element geochemical recycling, bioenergy harvesting, and bioremediation, including the technique for remediating U(VI)-contaminated environments. However, the low EET rate hinders its full potential from being fulfilled. The main challenge for engineering microbial EET is the difficulty in optimizing cell resource allocation for EET investment and basic metabolism and the optimal coordination of the different EET pathways. Here, we report a novel combinatorial optimization strategy with a physiologically adapted regulatory platform. Through exploring the physiologically adapted regulatory elements, a 271.97-fold strength range, autonomous, and dynamic regulatory platform was established for Shewanella oneidensis, a prominent electrochemically active bacterium. Both direct and mediated EET pathways are modularly reconfigured and tuned at various intensities with the regulatory platform, which were further assembled combinatorically. The optimal combinations exhibit up to 16.12-, 4.51-, and 8.40-fold improvements over the control in the maximum current density (1009.2 mA/m²) of microbial electrolysis cells and the voltage output (413.8 mV) and power density (229.1 mW/m²) of microbial fuel cells. In addition, the optimal strains exhibited up to 6.53-fold improvement in the radionuclide U(VI) removal efficiency. This work provides an effective and feasible approach to boost microbial EET performance for environmental applications.

Shikimic acid biosynthesis in microorganisms: Current status and future direction

Shikimic acid (SA), a hydroaromatic natural product, is used as a chiral precursor for organic synthesis of oseltamivir (Tamiflu®, an antiviral drug). The process of microbial production of SA has recently undergone vigorous development. Particularly, the sustainable construction of recombinant Corynebacterium glutamicum (141.2 g/L) and Escherichia coli (87 g/L) laid a solid foundation for the microbial fermentation production of SA. However, its industrial application is restricted by limitations such as the lack of fermentation tests for industrial-scale and the requirement of growth-limiting factors, antibiotics, and inducers. Therefore, the development of SA biosensors and dynamic molecular switches, as well as genetic modification strategies and optimization of the fermentation process based on omics technology could improve the performance of SA-producing strains. In this review, recent advances in the development of SA-producing strains, including genetic modification strategies, metabolic pathway construction, and biosensor-assisted evolution, are discussed and critically reviewed. Finally, future challenges and perspectives for further reinforcing the development of robust SA-producing strains are predicted, providing theoretical guidance for the industrial production of SA.

A toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists

Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test and learn cycle of synthetic biology. They provide a better understanding of synthetic designs, and they help identify ways to improve and select designs. While these data are beneficial to design, their collection and analysis is a complex, multistep process that has implications on both discovery and reproducibility of experiments. Additionally, tool parameters, experimental metadata, normalization of data and standardization of file formats present challenges that are computationally intensive. This calls for high-throughput pipelines expressly designed to handle the combinatorial and longitudinal nature of synthetic biology. In this paper, we present a pipeline to maximize the analytical reproducibility of RNASeq for synthetic biologists. We also explore the impact of reproducibility on the validation of machine learning models. We present the design of a pipeline that combines traditional RNASeq data processing tools with structured metadata tracking to allow for the exploration of the combinatorial design in a high-throughput and reproducible manner. We then demonstrate utility via two different experiments: a control comparison experiment and a machine learning model experiment. The first experiment compares datasets collected from identical biological controls across multiple days for two different organisms. It shows that a reproducible experimental protocol for one organism does not guarantee reproducibility in another. The second experiment quantifies the differences in experimental runs from multiple perspectives. It shows that the lack of reproducibility from these different perspectives can place an upper bound on the validation of machine learning models trained on RNASeq data.

Graphical Abstract

Putative Protein Discovery from Microalgal Genomes as a Synthetic Biology Protein Library for Heavy Metal Bio-Removal

Simple Summary

Nowadays, heavy metal polluted wastewater is one of the global challenges that leads to an insufficient supply of clean water. Taking advantage of what nature has to offer, several organisms, including microalgae, can natively bioremediate these heavy metals. However, the effectiveness of such processes does not meet expectations, especially with the increasing amount of pollution in today’s world. Therefore, with the goal of creating effective strains, synthetic biology via bioengineering is widely used as a strategy to enhance the heavy metal bio-removing capability, either by directly engineering the native ability of organisms or by transferring the ability to a more suitable host. In order to do so, a list of genes or proteins involved in the processes is crucial for stepwise engineering. Yet, a large amount of information remains to be discovered. In this work, a comprehensive library of putative proteins that are involved in heavy metal bio-removal from microalgae was constructed. Moreover, with the development of machine learning, the 3D structures of these proteins are also predicted, using machine learning-based methods, to aid the use of synthetic biology further.

Abstract

Synthetic biology is a principle that aims to create new biological systems with particular functions or to redesign the existing ones through bioengineering. Therefore, this principle is often utilized as a tool to put the knowledge learned to practical use in actual fields. However, there is still a great deal of information remaining to be found, and this limits the possible utilization of synthetic biology, particularly on the topic that is the focus of the present work—heavy metal bio-removal. In this work, we aim to construct a comprehensive library of putative proteins that might support heavy metal bio-removal. Hypothetical proteins were discovered from Chlorella and Scenedesmus genomes and extensively annotated. The protein structures of these putative proteins were also modeled through Alphafold2. Although a portion of this workflow has previously been demonstrated to annotate hypothetical proteins from whole genome sequences, the adaptation of such steps is yet to be done for library construction purposes. We also demonstrated further downstream steps that allow a more accurate function prediction of the hypothetical proteins by subjecting the models generated to structure-based annotation. In conclusion, a total of 72 newly discovered putative proteins were annotated with ready-to-use predicted structures available for further investigation.

A versatile active learning workflow for optimization of genetic and metabolic networks

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO₂-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 10²⁵ conditions with only 1,000 experiments to yield the most efficient CO₂-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, aimed at democratization and standardization, the authors describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets.

Transcriptional Tuning of Mevalonate Pathway Enzymes to Identify the Impact on Limonene Production in Escherichia coli

Heterologous production of limonene in microorganisms through the mevalonate (MVA) pathway has traditionally imposed metabolic burden and reduced cell fitness, where imbalanced stoichiometries among sequential enzymes result in the accumulation of toxic intermediates. Although prior studies have shown that changes to mRNA stability, RBS strength, and protein homology can be effective strategies for balancing enzyme levels in the MVA pathway, testing different variations of these parameters often requires distinct genetic constructs, which can exponentially increase assembly costs as pathways increase in size. Here, we developed a multi-input transcriptional circuit to regulate the MVA pathway, where four chemical inducers, l-arabinose (Ara), choline chloride (Cho), cuminic acid (Cuma), and isopropyl β-d-1-thiogalactopyranoside (IPTG), each regulate one of four orthogonal promoters. We tested modular transcriptional regulation of the MVA pathway by placing this circuit in an engineered Escherichia coli "marionette" strain, which enabled systematic and independent tuning of the first three enzymes (AtoB, HMGS, and HMGR) in the MVA pathway. By systematically testing combinations of chemical inducers as inputs, we investigated relationships between the expressions of different MVA pathway submodules, finding that limonene yields are sensitive to the coordinated transcriptional regulation of HMGS and HMGR.

Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology

Synthetic biology (SynBio) is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation (GDA) allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time, and resources in the process. Establishing SynBio's future is dependent on GDA, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers recent advancements in GDA, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future.

Mining and engineering exporters for titer improvement of macrolide biopesticides in Streptomyces

Exporter engineering is a promising strategy to construct high-yield Streptomyces for natural product pharmaceuticals in industrial biotechnology. However, available exporters are scarce, due to the limited knowledge of bacterial transporters. Here, we built a workflow for exporter mining and devised a tunable plug-and-play exporter (TuPPE) module to improve the production of macrolide biopesticides in Streptomyces. Combining genome analyses and experimental confirmations, we found three ATP-binding cassette transporters that contribute to milbemycin production in Streptomyces bingchenggensis. We then optimized the expression level of target exporters for milbemycin titer optimization by designing a TuPPE module with replaceable promoters and ribosome binding sites. Finally, broader applications of the TuPPE module were implemented in industrial S. bingchenggensis BC04, Streptomyces avermitilis NEAU12 and Streptomyces cyaneogriseus NMWT1, which led to optimal titer improvement of milbemycin A3/A4, avermectin B1a and nemadectin α by 24.2%, 53.0% and 41.0%, respectively. Our work provides useful exporters and a convenient TuPPE module for titer improvement of macrolide biopesticides in Streptomyces. More importantly, the feasible exporter mining workflow developed here might shed light on widespread applications of exporter engineering in Streptomyces to boost the production of other secondary metabolites.

PASIV: A Pooled Approach-Based Workflow to Overcome Toxicity-Induced Design of Experiments Failures and Inefficiencies

We present here a newly developed workflow—which we have called PASIV—designed to provide a solution to a practical problem with design of experiments (DoE) methodology: i.e., what can be done if the scoping phase of the DoE cycle is severely hampered by burden and toxicity issues (caused by either the metabolite or an intermediary), making it unreliable or impossible to proceed to the screening phase? PASIV—standing for pooled approach, screening, identification, and visualization—was designed so the (viable) region of interest can be made to appear through an interplay between biology and software. This was achieved by combining multiplex construction in a pooled approach (one-pot reaction) with a viability assay and with a range of bioinformatics tools (including a novel construct matching tool). PASIV was tested on the exemplar of the lycopene pathway—under stressful constitutive expression—yielding a region of interest with comparatively stronger producers.

Multiscale engineering of microbial cell factories: A step forward towards sustainable natural products industry

Microbial cell factories (bacteria and fungi) are the leading producers of beneficial natural products such as lycopene, carotene, herbal medicine, and biodiesel etc. These microorganisms are considered efficient due to their effective bioprocessing strategy (monoculture- and consortial-based approach) under distinct processing conditions. Meanwhile, the advancement in genetic and process optimization techniques leads to enhanced biosynthesis of natural products that are known functional ingredients with numerous applications in the food, cosmetic and medical industries. Natural consortia and monoculture thrive in nature in a small proportion, such as wastewater, food products, and soils. In similitude to natural consortia, it is possible to engineer artificial microbial consortia and program their behaviours via synthetic biology tools. Therefore, this review summarizes the optimization of genetic and physicochemical parameters of the microbial system for improved production of natural products. Also, this review presents a brief history of natural consortium and describes the functional properties of monocultures. This review focuses on synthetic biology tools that enable new approaches to design synthetic consortia; and highlights the syntropic interactions that determine the performance and stability of synthetic consortia. In particular, the effect of processing conditions and advanced genetic techniques to improve the productibility of both monoculture and consortial based systems have been greatly emphasized. In this context, possible strategies are also discussed to give an insight into microbial engineering for improved production of natural products in the future. In summary, it is concluded that the coupling of genomic modifications with optimum physicochemical factors would be promising for producing a robust microbial cell factory that shall contribute to the increased production of natural products.

Highly Reversible Tunable Thermal-Repressible Split-T7 RNA Polymerases (Thermal-T7RNAPs) for Dynamic Gene Regulation

Temperature is a physical cue that is easy to apply, allowing cellular behaviors to be controlled in a contactless and dynamic manner via heat-inducible/repressible systems. However, existing heat-repressible systems are limited in number, rely on thermal sensitive mRNA or transcription factors that function at low temperatures, lack tunability, suffer delays, and are overly complex. To provide an alternative mode of thermal regulation, we developed a library of compact, reversible, and tunable thermal-repressible split-T7 RNA polymerase systems (Thermal-T7RNAPs), which fused temperature-sensitive domains of Tlpa protein with split-T7RNAP to enable direct thermal control of the T7RNAP activity between 30 and 42 °C. We generated a large mutant library with varying thermal performances via an automated screening framework to extend temperature tunability. Lastly, using the mutants, novel thermal logic circuitry was implemented to regulate cell growth and achieve active thermal control of the cell proportions within co-cultures. Overall, this technology expanded avenues for thermal control in biotechnology applications.

Medium optimization and subsequent fermentative regulation enabled the scaled-up production of anti-tuberculosis drug leads ilamycin-E1/E2

BACKGROUND

Tuberculosis (TB) and its evolving drug resistance have exerted severe threats on the global health, hence it is still essential to develop novel anti-TB antibiotics. Ilamycin-E1/E2 is a pair of cycloheptapeptide enantiomers obtained from a marine Streptomyces atratus SCSIO ZH16-ΔilaR mutant, and have presented significant anti-TB activities as promising drug lead compounds, but their clinical development has been hampered by low fermentation titers.

MAIN METHODS AND MAJOR RESULTS

By applying the statistical Plackett-Burman design (PBD) model, bacterial peptone was first screened out as the only significant but negative factor to affect the ilamycin-E1/E2 production. Subsequent single factor optimization in shaking flasks revealed that the replacement of bacterial peptone with malt extract could not only eliminate the accumulation of porphyrin-type competitive byproducts, but also improve the titer of ilamycin-E1/E2 from original 13.6±0.8 to 142.7±5.7 mg/L, about 10.5-fold increase. Next, a pH coordinated feeding strategy was adopted in 30L fermentor and obtained 169.8±2.5 mg/L ilamycin-E1/E2, but further scaled-up production in 300L fermentor only gave a titer of 131.5±7.5 mg/L due to the unsynchronization of feeding response and pH change. Consequently, a continuous pulse feeding strategy was utilized in 300L fermentor to solve the above problem and finally achieved 415.7±29.2 mg/L ilamycin-E1/E2, representing a 30.5-fold improvement.

IMPLICATION

Our work has provided a solid basis to acquire sufficient ilamycin-E1/E2 lead compounds and then support their potential anti-TB drug development. This article is protected by copyright. All rights reserved.

Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology

We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).