A Yeast-Based Biosensor for Screening of Short- and Medium-Chain Fatty Acid Production

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

As a sustainable and renewable alternative to petroleum fuels, advanced biofuels shoulder the responsibility of energy saving, emission reduction and environmental protection. Traditional engineering of cell factories for production of advanced biofuels lacks efficient high-throughput screening tools and regulating systems, impeding the improvement of cellular productivity and yield. Transcription factor-based biosensors have been widely applied to monitor and regulate microbial cell factory products due to the advantages of fast detection and in-situ screening. This review updates the design and application of transcription factor-based biosensors tailored for advanced biofuels and related intermediates. The construction and genetic parts selection principle of biosensors are discussed. Strategies to enhance the performance of biosensor, including regulating promoter strength and RBS strength, optimizing plasmid copy number, implementing genetic amplifier, and modulating the structure of transcription factor, have also been summarized. We further review the application of biosensors in high-throughput screening of new metabolic engineering targets, evolution engineering, confirmation of protein function, and dynamic regulation of metabolic flux for higher production of advanced biofuels. At last, we discuss the current limitations and future trends of transcription factor-based biosensors.

Four ways of implementing robustness quantification in strain characterisation

BACKGROUND

In industrial bioprocesses, microorganisms are generally selected based on performance, whereas robustness, i.e., the ability of a system to maintain a stable performance, has been overlooked due to the challenges in its quantification and implementation into routine experimental procedures. This work presents four ways of implementing robustness quantification during strain characterisation. One Saccharomyces cerevisiae laboratory strain (CEN.PK113-7D) and two industrial strains (Ethanol Red and PE2) grown in seven different lignocellulosic hydrolysates were assessed for growth-related functions (specific growth rate, product yields, etc.) and eight intracellular parameters (using fluorescent biosensors).

RESULTS

Using flasks and high-throughput experimental setups, robustness was quantified in relation to: (i) stability of growth functions in response to the seven hydrolysates; (ii) stability of growth functions across different strains to establish the impact of perturbations on yeast metabolism; (iii) stability of intracellular parameters over time; (iv) stability of intracellular parameters within a cell population to indirectly quantify population heterogeneity. Ethanol Red was the best-performing strain under all tested conditions, achieving the highest growth function robustness. PE2 displayed the highest population heterogeneity. Moreover, the intracellular environment varied in response to non-woody or woody lignocellulosic hydrolysates, manifesting increased oxidative stress and unfolded protein response, respectively.

CONCLUSIONS

Robustness quantification is a powerful tool for strain characterisation as it offers novel information on physiological and biochemical parameters. Owing to the flexibility of the robustness quantification method, its implementation was successfully validated at single-cell as well as high-throughput levels, showcasing its versatility and potential for several applications.

In Vivo Detection of Low Molecular Weight Platform Chemicals and Environmental Contaminants by Genetically Encoded Biosensors

Genetically encoded biosensor systems operating in living cells are versatile, cheap, and transferable tools for the detection and quantification of a broad range of small molecules. This review presents state-of-the-art biosensor designs and assemblies, featuring transcription factor-, riboswitch-, and enzyme-coupled devices, highly engineered fluorescent probes, and emerging two-component systems. Importantly, (bioinformatic-assisted) strategies to resolve contextual issues, which cause biosensors to miss performance criteria in vivo, are highlighted. The optimized biosensing circuits can be used to monitor chemicals of low molecular mass (<200 g mol^-1) and physicochemical properties that challenge conventional chromatographical methods with high sensitivity. Examples herein include but are not limited to formaldehyde, formate, and pyruvate as immediate products from (synthetic) pathways for the fixation of carbon dioxide (CO₂), industrially important derivatives like small- and medium-chain fatty acids and biofuels, as well as environmental toxins such as heavy metals or reactive oxygen and nitrogen species. Lastly, this review showcases biosensors capable of assessing the biosynthesis of platform chemicals from renewable resources, the enzymatic degradation of plastic waste, or the bioadsorption of highly toxic chemicals from the environment. These applications offer new biosensor-based manufacturing, recycling, and remediation strategies to tackle current and future environmental and socioeconomic challenges including the wastage of fossil fuels, the emission of greenhouse gases like CO₂, and the pollution imposed on ecosystems and human health.

Fluorescence coupling strategies in fluorescence-activated droplet sorting (FADS) for ultrahigh-throughput screening of enzymes, metabolites, and antibodies

Fluorescence-activated droplet sorting (FADS) has emerged as a powerful tool for ultrahigh-throughput screening of enzymes, metabolites, and antibodies. Fluorescence coupling strategies (FCSs) are key to the development of new FADS methods through their coupling of analyte properties such as concentration, activities, and affinity with fluorescence signals. Over the last decade, a series of FCSs have been developed, greatly expanding applications of FADS. Here, we review recent advances in FCS for different analyte types, providing a critical comparison of the available FCSs and further classification into four categories according to their principles. We also summarize successful FADS applications employing FCSs in enzymes, metabolites, and antibodies. Further, we outline possible future developments in this area.

Recent advances in the analytical strategies of microbial biosensor for detection of pollutants

Microbial biosensor which integrates different types of microorganisms, such as bacteria, microalgae, fungi, and virus have become suitable technologies to address limitations of conventional analytical methods. The main applications of biosensors include the detection of environmental pollutants, pathogenic bacteria and compounds related to illness, and food quality. Each type of microorganisms possesses advantages and disadvantages with different mechanisms to detect the analytes of interest. Furthermore, there is an increasing trend in genetic modifications for the development of microbial biosensors due to potential for high-throughput analysis and portability. Many review articles have discussed the applications of microbial biosensor, but many of them focusing only about bacterial-based biosensor although other microbes also possess many advantages. Additionally, reviews on the applications of all microbes as biosensor especially viral and microbial fuel cell biosensors are also still limited. Therefore, this review summarizes all the current applications of bacterial-, microalgal-, fungal-, viral-based biosensor in regard to environmental, food, and medical-related applications. The underlying mechanism of each microbes to detect the analytes are also discussed. Additionally, microbial fuel cell biosensors which have great potential in the future are also discussed. Although many advantageous microbial-based biosensors have been discovered, other areas such as forensic detection, early detection of bacteria or virus species that can lead to pandemics, and others still need further investigation. With that said, microbial-based biosensors have promising potential for vast applications where the biosensing performance of various microorganisms are presented in this review along with future perspectives to resolve problems related on microbial biosensors.

Fluorescence lifetime imaging of NAD(P)H upon oxidative stress in Kluyveromyces marxianus

K. marxianus is a promising cell factory for producing heterologous proteins. Oxidative stresses were raised during overexpression of heterologous proteins, leading to the shift of the redox state. How to measure the redox state of live K. marxianus cells without perturbing their growth remains a big challenge. Here, a fluorescence lifetime imaging (FLIM)-based method was developed in live K. marxianus cells. During the early exponential growth, K. marxianus cells exhibited an increased mean fluorescence lifetime (τ-mean) of NAD(P)H compared with Saccharomyces cerevisiae cells, which was consistent with the preference for respiration in K. marxianus cells and that for fermentation in S. cerevisiae cells. Upon oxidative stresses induced by high temperature or H2O2, K. marxianus cells exhibited an increased τ-mean in company with decreased intracellular NAD(P)H/NAD(P)+, suggesting a correlation between an increased τ-mean and a more oxidized redox state. The relationship between τ-mean and the expression level of a heterologous protein was investigated. There was no difference between the τ-means of K. marxianus strains which were not producing a heterologous protein. The τ-mean of a strain yielding a high level of a heterologous protein was higher than that of a low-yielding strain. The results suggested the potential application of FLIM in the non-invasive screen of high-yielding cells.

Transporter-Driven Engineering of a Genetic Biosensor for the Detection and Production of Short-Branched Chain Fatty Acids in Saccharomyces cerevisiae

Biosensors can be used for real-time monitoring of metabolites and high-throughput screening of producer strains. Use of biosensors has facilitated strain engineering to efficiently produce value-added compounds. Following our recent work on the production of short branched-chain fatty acids (SBCFAs) in engineered Saccharomyces cerevisiae, here we harnessed a weak organic acid transporter Pdr12p, engineered a whole-cell biosensor to detect exogenous and intracellular SBCFAs and optimized the biosensor’s performance by varying PDR12 expression. We firstly constructed the biosensor and evaluated its response to a range of short-chain carboxylic acids. Next, we optimized its sensitivity and operational range by deletion and overexpression of PDR12. We found that the biosensor responded to exogenous SBCFAs including isovaleric acid, isobutyric acid and 2-methylbutanoic acid. PDR12 deletion enhanced the biosensor’s sensitivity to isovaleric acid at a low concentration and PDR12 overexpression shifted the operational range towards a higher concentration. Lastly, the deletion of PDR12 improved the biosensor’s sensitivity to the SBCFAs produced in our previously engineered SBCFA-overproducing strain. To our knowledge, our work represents the first study on employing an ATP-binding-cassette transporter to engineer a transcription-factor-based genetic biosensor for sensing SBCFAs in S. cerevisiae. Our findings provide useful insights into SBCFA detection by a genetic biosensor that will facilitate the screening of SBCFA-overproducing strains.

Robustness: linking strain design to viable bioprocesses

Microbial cell factories are becoming increasingly popular for the sustainable production of various chemicals. Metabolic engineering has led to the design of advanced cell factories; however, their long-term yield, titer, and productivity falter when scaled up and subjected to industrial conditions. This limitation arises from a lack of robustness - the ability to maintain a constant phenotype despite the perturbations of such processes. This review describes predictable and stochastic industrial perturbations as well as state-of-the-art technologies to counter process variability. Moreover, we distinguish robustness from tolerance and discuss the potential of single-cell studies for improving system robustness. Finally, we highlight ways of achieving consistent and comparable quantification of robustness that can guide the selection of strains for industrial bioprocesses.

High-Throughput Screening of an Octanoic Acid Producer Strain Library Enables Detection of New Targets for Increasing Titers in Saccharomyces cerevisiae

Octanoic acid is an industrially relevant compound with applications in antimicrobials or as a precursor for biofuels. Microbial biosynthesis through yeast is a promising alternative to current unsustainable production methods. To increase octanoic acid titers in Saccharomyces cerevisiae, we use a previously developed biosensor that is based on the octanoic acid responsive pPDR12 promotor coupled to GFP. We establish a biosensor strain amenable for high-throughput screening of an octanoic acid producer strain library. Through development, optimization, and execution of a high-throughput screening approach, we were able to detect two new genetic targets, KCS1 and FSH2, which increased octanoic acid titers through combined overexpression by about 55% compared to the parental strain. Neither target has yet been reported to be involved in fatty acid biosynthesis. The presented methodology can be employed to screen any genetic library and thereby more genes involved in improving octanoic acid production can be detected in the future.

Transcription-Factor-based Biosensor Engineering for Applications in Synthetic Biology

Transcription-factor-based biosensors (TFBs) are often used for metabolite detection, adaptive evolution, and metabolic flux control. However, designing TFBs with superior performance for applications in synthetic biology remains challenging. Specifically, natural TFBs often do not meet real-time detection requirements owing to their slow response times and inappropriate dynamic ranges, detection ranges, sensitivity, and selectivity. Furthermore, designing and optimizing complex dynamic regulation networks is time-consuming and labor-intensive. This Review highlights TFB-based applications and recent engineering strategies ranging from traditional trial-and-error approaches to novel computer-model-based rational design approaches. The limitations of the applications and these engineering strategies are additionally reviewed.

Production of octanoic acid in Saccharomyces cerevisiae: Investigation of new precursor supply engineering strategies and intrinsic limitations

The eight-carbon fatty acid octanoic acid (OA) is an important platform chemical and precursor of many industrially relevant products. Its microbial biosynthesis is regarded as a promising alternative to current unsustainable production methods. In Saccharomyces cerevisiae, the production of OA had been previously achieved by rational engineering of the fatty acid synthase. For the supply of the precursor molecule acetyl-CoA and of the redox cofactor NADPH, the native pyruvate dehydrogenase bypass had been harnessed, or the cells had been additionally provided with a pathway involving a heterologous ATP-citrate lyase. Here, we redirected the flux of glucose towards the oxidative branch of the pentose phosphate pathway and overexpressed a heterologous phosphoketolase/phosphotransacetylase shunt to improve the supply of NADPH and acetyl-CoA in a strain background with abolished OA degradation. We show that these modifications lead to an increased yield of OA during the consumption of glucose by more than 60% compared to the parental strain. Furthermore, we investigated different genetic engineering targets to identify potential factors that limit the OA production in yeast. Toxicity assays performed with the engineered strains suggest that the inhibitory effects of OA on cell growth likely impose an upper limit to attainable OA yields.

The Model System Saccharomyces cerevisiae Versus Emerging Non-Model Yeasts for the Production of Biofuels

Microorganisms are effective platforms for the production of a variety of chemicals including biofuels, commodity chemicals, polymers and other natural products. However, deep cellular understanding is required for improvement of current biofuel cell factories to truly transform the Bioeconomy. Modifications in microbial metabolic pathways and increased resistance to various types of stress caused by the production of these chemicals are crucial in the generation of robust and efficient production hosts. Recent advances in systems and synthetic biology provide new tools for metabolic engineering to design strategies and construct optimal biocatalysts for the sustainable production of desired chemicals, especially in the case of ethanol and fatty acid production. Yeast is an efficient producer of bioethanol and most of the available synthetic biology tools have been developed for the industrial yeast Saccharomyces cerevisiae. Non-conventional yeast systems have several advantageous characteristics that are not easily engineered such as ethanol tolerance, low pH tolerance, thermotolerance, inhibitor tolerance, genetic diversity and so forth. Currently, synthetic biology is still in its initial steps for studies in non-conventional yeasts such as Yarrowia lipolytica, Kluyveromyces marxianus, Issatchenkia orientalis and Pichia pastoris. Therefore, the development and application of advanced synthetic engineering tools must also focus on these underexploited, non-conventional yeast species. Herein, we review the basic synthetic biology tools that can be applied to the standard S. cerevisiae model strain, as well as those that have been developed for non-conventional yeasts. In addition, we will discuss the recent advances employed to develop non-conventional yeast strains that are efficient for the production of a variety of chemicals through the use of metabolic engineering and synthetic biology.

Transporter Engineering for Microbial Manufacturing

Microbes play an important role in biotransformation and biosynthesis of biofuels, natural products, and polymers. Therefore, microbial manufacturing has been widely used in medicine, industry, and agriculture. However, common strategies including enzyme engineering, pathway optimization, and host engineering are generally inadequate to obtain an efficient microbial production system. Transporter engineering provides an alternative strategy to promote the transmembrane transfer of substrates, intermediates, and final products in microbial cells and thus enhances production by alleviating feedback inhibition and cytotoxicity caused by final products. According to the current studies in transport engineering, native transporters usually have low expression and poor transportation ability, resulting in inefficient transport processes and microbial production. In this review, current approaches for transporter mining, characterization, and verification are comprehensively summarized. Practical approaches to enhance the transport system in engineered cells, such as balancing transporter overexpression and cell growth, and evolution of native transporters are discussed. Furthermore, the applications of transporter engineering in microbial manufacturing, including enhancement of substrate utilization, concentration of metabolic flux to the target pathway, and acceleration of efflux and recovery of products, demonstrate its outstanding advantages and promising prospects.

Discovery and identification of medium-chain fatty acid responsive promoters in Saccharomyces cerevisiae

Medium-chain fatty acids (MCFAs) and their derivatives are important chemicals that can be used in lubricants, detergents, and cosmetics. MCFAs can be produced in several microbes, although production is not high. Dynamic regulation by synthetic biology is a good method of improving production of chemicals that avoids toxic intermediates, but chemical-responsive promoters are required. Several MCFA sensors or promoters have been reported in Saccharomyces cerevisiae. In this study, by using transcriptomic analysis of S. cerevisiae exposed to fatty acids with 6-, 12-, and 16-carbon chains, we identified 58 candidate genes that may be responsive to MCFAs. Using a fluorescence-based screening method, we identified MCFA-responsive promoters, four that upregulated gene expression, and three that downregulated gene expression. Dose-response analysis revealed that some of the promoters were sensitive to fatty acid concentrations as low as 0.02-0.06 mM. The MCFA-responsive promoters reported in this study could be used in dynamic regulation of fatty acids and fatty acid-derived products in S. cerevisiae.

Advanced Strategies for Production of Natural Products in Yeast

Natural products account for more than 50% of all small-molecule pharmaceutical agents currently in clinical use. However, low availability often becomes problematic when a bioactive natural product is promising to become a pharmaceutical or leading compound. Advances in synthetic biology and metabolic engineering provide a feasible solution for sustainable supply of these compounds. In this review, we have summarized current progress in engineering yeast cell factories for production of natural products, including terpenoids, alkaloids, and phenylpropanoids. We then discuss advanced strategies in metabolic engineering at three different dimensions, including point, line, and plane (corresponding to the individual enzymes and cofactors, metabolic pathways, and the global cellular network). In particular, we comprehensively discuss how to engineer cofactor biosynthesis for enhancing the biosynthesis efficiency, other than the enzyme activity. Finally, current challenges and perspective are also discussed for future engineering direction.

Biosensor-based enzyme engineering approach applied to psicose biosynthesis

Bioproduction of chemical compounds is of great interest for modern industries, as it reduces their production costs and ecological impact. With the use of synthetic biology, metabolic engineering and enzyme engineering tools, the yield of production can be improved to reach mass production and cost-effectiveness expectations. In this study, we explore the bioproduction of D-psicose, also known as D-allulose, a rare non-toxic sugar and a sweetener present in nature in low amounts. D-psicose has interesting properties and seemingly the ability to fight against obesity and type 2 diabetes. We developed a biosensor-based enzyme screening approach as a tool for enzyme selection that we benchmarked with the Clostridium cellulolyticum D-psicose 3-epimerase for the production of D-psicose from D-fructose. For this purpose, we constructed and characterized seven psicose responsive biosensors based on previously uncharacterized transcription factors and either their predicted promoters or an engineered promoter. In order to standardize our system, we created the Universal Biosensor Chassis, a construct with a highly modular architecture that allows rapid engineering of any transcription factor-based biosensor. Among the seven biosensors, we chose the one displaying the most linear behavior and the highest increase in fluorescence fold change. Next, we generated a library of D-psicose 3-epimerase mutants by error-prone PCR and screened it using the biosensor to select gain of function enzyme mutants, thus demonstrating the framework's efficiency.

Engineering transcription factor BmoR for screening butanol overproducers

The wild-type transcription factors are sensitive to their corresponding signal molecules. Using wild-type transcription factors as biosensors to screen industrial overproducers are generally impractical because of their narrow detection ranges. This study took transcription factor BmoR as an example and aimed to expand the detection range of BmoR for screening alcohols overproducers. Firstly, a BmoR mutation library was established, and the mutations distributed randomly in all predicted functional domains of BmoR. Structure of BmoR-isobutanol complex were modelled, and isobutanol binding sites were confirmed by site-directed mutagenesis. Subsequently, the effects of the mutations on the detection range or output were confirmed in the BmoR mutants. Four combinatorial mutants containing one increased-detection-range mutation and one enhanced-output mutation were constructed. Compared with wild-type BmoR, F276A/E627N BmoR and D333N/E627N BmoR have wider detection ranges (0-100 mM) and relatively high outputs to the isobutanol added quantitatively or produced intracellularly, demonstrating they have potential for screening isobutanol overproduction strains. This work presented an example of engineering the wild-type transcription factors with physiological significance for industrial utilization.

Synthetic Biology of Yeast

With the rapid development of DNA synthesis and next-generation sequencing, synthetic biology that aims to standardize, modularize, and innovate cellular functions, has achieved vast progress. Here we review key advances in synthetic biology of the yeast Saccharomyces cerevisiae, which serves as an important eukaryal model organism and widely applied cell factory. This covers the development of new building blocks, i.e., promoters, terminators and enzymes, pathway engineering, tools developments, and gene circuits utilization. We will also summarize impacts of synthetic biology on both basic and applied biology, and end with further directions for advancing synthetic biology in yeast.