Empowering a Methanol-Dependent Escherichia coli via Adaptive Evolution Using a High-Throughput Microbial Microdroplet Culture System

Multidimensional combinatorial screening for high-level production of erythritol in Yarrowia lipolytica

Yarrowia lipolytica was successfully engineered to synthesize erythritol from crude glycerol, a cheap by-product of biodiesel production, but the yield remained low. Here, a biosensor-guided adaptive evolution screening platform was constructed to obtain mutant strains which could efficiently utilize crude glycerol to produce erythritol. Erythrose reductase D46A (M1) was identified as a key mutant through whole-genome sequencing of the strain G12, which exhibited higher catalytic activity (1.6-fold of the wild-type). M1 was further modified to obtain a combinatorial mutant with 4.1-fold enhancement of catalytic activity. Finally, the metabolic network was reconfigured to redirect carbon fluxes toward erythritol synthesis. The erythritol titer of the engineered strain G31 reached 220.5 g/L with a productivity of 1.8 g/L/h in a 5-L bioreactor. The study provides valuable guidance for biosensor-based ultra-high-throughput screening strategies in Y. lipolytica, as well as presenting a new paradigm for the sustainable valorization of crude glycerol.

High-throughput G protein-coupled receptor-based autocrine screening for secondary metabolite production in yeast

Biosensors are valuable tools in accelerating the test phase of the design-build-test-learn cycle of cell factory development, as well as in bioprocess monitoring and control. G protein-coupled receptor (GPCR)-based biosensors enable cells to sense a wide array of molecules and environmental conditions in a specific manner. Due to the extracellular nature of their sensing, GPCR-based biosensors require compartmentalization of distinct genotypes when screening production levels of a strain library to ensure that detected levels originate exclusively from the strain under assessment. Here, we explore the integration of production and sensing modalities into a single Saccharomyces cerevisiae strain and compartmentalization using three different methods: (1) cultivation in microtiter plates, (2) spatial separation on agar plates, and (3) encapsulation in water-in-oil-in-water double emulsion droplets, combined with analysis and sorting via a fluorescence-activated cell sorting machine. Employing tryptamine and serotonin as proof-of-concept target molecules, we optimize biosensing conditions and demonstrate the ability of the autocrine screening method to enrich for high producers, showing the enrichment of a serotonin-producing strain over a nonproducing strain. These findings illustrate a workflow that can be adapted to screening for a wide range of complex chemistry at high throughput using commercially available microfluidic systems.

From Saccharomyces cerevisiae to Ethanol: Unlocking the Power of Evolutionary Engineering in Metabolic Engineering Applications

Increased human population and the rapid decline of fossil fuels resulted in a global tendency to look for alternative fuel sources. Environmental concerns about fossil fuel combustion led to a sharp move towards renewable and environmentally friendly biofuels. Ethanol has been the primary fossil fuel alternative due to its low carbon emission rates, high octane content and comparatively facile microbial production processes. In parallel to the increased use of bioethanol in various fields such as transportation, heating and power generation, improvements in ethanol production processes turned out to be a global hot topic. Ethanol is by far the leading yeast output amongst a broad spectrum of bio-based industries. Thus, as a well-known platform microorganism and native ethanol producer, baker's yeast Saccharomyces cerevisiae has been the primary subject of interest for both academic and industrial perspectives in terms of enhanced ethanol production processes. Metabolic engineering strategies have been primarily adopted for direct manipulation of genes of interest responsible in mainstreams of ethanol metabolism. To overcome limitations of rational metabolic engineering, an alternative bottom-up strategy called inverse metabolic engineering has been widely used. In this context, evolutionary engineering, also known as adaptive laboratory evolution (ALE), which is based on random mutagenesis and systematic selection, is a powerful strategy to improve bioethanol production of S. cerevisiae. In this review, we focus on key examples of metabolic and evolutionary engineering for improved first- and second-generation S. cerevisiae bioethanol production processes. We delve into the current state of the field and show that metabolic and evolutionary engineering strategies are intertwined and many metabolically engineered strains for bioethanol production can be further improved by powerful evolutionary engineering strategies. We also discuss potential future directions that involve recent advancements in directed genome evolution, including CRISPR-Cas9 technology.

Advances in biosynthesis of higher alcohols in Escherichia coli

In recent years, the development of green energy to replace fossil fuels has been the focus of research. Higher alcohols are important biofuels and chemicals. The production of higher alcohols in microbes has gained attention due to its environmentally friendly character. Higher alcohols have been synthesized in model microorganism Escherichia coli, and the production has reached the gram level through enhancement of metabolic flow, the balance of reducing power and the optimization of fermentation processes. Sustainable bio-higher alcohols production is expected to replace fossil fuels as a green and renewable energy source. Therefore, this review summarizes the latest developments in producing higher alcohols (C3-C6) by E. coli, elucidate the main bottlenecks limiting the biosynthesis of higher alcohols, and proposes potential engineering strategies of improving the production of biological higher alcohols. This review would provide a theoretical basis for further research on higher alcohols production by E. coli.

Accelerated Adaptive Laboratory Evolution by Automated Repeated Batch Processes in Parallelized Bioreactors

Adaptive laboratory evolution (ALE) is a valuable complementary tool for modern strain development. Insights from ALE experiments enable the improvement of microbial cell factories regarding the growth rate and substrate utilization, among others. Most ALE experiments are conducted by serial passaging, a method that involves large amounts of repetitive manual labor and comes with inherent experimental design flaws. The acquisition of meaningful and reliable process data is a burdensome task and is often undervalued and neglected, but also unfeasible in shake flask experiments due to technical limitations. Some of these limitations are alleviated by emerging automated ALE methods on the μL and mL scale. A novel approach to conducting ALE experiments is described that is faster and more efficient than previously used methods. The conventional shake flask approach was translated to a parallelized, L scale stirred-tank bioreactor system that runs controlled, automated, repeated batch processes. The method was validated with a growth optimization experiment of E. coli K-12 MG1655 grown with glycerol minimal media as a benchmark. Off-gas analysis enables the continuous estimation of the biomass concentration and growth rate using a black-box model based on first principles (soft sensor). The proposed method led to the same stable growth rates of E. coli with the non-native carbon source glycerol 9.4 times faster than the traditional manual approach with serial passaging in uncontrolled shake flasks and 3.6 times faster than an automated approach on the mL scale. Furthermore, it is shown that the cumulative number of cell divisions (CCD) alone is not a suitable timescale for measuring and comparing evolutionary progress.

Improving tolerance and 1,3-propanediol production of Clostridium butyricum using physical mutagenesis, adaptive evolution and genome shuffling

Bioconversion efficiency of glycerol to 1,3-propanediol (1,3-PD) by Clostridium butyricum is bottlenecked by its low tolerance to various stressors, especially glycerol as the substrate, 1,3-PD as the end product, and butyric acid as a by-product, which eventually decreases 1,3-PD yield. This study aimed at improving the tolerance and 1,3-PD production capability of C. butyricum using random mutagenesis and evolutionary techniques. Mutagenesis of wild strain by atmospheric room temperature plasma (ARTP) provided the first population with maximum tolerance to 160 g/L glycerol, while microbial microdroplet culture system (MMC)-mediated adaptive laboratory evolution (ALE) generated the second population with tolerance to 100 g/L 1,3-PD. Subsequently, genome shuffling of both populations yielded a final strain, GJH-418, which generated 60.12 g/L1,3-PD with a productivity of 1.72 g/L/h. The transcript analysis of the mutant and wild strains revealed the possible involvement of 8 genes in high tolerance and high 1,3-PD production through either up- or down-regulation.

Unravelling Formaldehyde Metabolism in Bacteria: Road towards Synthetic Methylotrophy

Formaldehyde metabolism is prevalent in all organisms, where the accumulation of formaldehyde can be prevented through the activity of dissimilation pathways. Furthermore, formaldehyde assimilatory pathways play a fundamental role in many methylotrophs, which are microorganisms able to build biomass and obtain energy from single- and multicarbon compounds with no carbon–carbon bonds. Here, we describe how formaldehyde is formed in the environment, the mechanisms of its toxicity to the cells, and the cell’s strategies to circumvent it. While their importance is unquestionable for cell survival in formaldehyde rich environments, we present examples of how the modification of native formaldehyde dissimilation pathways in nonmethylotrophic bacteria can be applied to redirect carbon flux toward heterologous, synthetic formaldehyde assimilation pathways introduced into their metabolism. Attempts to engineer methylotrophy into nonmethylotrophic hosts have gained interest in the past decade, with only limited successes leading to the creation of autonomous synthetic methylotrophy. Here, we discuss how native formaldehyde assimilation pathways can additionally be employed as a premise to achieving synthetic methylotrophy. Lastly, we discuss how emerging knowledge on regulation of formaldehyde metabolism can contribute to creating synthetic regulatory circuits applied in metabolic engineering strategies.

Recent advances of integrated microfluidic suspension cell culture system

Microfluidic devices with superior microscale fluid manipulation ability and large integration flexibility offer great advantages of high throughput, parallelisation and multifunctional automation. Such features have been extensively utilised to facilitate cell culture processes such as cell capturing and culturing under controllable and monitored conditions for cell-based assays. Incorporating functional components and microfabricated configurations offered different levels of fluid control and cell manipulation strategies to meet diverse culture demands. This review will discuss the advances of single-phase flow and droplet-based integrated microfluidic suspension cell culture systems and their applications for accelerated bioprocess development, high-throughput cell selection, drug screening and scientific research to insight cell biology. Challenges and future prospects for this dynamically developing field are also highlighted.

Exploration of an Efficient Electroporation System for Heterologous Gene Expression in the Genome of Methanotroph

One-carbon (C1) substrates such as methane and methanol have been considered as the next-generation carbon source in industrial biotechnology with the characteristics of low cost, availability, and bioconvertibility. Recently, methanotrophic bacteria naturally capable of converting C1 substrates have drawn attractive attention for their promising applications in C1-based biomanufacturing for the production of chemicals or fuels. Although genetic tools have been explored for metabolically engineered methanotroph construction, there is still a lack of efficient methods for heterologous gene expression in methanotrophs. Here, a rapid and efficient electroporation method with a high transformation efficiency was developed for a robust methanotroph of Methylomicrobium buryatense 5GB1. Based on the homologous recombination and high transformation efficiency, gene deletion and heterologous gene expression can be simultaneously achieved by direct electroporation of PCR-generated linear DNA fragments. In this study, the influence of several key parameters (competent cell preparation, electroporation condition, recovery time, and antibiotic concentration) on the transformation efficiency was investigated for optimum conditions. The maximum electroporation efficiency of 719 ± 22.5 CFU/μg DNA was reached, which presents a 10-fold improvement. By employing this method, an engineered M. buryatense 5GB1 was constructed to biosynthesize isobutyraldehyde by replacing an endogenous fadE gene in the genome with a heterologous kivd gene. This study provides a potential and efficient strategy and method to facilitate the cell factory construction of methanotrophs.

Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution

Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.