Coupling S-adenosylmethionine-dependent methylation to growth: Design and uses

Emerging methylation-based approaches in microbiome engineering

Bacterial epigenetics, particularly through DNA methylation, exerts significant influence over various biological processes such as DNA replication, uptake, and gene regulation in bacteria. In this review, we explore recent advances in characterizing bacterial epigenomes, accompanied by emerging strategies that harness bacterial epigenetics to elucidate and engineer diverse bacterial species with precision and effectiveness. Furthermore, we delve into the potential of epigenetic modifications to steer microbial functions and influence community dynamics, offering promising opportunities for understanding and modulating microbiomes. Additionally, we investigate the extensive diversity of DNA methyltransferases and emphasize their potential utility in the context of the human microbiome. In summary, this review highlights the potential of DNA methylation as a powerful toolkit for engineering microbiomes.

Meta-analysis Driven Strain Design for Mitigating Oxidative Stresses Important in Biomanufacturing

As the availability of data sets increases, meta-analysis leveraging aggregated and interoperable data types is proving valuable. This study leveraged a meta-analysis workflow to identify mutations that could improve robustness to reactive oxygen species (ROS) stresses using an industrially important melatonin production strain as an example. ROS stresses often occur during cultivation and negatively affect strain performance. Cellular response to ROS is also linked to the SOS response and resistance to pH fluctuations, which is important to strain robustness in large-scale biomanufacturing. This work integrated more than 7000 E. coli adaptive laboratory evolution (ALE) mutations across 59 experiments to statistically associate mutated genes to 2 ROS tolerance ALE conditions from 72 unique conditions. Mutant oxyR, fur, iscR, and ygfZ were significantly associated and hypothesized to contribute fitness in ROS stress. Across these genes, 259 total mutations were inspected in conjunction with transcriptomics from 46 iModulon experiments. Ten mutations were chosen for reintroduction based on mutation clustering and coinciding transcriptional changes as evidence of fitness impact. Strains with mutations reintroduced into oxyR, fur, iscR, and ygfZ exhibited increased tolerance to H2O2 and acid stress and reduced SOS response, all of which are related to ROS. Additionally, new evidence was generated toward understanding the function of ygfZ, an uncharacterized gene. This meta-analysis approach utilized aggregated and interoperable multiomics data sets to identify mutations conferring industrially relevant phenotypes with the least drawbacks, describing an approach for data-driven strain engineering to optimize microbial cell factories.

A growth selection system for sucrose synthases (SuSy): design and test

High throughput screening (HTS) methods of enzyme variants are essential for the development of robust biocatalysts suited for low impact, industrial scale, biobased synthesis of a myriad of compounds. However, for the majority of enzyme classes, current screening methods have limited throughput, or need expensive substrates in combination with sophisticated setups. Here, we present a straightforward, high throughput selection system that couples sucrose synthase activity to growth. Enabling high throughput screening of this enzyme class holds the potential to facilitate the creation of robust variants, which in turn can significantly impact the future of cost effective industrial glycosylation.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Overexpression of HbGRF4 or HbGRF4-HbGIF1 Chimera Improves the Efficiency of Somatic Embryogenesis in Hevea brasiliensis

Transgenic technology is a crucial tool for gene functional analysis and targeted genetic modification in the para rubber tree (Hevea brasiliensis). However, low efficiency of plant regeneration via somatic embryogenesis remains a bottleneck of successful genetic transformation in H. brasiliensis. Enhancing expression of GROWTH-REGULATING FACTOR 4 (GRF4)-GRF-INTERACTING FACTOR 1 (GIF1) has been reported to significantly improve shoot and embryo regeneration in multiple crops. Here, we identified endogenous HbGRF4 and HbGIF1 from the rubber clone Reyan7-33-97, the expressions of which dramatically increased along with somatic embryo (SE) production. Intriguingly, overexpression of HbGRF4 or HbGRF4-HbGIF1 markedly enhanced the efficiency of embryogenesis in two H. brasiliensis callus lines with contrasting rates of SE production. Transcriptional profiling revealed that the genes involved in jasmonic acid response were up-regulated, whereas those in ethylene biosynthesis and response as well as the S-adenosylmethionine-dependent methyltransferase activity were down-regulated in HbGRF4- and HbGRF4-HbGIF1-overexpressing H. brasiliensis embryos. These findings open up a new avenue for improving SE production in rubber tree, and help to unravel the underlying mechanisms of HbGRF4-enhanced somatic embryogenesis.

Overcoming barriers to medium-chain fatty alcohol production

Medium-chain fatty alcohols (mcFaOHs) are aliphatic primary alcohols containing six to twelve carbons that are widely used in materials, pharmaceuticals, and cosmetics. Microbial biosynthesis has been touted as a route to less-abundant chain-length molecules and as a sustainable alternative to current petrochemical processes. Several metabolic engineering strategies for producing mcFaOHs have been demonstrated in the literature, yet processes continue to suffer from poor selectivity and mcFaOH toxicity, leading to reduced titers, rates, and yields of the desired compounds. This opinion examines the current state of microbial mcFaOH biosynthesis, summarizing engineering efforts to tailor selectivity and improve product tolerance by implementing engineering strategies that circumvent or overcome mcFaOH toxicity.

Growth-coupled high throughput selection for directed enzyme evolution

Directed enzyme evolution has revolutionized the rapid development of enzymes with desired properties. However, the lack of a high-throughput method to identify the most suitable variants from a large pool of genetic diversity poses a major bottleneck. To overcome this challenge, growth-coupled in vivo high-throughput selection approaches (GCHTS) have emerged as a novel selection system for enzyme evolution. GCHTS links the survival of the host cell with the properties of the target protein, resulting in a screening system that is easily measurable and has a high throughput-scale limited only by transformation efficiency. This allows for the rapid identification of desired variants from a pool of >109 variants in each experiment. In recent years, GCHTS approaches have been extensively utilized in the directed evolution of multiple enzymes, demonstrating success in catalyzing non-native substrates, enhancing catalytic activity, and acquiring novel functions. This review introduces three main strategies employed to achieve GCHTS: the elimination of toxic compounds via desired variants, enabling host cells to thrive in hazardous conditions; the complementation of an auxotroph with desired variants, where essential genes for cell growth have been eliminated; and the control of the transcription or expression of a reporter gene related to host cell growth, regulated by the desired variants. Additionally, we highlighted the recent developments in the in vivo continuous evolution of enzyme technology, including phage-assisted continuous evolution (PACE) and orthogonal DNA Replication (OrthoRep). Furthermore, this review discusses the challenges and future prospects in the field of growth-coupled selection for protein engineering.

Optimizing human performance in extreme environments through precision medicine: From spaceflight to high-performance operations on Earth

Humans operating in extreme environments often conduct their operations at the edges of the limits of human performance. Sometimes, they are required to push these limits to previously unattained levels. As a result, their margins for error in execution are much smaller than that found in the general public. These same small margins for error that impact execution may also impact risk, safety, health, and even survival. Thus, humans operating in extreme environments have a need for greater refinement in their preparation, training, fitness, and medical care. Precision medicine (PM) is uniquely suited to address the needs of those engaged in these extreme operations because of its depth of molecular analysis, derived precision countermeasures, and ability to match each individual (and his or her specific molecular phenotype) with any given operating context (environment). Herein, we present an overview of a systems approach to PM in extreme environments, which affords clinicians one method to contextualize the inputs, processes, and outputs that can form the basis of a formal practice. For the sake of brevity, this overview is focused on molecular dynamics, while providing only a brief introduction to the also important physiologic and behavioral phenotypes in PM. Moreover, rather than a full review, it highlights important concepts, while using only selected citations to illustrate those concepts. It further explores, by demonstration, the basic principles of using functionally characterized molecular networks to guide the practical application of PM in extreme environments. At its core, PM in extreme environments is about attention to incremental gains and losses in molecular network efficiency that can scale to produce notable changes in health and performance. The aim of this overview is to provide a conceptual overview of one approach to PM in extreme environments, coupled with a selected suite of practical considerations for molecular profiling and countermeasures.

Adaptive Laboratory Evolution of Microorganisms: Methodology and Application for Bioproduction

Adaptive laboratory evolution (ALE) is a useful experimental methodology for fundamental scientific research and industrial applications to create microbial cell factories. By using ALE, cells are adapted to the environment that researchers set based on their objectives through the serial transfer of cell populations in batch cultivations or continuous cultures and the fitness of the cells (i.e., cell growth) under such an environment increases. Then, omics analyses of the evolved mutants, including genome sequencing, transcriptome, proteome and metabolome analyses, are performed. It is expected that researchers can understand the evolutionary adaptation processes, and for industrial applications, researchers can create useful microorganisms that exhibit increased carbon source availability, stress tolerance, and production of target compounds based on omics analysis data. In this review article, the methodologies for ALE in microorganisms are introduced. Moreover, the application of ALE for the creation of useful microorganisms as cell factories has also been introduced.

Recent progress in adaptive laboratory evolution of industrial microorganisms

Adaptive laboratory evolution (ALE) is a technique for the selection of strains with better phenotypes by long-term culture under a specific selection pressure or growth environment. Because ALE does not require detailed knowledge of a variety of complex and interactive metabolic networks, and only needs to simulate natural environmental conditions in the laboratory to design a selection pressure, it has the advantages of broad adaptability, strong practicability, and more convenient transformation of strains. In addition, ALE provides a powerful method for studying the evolutionary forces that change the phenotype, performance, and stability of strains, resulting in more productive industrial strains with beneficial mutations. In recent years, ALE has been widely used in the activation of specific microbial metabolic pathways and phenotypic optimization, the efficient utilization of specific substrates, the optimization of tolerance to toxic substance, and the biosynthesis of target products, which is more conducive to the production of industrial strains with excellent phenotypic characteristics. In this paper, typical examples of ALE applications in the development of industrial strains and the research progress of this technology are reviewed, followed by a discussion of its development prospects.

Pathotypes of Fusarium oxysporum f. sp. fragariae express discrete repertoires of accessory genes and induce distinct host transcriptional responses during root infection

Convergent evolution of phytopathogenicity is poorly described, especially among multiple strains of a single microbial species. We investigated this phenomenon with genetically diverse isolates of Fusarium oxysporum f. sp. fragariae (Fof) that cause one of two syndromes: chlorosis and wilting (the 'yellows-fragariae' pathotype), or only wilting (the 'wilt-fragariae' pathotype). We challenged strawberry (Fragaria × ananassa) plants to root infection by five fungal isolates: three yellows-fragariae, one wilt-fragariae and one that is not pathogenic to strawberry. All Fof isolates had chromosome-level assemblies; three were newly generated. The two pathotypes triggered distinct host responses, especially among phytohormone-associated genes; yellows-fragariae isolates strongly induced jasmonic acid-associated genes, whereas the wilt-fragariae isolate primarily induced ethylene biosynthesis and signalling. The differentially expressed genes on fungal accessory chromosomes were almost entirely distinct between pathotypes. We identified an ~150 kbp 'pathogenicity island' that was horizontally transferred between wilt-fragariae strains. This predicted pathogenicity island was enriched with differentially expressed genes whose predicted functions were related to plant infection, and only one of these genes was also upregulated in planta by yellows-fragariae isolates. These results support the conclusion that wilt- and yellows-fragariae cause physiologically distinct syndromes by the expression of discrete repertoires of genes on accessory chromosomes.

Identification and Engineering of Transporters for Efficient Melatonin Production in Escherichia coli

Transporter discovery and engineering play an important role in cell factory development. Decreasing the intracellular concentration of the product reduces product inhibition and/or toxicity. Lowering intracellular concentrations is especially beneficial for achieving a robust strain at high titers. However, the identification of transporters for xenobiotic chemicals in the host strain is challenging. Here we present a high-throughput workflow to discover Escherichia coli transporters responsible for the efflux of the inhibitory xenobiotic compound melatonin. We took advantage of the Keio collection and screened about 400 transporter knockouts in the presence of a high concentration of melatonin. We found five transporters that when knocked out showed decreased tolerance to melatonin, indicating they are exporters of melatonin. We overexpressed these five genes individually in the production strain and found that one of them, yhjV, encoding a transporter with unknown substrates, resulted in a 27% titer increase in cultivation mimicking fed-batch fermentation. This study demonstrates how microbial cell factories can be improved through transporter identification and engineering. Further, these results lay the foundation for the scale-up of melatonin production in E. coli.

Nondestructive Chemical Sensing within Bulk Soil Using 1000 Biosensors Per Gram of Matrix

Gene expression can be monitored in hard-to-image environmental materials using gas-reporting biosensors, but these outputs have only been applied in autoclaved matrices that are hydrated with rich medium. To better understand the compatibility of indicator gas reporting with environmental samples, we evaluated how matrix hydration affects the gas signal of an engineered microbe added to a sieved soil. A gas-reporting microbe presented a gas signal in a forest soil (Alfisol) when hydrated to an environmentally relevant osmotic pressure. When the gas signal was concentrated prior to analysis, a biosensor titer of 10³ cells/gram of soil produced a significant signal when soil was supplemented with halides. A signal was also observed without halide amendment, but a higher cell titer (10⁶ cells/gram of soil) was required. A sugar-regulated gas biosensor was able to report with a similar level of sensitivity when added to an unsterilized soil matrix, illustrating how gas concentration enables biosensing within a soil containing environmental microbes. These results establish conditions where engineered microbes can report on gene expression in living environmental matrices with decreased perturbation of the soil environment compared to previously reported approaches, using biosensor titers that are orders of magnitude lower than the number of cells typically observed in a gram of soil.

Methyltransferases, functions and applications

In this review the current state‐of‐the‐art of S‐adenosylmethionine (SAM)‐dependent methyltransferases and SAM are evaluated. Their structural classification and diversity is introduced and key mechanistic aspects presented which are then detailed further. Then, catalytic SAM as a target for drugs, and approaches to utilise SAM as a cofactor in synthesis are introduced with different supply and regeneration approaches evaluated. The use of SAM analogues are also described. Finally O‐, N‐, C‐ and S‐MTs, their synthetic applications and potential for compound diversification is given.

Insights into the Ancient Adaptation to Intertidal Environments by Red Algae Based on a Genomic and Multiomics Investigation of Neoporphyra haitanensis

Colonization of land from marine environments was a major transition for biological life on Earth, and intertidal adaptation was a key evolutionary event in the transition from marine- to land-based lifestyles. Multicellular intertidal red algae exhibit the earliest, systematic, and successful adaptation to intertidal environments, with Porphyra sensu lato (Bangiales, Rhodophyta) being a typical example. Here, a chromosome-level 49.67 Mb genome for Neoporphyra haitanensis comprising 9,496 gene loci is described based on metagenome-Hi-C-assisted whole-genome assembly, which allowed the isolation of epiphytic bacterial genome sequences from a seaweed genome for the first time. The compact, function-rich N. haitanensis genome revealed that ancestral lineages of red algae share common horizontal gene transfer events and close relationships with epiphytic bacterial populations. Specifically, the ancestor of N. haitanensis obtained unique lipoxygenase family genes from bacteria for complex chemical defense, carbonic anhydrases for survival in shell-borne conchocelis lifestyle stages, and numerous genes involved in stress tolerance. Combined proteomic, transcriptomic, and metabolomic analyses revealed complex regulation of rapid responses to intertidal dehydration/rehydration cycling within N. haitanensis. These adaptations include rapid regulation of its photosynthetic system, a readily available capacity to utilize ribosomal stores, increased methylation activity to rapidly synthesize proteins, and a strong anti-oxidation system to dissipate excess redox energy upon exposure to air. These novel insights into the unique adaptations of red algae to intertidal lifestyles inform our understanding of adaptations to intertidal ecosystems and the unique evolutionary steps required for intertidal colonization by biological life.

Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological systems. Adaptive Laboratory Evolution (ALE) leverages nature’s problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study describes how novel strain designs, or genome sequences not yet observed in ALE experiments or published designs, can be extracted from aggregated ALE data and demonstrates this by designing, building, and testing three novel Escherichia coli strains with fitnesses comparable to ALE mutants. These designs were achieved through a meta-analysis of aggregated ALE mutations data (63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13 957 observed mutations), which additionally revealed global ALE mutation trends that inform on ALE-derived strain design principles. Such informative trends anticipate ALE-derived strain designs as largely gene-centric, as opposed to noncoding, and composed of a relatively small number of beneficial variants (approximately 6). These results demonstrate how strain design efforts can be enhanced by the meta-analysis of aggregated ALE data.

Cholecalciferol and metformin protect against lipopolysaccharide-induced endothelial dysfunction and senescence by modulating sirtuin-1 and protein arginine methyltransferase-1

Endothelial cell activation through nuclear factor-kappa-B (NFkB) and mitogen-activated protein kinases leads to increased biosynthesis of pro-inflammatory mediators, cellular injury and vascular inflammation under lipopolysaccharide (LPS) exposure. Recent studies report that LPS up-regulated global methyltransferase activity. In this study, we observed that a combination treatment with metformin (MET) and cholecalciferol (VD) blocked the LPS-induced S-adenosylmethionine (SAM)-dependent methyltransferase (SDM) activity in Eahy926 cells. We found that LPS challenge (i) increased arginine methylation through up-regulated protein arginine methyltransferase-1 (PRMT1) mRNA, intracellular concentrations of asymmetric dimethylarginine (ADMA) and homocysteine (HCY); (ii) up-regulated cell senescence through mitigated sirtuin-1 (SIRT1) mRNA, nicotinamide adenine dinucleotide (NAD+) concentration, telomerase activity and total antioxidant capacity; and (iii) lead to endothelial dysfunction through compromised nitric oxide (NOx) production. However, these LPS-mediated cellular events in Eahy926 cells were restored by the synergistic effect of MET and VD. Taken together, this study identified that the dual compound effect inhibits LPS-induced protein arginine methylation, endothelial senescence and dysfunction through the components of epigenetic machinery, SIRT1 and PRMT1, which is a previously unidentified function of the test compounds. In silico results identified the presence of vitamin D response element (VDRE) sequence on PRMT1 suggesting that VDR could regulate PRMT1 gene expression. Further characterization of the cellular events associated with the dual compound challenge, using gene silencing approach or adenoviral constructs for SIRT1 and/or PRMT1 under inflammatory stress, could identify therapeutic strategies to address the endothelial consequences in vascular inflammation-mediated atherosclerosis.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Prophage Tracer: precisely tracing prophages in prokaryotic genomes using overlapping split-read alignment

The life cycle of temperate phages includes a lysogenic cycle stage when the phage integrates into the host genome and becomes a prophage. However, the identification of prophages that are highly divergent from known phages remains challenging. In this study, by taking advantage of the lysis-lysogeny switch of temperate phages, we designed Prophage Tracer, a tool for recognizing active prophages in prokaryotic genomes using short-read sequencing data, independent of phage gene similarity searching. Prophage Tracer uses the criterion of overlapping split-read alignment to recognize discriminative reads that contain bacterial (attB) and phage (attP) att sites representing prophage excision signals. Performance testing showed that Prophage Tracer could predict known prophages with precise boundaries, as well as novel prophages. Two novel prophages, dsDNA and ssDNA, encoding highly divergent major capsid proteins, were identified in coral-associated bacteria. Prophage Tracer is a reliable data mining tool for the identification of novel temperate phages and mobile genetic elements. The code for the Prophage Tracer is publicly available at https://github.com/WangLab-SCSIO/Prophage_Tracer.

Biotechnological applications of S-adenosyl-methionine-dependent methyltransferases for natural products biosynthesis and diversification

In the biosynthesis of natural products, methylation is a common and essential transformation to alter molecules' bioavailability and bioactivity. The main methylation reaction is performed by S-adenosylmethionine (SAM)-dependent methyltransferases (MTs). With advancements in genomic and chemical profiling technologies, novel MTs have been discovered to accept complex substrates and synthesize industrially valuable natural products. However, to achieve a high yield of small molecules in microbial hosts, many methyltransferase activities have been reported to be insufficient. Moreover, inadequate co-factor supplies and feedback inhibition of the by-product, S-adenosylhomocysteine (SAH), further limit MTs' activities. Here, we review recent advances in SAM-dependent MTs to produce and diversify natural products. First, we surveyed recently identified novel methyltransferases in natural product biosynthesis. Second, we summarized enzyme engineering strategies to improve methyltransferase activity, with a particular focus on high-throughput assay design and application. Finally, we reviewed innovations in co-factor regeneration and diversification, both in vitro and in vivo. Noteworthily, many MTs are able to accept multiple structurally similar substrates. Such promiscuous methyltransferases are versatile and can be tailored to design de novo pathways to produce molecules whose biosynthetic pathway is unknown or non-existent in nature, thus broadening the scope of biosynthesized functional molecules.

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.

Diversity of the reaction mechanisms of SAM-dependent enzymes

S-adenosylmethionine (SAM) is ubiquitous in living organisms and is of great significance in metabolism as a cofactor of various enzymes. Methyltransferases (MTases), a major group of SAM-dependent enzymes, catalyze methyl transfer from SAM to C, O, N, and S atoms in small-molecule secondary metabolites and macromolecules, including proteins and nucleic acids. MTases have long been a hot topic in biomedical research because of their crucial role in epigenetic regulation of macromolecules and biosynthesis of natural products with prolific pharmacological moieties. However, another group of SAM-dependent enzymes, sharing similar core domains with MTases, can catalyze nonmethylation reactions and have multiple functions. Herein, we mainly describe the nonmethylation reactions of SAM-dependent enzymes in biosynthesis. First, we compare the structural and mechanistic similarities and distinctions between SAM-dependent MTases and the non-methylating SAM-dependent enzymes. Second, we summarize the reactions catalyzed by these enzymes and explore the mechanisms. Finally, we discuss the structural conservation and catalytical diversity of class I-like non-methylating SAM-dependent enzymes and propose a possibility in enzymes evolution, suggesting future perspectives for enzyme-mediated chemistry and biotechnology, which will help the development of new methods for drug synthesis.

Dietary Change Enables Robust Growth-Coupling of Heterologous Methyltransferase Activity in Yeast

Genetic modifications of living organisms and proteins are made possible by a catalogue of molecular and synthetic biology tools, yet proper screening assays for genetic variants of interest continue to lag behind. Synthetic growth-coupling (GC) of enzyme activities offers a simple, inexpensive way to track such improvements. In this follow-up study we present the optimization of a recently established GC design for screening of heterologous methyltransferases (MTases) and related pathways in the yeast Saccharomyces cerevisiae. Specifically, upon testing different media compositions and genetic backgrounds, improved GC of different heterologous MTase activities is obtained. Furthermore, we demonstrate the strength of the system by screening a library of catechol O-MTase variants converting protocatechuic acid into vanillic acid. We demonstrated high correlation (R² = 0.775) between vanillic acid and cell density as a proxy for MTase activity. We envision that the improved MTase GC can aid evolution-guided optimization of biobased production processes for methylated compounds with yeast in the future.

Innovative Tools and Strategies for Optimizing Yeast Cell Factories

Metabolic engineering (ME) aims to develop efficient microbial cell factories that can produce a wide variety of valuable compounds, ideally at the highest yield and from various feedstocks. We summarize recent developments in ME methods for tailoring different yeast cell factories (YCFs). In particular, we highlight the most timely and cutting-edge molecular tools and strategies for biosynthetic pathway optimization (including genome-editing tools), combinatorial transcriptional and post-transcriptional engineering (cis/trans regulators), dynamic control of metabolic fluxes (e.g., rewiring of primary metabolism), and spatial reconfiguration of metabolic pathways. Finally, we discuss challenges and perspectives for adaptive laboratory evolution (ALE) of yeast to advance ME of microbial cell factories.

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

Novel genes associated with folic acid-mediated metabolism in mouse: A bioinformatics study

Folic acid plays an essential role in the central nervous system and cancer. This study aimed to screen genes related to folic acid metabolism. Datasets (GSE80587, GSE65267 and GSE116299) correlated to folic acid were screened in the Gene Expression Omnibus. Weighed gene co-expression network analysis was performed to identify modules associated with sample traits of folic acid and organs (brain, prostate and kidney). Functional enrichment analysis was performed for the eigengenes in modules that were significantly correlated with sample traits. Accordingly, the hub genes and key nodes in the modules were identified using the protein interaction network. A total of 17,252 genes in three datasets were identified. One module, which included 97 genes that were highly correlated with sample traits (including folic acid treatment [cor = -0.57, P = 3e-04] and kidney [cor = -0.68, p = 4e-06]), was screened out. Hub genes, including tetratricopeptide repeat protein 38 (Ttc38) and miR-185, as well as those (including Sema3A, Insl3, Dll1, Msh4 and Snai1) associated with "neuropilin binding", "regulation of reproductive process" and "vitamin D metabolic process", were identified. Genes, including Ttc38, Sema3A, Insl3, Dll1, Msh4 and Snai1, were the novel factors that may be associated with the development of the kidneys and related to folic acid treatment.

Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

Microbial Synthesis of Human-Hormone Melatonin at Gram Scales

Melatonin is a commercially attractive tryptophan-derived hormone. Here we describe a bioprocess for the production of melatonin using Escherichia coli to high titers. The first engineered strain produced 0.13 g/L of melatonin from tryptophan under fed-batch fermentation conditions. A 4-fold improvement on melatonin titer was further achieved by (1) protein engineering of rate-limiting tryptophan hydroxylase to improve 5-hydroxytryptophan biosynthesis and (2) chromosomal integration of aromatic-amino-acid decarboxylase to limit byproduct formation and to minimize gene toxicity to the host cell. Fermentation optimization improved melatonin titer by an additional 2-fold. Deletion of yddG, a tryptophan exporter, exhibited an additive beneficial effect. The final engineered strain produced ∼2.0 g/L of melatonin with tryptophan supplemented externally and ∼1.0 g/L with glucose as the sole carbon source for tryptophan supply. This study lays the foundation for further developing a commercial melatonin-producing E. coli strain.

Directed Metabolic Pathway Evolution Enables Functional Pterin-Dependent Aromatic-Amino-Acid Hydroxylation in Escherichia coli

Tetrahydrobiopterin-dependent hydroxylation of aromatic amino acids is the first step in the biosynthesis of many neuroactive compounds in humans. A fundamental challenge in building these pathways in Escherichia coli is the provision of the non-native hydroxylase cofactor, tetrahydrobiopterin. To solve this, we designed a genetic selection that relies on the tyrosine synthesis activity of phenylalanine hydroxylase. Using adaptive laboratory evolution, we demonstrate the use of this selection to discover: (1) a minimum set of heterologous enzymes and a host folE (T198I) mutation for achieving this type of hydroxylation chemistry in whole cells, (2) functional complementation of tetrahydrobiopterin by indigenous cofactors, and (3) a tryptophan hydroxylase mutation for improving protein abundance. Thus, the goal of having functional aromatic-amino-acid hydroxylation in E. coli was achieved through directed metabolic pathway evolution.

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.