Chao Ye

Oleanane-type pentacyclic triterpenoids are a class of naturally occurring specialized metabolites with diverse biological activities. Conventional plant extraction and chemical synthesis are constrained by low yields, high costs, and a reliance on natural resources. With the development of synthetic biology and metabolic engineering, the biosynthetic pathways of these compounds can now be systematically reconstructed and optimized, providing new opportunities for efficient production. This review summarizes the classification of oleanane-type pentacyclic triterpenoids and outlines their biosynthetic pathways and recent progress in production platforms such as plant in vitro culture and microbial cell factories. In addition, we discuss major engineering strategies used to improve triterpenoid production, including increasing precursor supply, reducing competing pathways, implementing enzyme engineering and engineering cofactors, improving protein performance, subcellular compartmentalization, enhancing transport, and optimizing fermentation conditions. In view of the current progress, this review also identifies persistent challenges in the biosynthesis of these compounds and outlines potential strategies to address them.

L-Threonine is a high-value amino acid widely used in the feed, food, and pharmaceutical industries, but its industrial production is limited by low yield. In this study, whole-genome sequencing of industrial strain Thr1 was performed, and the genome-scale metabolic model GSMM-iJHL1061 was reconstructed. The model identified 54 CO2-related reactions and predicted 0.89 mol CO2 release per mol L-threonine synthesized, mainly from the PPP and TCA cycle. Attenuation of decarboxylation reactions involving ilvBN, lysA, gnd, and icd, combined with RuBisCO- and cynT-pyc-based CO2 fixation, increased the yield to 0.66 g/g. Deletion of 166 non-essential genes generated Thr9, increasing the yield to 0.67 g/g. Further introduction of maltose hydrolysis and sucrose utilization systems generated Thr10, which produced 132.5 g/L L-threonine in a 1000-L fermenter, with a yield of 0.68 g/g. Overall, L-threonine yield was substantially improved through systems metabolic engineering, providing a useful reference for constructing high-yield microbial cell factories.

Terpenoids are valuable natural products that are widely used in medicine, agriculture, energy, and food. Traditional production by plant extraction or chemical synthesis is inefficient, costly, and polluting. Microbial fermentation via synthetic biology offers a greener alternative but faces challenges such as metabolic flux competition, cofactor imbalance, and product toxicity that limit yields. Genome-scale metabolic models (GSMMs), as essential tools in systems biology, can provide computational guidance for the rational design. This paper systematically reviews the progress of GSMMs in four typical terpenoid-producing microorganisms: the model microorganisms Escherichia coli and Saccharomyces cerevisiae, as well as the nonmodel microorganisms cyanobacteria and Yarrowia lipolytica. It focuses on their applications in fermentation process optimization and metabolic engineering strategies. Furthermore, future development directions, such as multiconstraint models and the integration of machine learning with synthetic biology, are discussed, aiming to provide a theoretical reference for the intelligent design and efficient construction of terpenoid cell factories.

Abscisic acid (ABA) is a key plant growth regulator widely used in agriculture and ecological restoration. Although metabolic engineering of the fungus Botrytis cinerea can enhance ABA production, it has been hindered by inefficient genetic tools. In this study, we first established a recyclable selection marker system in B. cinerea based on orotidine-5'-phosphate decarboxylase. Subsequently, the CRISPR/Cas9 system was optimized, achieving up to 100% editing efficiency, far surpassing traditional homologous recombination. Based on this platform, multiple metabolic engineering strategies were systematically explored to enhance ABA biosynthesis. Increasing acetyl-CoA supply, inhibiting squalene synthesis, and knocking out key secondary metabolism genes Bcpks12 and Bcphs1 all significantly promoted ABA accumulation. Notably, co-overexpression of Bcacly1 and Bcacly2 combined with 1 g/L citrate increased ABA production to 1.36 g/L, representing a 38.66% improvement. Overall, this study provides an efficient genetic toolkit and a solid foundation for the industrial-scale production of ABA via engineered B. cinerea.

1,3-Propanediol (1,3-PD) is a notable industrial compound used in applications in materials, chemicals, pharmaceuticals, and the light industry. Because it is environmentally friendly and sustainable, the 1,3-PD biogenic synthesis has drawn a lot of attention recently. More economically viable microbial cell factories, engineering operations, and downstream processing technologies will be required to lower the 1,3-PD biological process expenses. First, the selection of suitable strains for constructing microbial cell factories for 1,3-PD is crucial for its efficient biogenic synthesis. The predominant 1,3-PD producing strains primarily include wild-type and heterologous producers. To boost the performance of production strains, a number of advanced genetic engineering methods have already been used, involving optimizing metabolic pathways, improving cofactor availability, enhancing transport systems, and developing new pathways. However, achieving large-scale industrial production solely through improving the performance of the production strains is challenging. To scale up production from the laboratory scale, various operational strategies have been developed, including cosubstrate cultivation, two-stage fermentation strategies, microbial cocultivation systems, Artificial Intelligence-assisted fed-batch fermentation technologies, and scale-up technologies. Furthermore, developing an economically efficient downstream technique is critical for the commercial manufacturing of 1,3-PD. The purpose of this review is to offer an in-depth comprehension of 1,3-PD, offering valuable reference information for researchers.

Abscisic acid (ABA), a pivotal plant growth regulator, is extensively utilized in agricultural production, ecological vegetation restoration, and landscape greening. Although Botrytis cinerea remains the primary strain for industrial ABA production, its suboptimal efficiency limits large-scale applications. Recent advancements have elucidated the biosynthetic pathway of ABA. Coupled with the rapid development of genetic engineering technologies, it is now feasible to enhance ABA productivity through rational strain optimization. This review summarizes an in-depth analysis of recent advancements in the field of ABA biosynthesis, encompassing its biosynthetic mechanisms, enhancement strategies, and functional applications. Additionally, we highlight significant advancements in the molecular biotechnology of B. cinerea as the primary ABA-producing strain. In summary, this review offers valuable insights and references for the industrial production and application of ABA, laying a foundation for continued developments in ABA research and industry.

The wide application of machine learning has provided more possibilities for biological manufacturing, and the combination of machine learning and synthetic biology technology has ignited even more brilliant sparks, which has created an unpredictable value for the upgrading of microbial cell factories. The review delves into the synergies between machine learning and synthetic biology to create research worth investigating in biotechnology. We explore relevant databases, toolboxes, and machine learning–derived models. Furthermore, we examine specific applications of this combined approach in chemical production, human health, and environmental remediation. By elucidating these successful integrations, this review aims to provide valuable guidance for future research at the intersection of biomanufacturing and artificial intelligence. Graphical Abstract

Oleaginous microorganisms have the unique ability to accumulate lipids that can exceed 20% of their dry cell weight under certain conditions. Despite their potential for efficient lipid production, the metabolic pathways involved are not yet fully understood, largely due to the complexity of intracellular processes and the challenges in phenotypic prediction. This review synthesizes the latest research on the application of Genome‐scale Metabolic Network Models (GSMMs) to study oleaginous microorganisms, including bacteria, cyanobacteria, yeast, microalgae, and fungi, and provides a comprehensive analysis of how GSMMs have been utilized to decipher the metabolic mechanisms behind lipid accumulation and to identify key genes involved in lipid synthesis. The review highlights the role of GSMMs in predicting cellular behavior, optimizing metabolic engineering strategies, and discusses the future directions and potential of GSMMs in enhancing lipid production in microorganisms. This comprehensive overview not only summarizes the current state of research but also identifies gaps and opportunities for further investigation in the field.