Pyruvate is a hub of various endogenous metabolic pathways, including glycolysis, TCA cycle, amino acid, and fatty acid biosynthesis. It has also been used as a precursor for pyruvate-derived compounds such as acetoin, 2,3-butanediol (2,3-BD), butanol, butyrate, and L-alanine biosynthesis. Pyruvate and derivatives are widely utilized in food, pharmaceuticals, pesticides, feed additives, and bioenergy industries. However, compounds such as pyruvate, acetoin, and butanol are often chemically synthesized from fossil feedstocks, resulting in declining fossil fuels and increasing environmental pollution. Metabolic engineering is a powerful tool for producing eco-friendly chemicals from renewable biomass resources through microbial fermentation. Here, we review and systematically summarize recent advances in the biosynthesis pathways, regulatory mechanisms, and metabolic engineering strategies for pyruvate and derivatives. Furthermore, the establishment of sustainable industrial synthesis platforms based on alternative substrates and new tools to produce these compounds is elaborated. Finally, we discuss the potential difficulties in the current metabolic engineering of pyruvate and derivatives and promising strategies for constructing efficient producers.

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.

The phosphoenol pyruvate–oxaloacetate–pyruvate-derived amino acids (POP-AAs) comprise native intermediates in cellular metabolism, within which the phosphoenol pyruvate–oxaloacetate–pyruvate (POP) node is the switch point among the major metabolic pathways existing in most living organisms. POP-AAs have widespread applications in the nutrition, food, and pharmaceutical industries. These amino acids have been predominantly produced in Escherichia coli and Corynebacterium glutamicum through microbial fermentation. With the rapid increase in market requirements, along with the global food shortage situation, the industrial production capacity of these two bacteria has encountered two bottlenecks: low product conversion efficiency and high cost of raw materials. Aiming to push forward the update and upgrade of engineered strains with higher yield and productivity, this paper presents a comprehensive summarization of the fundamental strategy of metabolic engineering techniques around phosphoenol pyruvate–oxaloacetate–pyruvate node for POP-AA production, including L-tryptophan, L-tyrosine, L-phenylalanine, L-valine, L-lysine, L-threonine, and L-isoleucine. Novel heterologous routes and regulation methods regarding the carbon flux redistribution in the POP node and the formation of amino acids should be taken into consideration to improve POP-AA production to approach maximum theoretical values. Furthermore, an outlook for future strategies of low-cost feedstock and energy utilization for developing amino acid overproducers is proposed.

Glucaric acid (GA) is a value-added chemical and can be used to manufacture food additives, anticancer drugs, and polymers. The non-genetic cell-to-cell variations in GA biosynthesis are naturally inherent, indicating the presence of both high- and low-performance cells in culture. Low-performance cells can lead to nutrient waste and inefficient production. Furthermore, myo -inositol oxygenase (MIOX) is a key rate-limiting enzyme with the problem of low stability and activity in GA production. Therefore, eliminating cell-to-cell variations and increasing MIOX stability can select high-performance cells and improve GA production. In this study, an in vivo GA bioselector was constructed based on GA biosensor and tetracycline efflux pump protein TetA to continuously select GA-efficient production strains. Additionally, the upper limit of the GA biosensor was improved to 40 g/L based on ribosome-binding site optimization, achieving efficient enrichment of GA high-performance cells. A small ubiquitin-like modifier (SUMO) enhanced MIOX stability and activity. Overall, we used the GA bioselector and SUMO-MIOX fusion in fed-batch GA production and achieved a 5.52-g/L titer in Escherichia coli , which was 17-fold higher than that of the original strain. IMPORTANCE: Glucaric acid is a non-toxic valuable product that was mainly synthesized by chemical methods. Due to the problems of non-selectivity, inefficiency, and environmental pollution, GA biosynthesis has attracted significant attention. The non-genetic cell-to-cell variations and MIOX stability were both critical factors for GA production. In addition, the high detection limit of the GA biosensor was a key condition for performing high-throughput screening of GA-efficient production strains. To increase GA titer, this work eliminated the cell-to-cell variations by GA bioselector constructed based on GA biosensor and TetA, and improved the stability and activity of MIOX in the GA biosynthetic pathway through fusing the SUMO to MIOX. Finally, these approaches improved the GA production by 17-fold to 5.52 g/L at 65 h. This study represents a significant step toward the industrial application of GA biosynthetic pathways in E. coli .

Gibberellic acids (GAs) are a class of tetracyclic diterpene carboxylic acid compounds produced by green plants, fungi, and bacteria, which have a wide range of applications in agricultural production and food ingredients processing. Owing to the continuously growing market demand, enhancing GA yield has become imperative. The biosynthesis of GAs is a multi-enzymatic synergistic process that can be enhanced through genetic and metabolic engineering strategies. In this review, we first summarize recent advances in GA production by Fusarium fujikuroi. We then highlight key metabolic engineering strategies, including biosynthetic pathway engineering, cluster-specific channeling of geranylgeranyl diphosphate biosynthesis, cofactor engineering, as well as regulatory mechanisms involving nitrogen modulation and histone modification. Finally, we discuss promising approaches for constructing high-efficiency microbial cell factories, such as implementation of the CRISPR/Cas9 system, the application of strong promoters, the development of target-specific technologies for small molecules, and the employment of genome-scale metabolic models. Recent metabolic engineering efforts have achieved GA3 titers of up to 3.16 g/L through multi-target nitrogen regulation strategies, highlighting the potential for further yield improvement.