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β-Farnesene is an advanced molecule with promising applications in agriculture, the cosmetics industry, pharmaceuticals, and bioenergy. To supplement the shortcomings of rational design in the development of high-producing β-farnesene strains, a Metabolic Pathway Design-Fermentation Test-Metabolomic Analysis-Target Mining experimental cycle was designed. In this study, by over-adding 20 different amino acids/nucleobases to induce fluctuations in the production of β-farnesene, the changes in intracellular metabolites in the β-farnesene titer-increased group were analyzed using non-targeted metabolomics. Differential metabolites that were detected in each experimental group were selected, and their metabolic pathways were located. Based on these differential metabolites, targeted strain gene editing and culture medium optimization were performed. The overexpression of the coenzyme A synthesis-related gene pantothenate kinase (PanK) and the addition of four mixed water-soluble vitamins in the culture medium increased the β-farnesene titer in the shake flask to 1054.8 mg/L, a 48.5% increase from the initial strain. In the subsequent fed-batch fermentation, the β-farnesene titer further reached 24.6 g/L. This work demonstrates the tremendous application value of metabolomics analysis for the development of industrial recombinant strains and the optimization of fermentation conditions. [ABSTRACT FROM AUTHOR]

The ever-increasing global energy demand, juxtaposed with critical concerns about greenhouse gas emissions, emphatically underscores the urgency to pivot toward sustainable and eco-friendly energy alternatives. Tapping into microbial metabolism for clean energy generation stands out as a particularly promising avenue in this endeavor. Given this backdrop, we delved deeply into the metabolic engineering potential of Saccharomyces cerevisiae, thereby aiming for the bioconversion of formate and acetate—both CO2 derivatives—into free fatty acids (FFAs) as precursors for biofuel production. Our study not only elucidated the metabolic pathways within S. cerevisiae that are tailored for efficient formate and acetate utilization but also shone a light on the meticulous optimization strategies that amplify FFA synthesis. The engineered strains, under refined conditions, exhibited up to an 8-fold increase in an FFA titer, thus reaching a production level of 6.6 g/L, which showcases the potential of microbial metabolism in clean energy generation. Our findings offer a promising step toward harnessing microbial metabolism for sustainable energy production, thereby bridging the gap between waste carbon utilization and greener fuel alternatives. [ABSTRACT FROM AUTHOR]

The green biosynthesis of adipic acid, an important monomer of nylon 6,6, has become a research hotspot. α-Aminoadipate is a key intermediate in the metabolic pathway that converts L-lysine to produce adipic acid. In addition, metabolic flux analysis has become an important part of metabolic engineering. Many metabolic optimization algorithms have been developed to predict engineering intervention strategies with the aim of improving the production of target chemicals. Here, OptHandle, a new metabolic optimization algorithm, has been developed. And, we use OptHandle to optimize the biosynthesis of α-aminoadipate. Based on the results of OptHandle, an engineered Escherichia coli with a 13-fold higher titer was obtained, and 1.10 ± 0.02 g/L of α-aminoadipate was produced. The efficient synthesis of α-aminoadipate lays a foundation for the green production of adipic acid. [ABSTRACT FROM AUTHOR]

Different plasticizers have obvious differences in plasticizing properties. As one of the important indicators for evaluating plasticization performance, the substitution factor (SF) has great significance for product cost accounting. In this research, a genetic algorithm with "variable mutation probability" was developed to screen the key molecular descriptors of plasticizers that are highly correlated with the SF, and a SF prediction model was established based on these filtered molecular descriptors. The results show that the improved genetic algorithm greatly improved the prediction accuracy in different regression models. The coefficient of determination (R2) for the test set and the cross-validation both reached 0.92, which is at least 0.15 higher than the R2 of the unimproved genetic algorithm. From the results of the selected descriptors, most of the descriptors focused on describing the branching of the molecule, which is consistent with the view that the branching chain plays an important role in the plasticization process. As the first study to establish the relationship between plasticizer SF and plasticizer molecular structure, this work provides a basis for subsequent plasticizer performance and evaluation system modeling. [ABSTRACT FROM AUTHOR]