Alleviating glucose repression and enhancing respiratory capacity to increase itaconic acid production

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

Microbial itaconic acid bioproduction towards sustainable development: Insights, challenges, and prospects

Microbial biomanufacturing is a promising approach to produce high-value compounds with low-carbon footprint and significant economic benefits. Among twelve "Top Value-Added Chemicals from Biomass", itaconic acid (IA) stands out as a versatile platform chemical with numerous applications. IA is naturally produced by Aspergillus and Ustilago species through a cascade enzymatic reaction between aconitase (EC 4.2.1.3) and cis-aconitic acid decarboxylase (EC 4.1.1.6). Recently, non-native hosts such as Escherichia coli, Corynebacterium glutamicum, Saccharomyces cerevisiae, and Yarrowia lipolytica have been genetically engineered to produce IA through the introduction of key enzymes. This review provides an up-to-date summary of the progress made in IA bioproduction, from native to engineered hosts, covers in vivo and in vitro approaches, and highlights the prospects of combination tactics. Current challenges and recent endeavors are also addressed to envision comprehensive strategies for renewable IA production in the future towards sustainable development goals (SDGs).