Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling

Microalgal metabolic engineering facilitates precision nutrition and dietary regulation

Microalgae have gained considerable attention as promising candidates for precision nutrition and dietary regulation due to their versatile metabolic capabilities. This review innovatively applies system metabolic engineering to utilize microalgae for precision nutrition and sustainable diets, encompassing the construction of microalgal cell factories, cell cultivation and practical application of microalgae. Manipulating the metabolic pathways and key metabolites of microalgae through multi-omics analysis and employing advanced metabolic engineering strategies, including ZFNs, TALENs, and the CRISPR/Cas system, enhances the production of valuable bioactive compounds, such as omega-3 fatty acids, antioxidants, and essential amino acids. This work begins by providing an overview of the metabolic diversity of microalgae and their ability to thrive in diverse environmental conditions. It then delves into the principles and strategies of metabolic engineering, emphasizing the genetic modifications employed to optimize microalgal strains for enhanced nutritional content. Enhancing PSY, BKT, and CHYB benefits carotenoid synthesis, whereas boosting ACCase, fatty acid desaturases, and elongases promotes polyunsaturated fatty acid production. Here, advancements in synthetic biology, evolutionary biology and machine learning are discussed, offering insights into the precision and efficiency of metabolic pathway manipulation. Also, this review highlights the potential impact of microalgal precision nutrition on human health and aquaculture. The optimized microalgal strains could serve as sustainable and cost-effective sources of nutrition for both human consumption and aquaculture feed, addressing the growing demand for functional foods and environmentally friendly feed alternatives. The tailored microalgal strains are anticipated to play a crucial role in meeting the nutritional needs of diverse populations and contributing to sustainable food production systems.

Molecular design of microalgae as sustainable cell factories

Microalgae are regarded as sustainable and potent chassis for biotechnology. Their capacity for efficient photosynthesis fuels dynamic growth independent from organic carbon sources and converts atmospheric CO2 directly into various valuable hydrocarbon-based metabolites. However, approaches to gene expression and metabolic regulation have been inferior to those in more established heterotrophs (e.g., prokaryotes or yeast) since the genetic tools and insights in expression regulation have been distinctly less advanced. In recent years, however, these tools and their efficiency have dramatically improved. Various examples have demonstrated new trends in microalgal biotechnology and the potential of microalgae for the transition towards a sustainable bioeconomy.

Engineering highly productive cyanobacteria towards carbon negative emissions technologies

Cyanobacteria are a diverse and ecologically important group of photosynthetic prokaryotes that contribute significantly to the global carbon cycle through the capture of CO2 as biomass. Cyanobacterial biotechnology could play a key role in a sustainable bioeconomy through negative emissions technologies (NETs), such as carbon sequestration or bioproduction. However, the primary issues of low productivities and high infrastructure costs currently limit the commercialisation of such applications. The isolation of several fast-growing strains and recent advancements in molecular biology tools now offer promising new avenues for improving yields, including metabolic engineering approaches guided by high-throughput screening and metabolic models. Furthermore, emerging research on engineering coculture communities could help to develop more robust culturing systems to support broader NET applications.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

The Specificities of Lysophosphatidic Acid Acyltransferase and Fatty Acid Desaturase Determine the High Content of Myristic and Myristoleic Acids in Cyanobacterium sp. IPPAS B-1200

The cyanobacterial strain Cyanobacterium sp. IPPAS B-1200 isolated from Lake Balkhash is characterized by high relative amounts of myristic (30%) and myristoleic (10%) acids. The remaining fatty acids (FAs) are represented mainly by palmitic (20%) and palmitoleic (40%) acids. We expressed the genes for lysophosphatidic acid acyltransferase (LPAAT; EC 2.3.1.51) and Δ9 fatty acid desaturase (FAD; EC 1.14.19.1) from Cyanobacterium sp. IPPAS B-1200 in Synechococcus elongatus PCC 7942, which synthesizes myristic and myristoleic acids at the level of 0.5-1% and produces mainly palmitic (~60%) and palmitoleic (35%) acids. S. elongatus cells that expressed foreign LPAAT synthesized myristic acid at 26%, but did not produce myristoleic acid, suggesting that Δ9-FAD of S. elongatus cannot desaturate FAs with chain lengths less than C16. Synechococcus cells that co-expressed LPAAT and Δ9-FAD of Cyanobacterium synthesized up to 45% palmitoleic and 9% myristoleic acid, suggesting that Δ9-FAD of Cyanobacterium is capable of desaturating saturated acyl chains of any length.

Model validation and selection in metabolic flux analysis and flux balance analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.