Advances in multi-omics technologies for identifying metabolic engineering targets and improving lipid production in microalgae

Polyunsaturated fatty acids (PUFAs), such as γ-linolenic acid, arachidonic acid, eicosapentaenoic acid, and docosahexaenoic acid, are highly valued in the global market due to their physiological effects and health benefits. Concerns related to overfishing and marine ecosystem degradation have driven interest in microalgal lipids as a sustainable and eco-friendly alternative for PUFA production. Despite some success in commercializing microalgal lipid products, they still fail to meet global demand. Advances in high-throughput omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have deepened the understanding of lipid biosynthesis in microalgae. This review explores the potential of multi-omics approaches to elucidate PUFA biosynthesis pathways, identify key regulatory genes, and optimize metabolic engineering strategies for enhanced lipid production. Additionally, this review discusses how multi-omics technologies address challenges in large-scale cultivation, promoting the industrialization of microalgal lipid productions. These insights provide a foundation for improving microalgal PUFA yields to meet growing global demand.

Intelligent biomanufacturing of water-soluble vitamins

Given the crucial role of water-soluble vitamins in the human body and the rising demand for natural sources, their biosynthesis has gained the attention of researchers. This review offers a comprehensive look at recent progress in water-soluble vitamin biosynthesis, emphasizing synthetic biotechnology for green biomanufacturing. Specifically, it encompasses the optimization of biological components, pathways, and systems, as well as energy metabolism regulation, stress-tolerance enhancement, high-throughput screening, and the upscaling of production processes. It also envisages intelligent biomanufacturing platforms, highlighting the role of systems biology and artificial intelligence (AI), and proposes future research directions, such as integrating AI-driven metabolic models, enzyme engineering, and cell-free systems, to address limitations in the efficiency, toxicity, and scalability of water-soluble vitamin production.

Thraustochytrids: Evolution, Ultrastructure, Biotechnology, and Modeling

The thraustochytrids are a group of marine protists known for their significant ecological roles as decomposers and parasites as well as for their potential biotechnological applications, yet their evolutionary and structural diversity remains poorly understood. Our review critically examines the phylogeny of this taxa, utilizing available up-to-date knowledge and their taxonomic classifications. Additionally, advanced imaging techniques, including electron microscopy, are employed to explore the ultrastructural characteristics of these organisms, revealing key features that contribute to their adaptive capabilities in varying marine environments. The integration of this knowledge with available omics data highlights the huge biotechnological potential of thraustochytrids, particularly in producing ω-3 fatty acids and other bioactive compounds. Our review underscores the importance of a systems biology approach in understanding thraustochytrids biology and highlights the urgent need for novel, accurate omics research to unlock their full biotechnological potential. Overall, this review aims to foster a deeper appreciation of thraustochytrids by synthesizing information on their evolution, ultrastructure, and practical applications, thereby providing a foundation for future studies in microbiology and biotechnology.

Docosahexaenoic acid-enriched diet improves the flesh quality of freshwater fish (Megalobrama amblycephala): Evaluation based on nutritional value, texture and flavor

Docosahexaenoic acid (DHA) is a potential regulatory substance for flesh quality of fish, while the related evaluation is still barely. In this study, the effects of DHA-enriched diets on the flesh quality of freshwater fish (Megalobrama amblycephala) were investigated systematically. The sub-adult M. amblycephala were randomly fed with control diet (CON), 0.2% DHA diet (DL) or 0.8% DHA diet (DH). After 12-week feeding trial, the DH group flesh had higher concentrations of essential amino acids and polyunsaturated fatty acids compared to the CON group. Meanwhile, the hardness, springiness, shear force and moisture-holding capacity, as well as the values of umami, richness and sweetness were also improved by DH. The non-targeted metabolomics analysis revealed the key metabolites that may have significantly positive influence on flavor. Collectively, the diet supplementation with 0.8% DHA could achieve the improvement of the flesh quality in terms of nutritional value, texture and flavor in freshwater fish.

Process simulation and evaluation of scaled-up biocatalytic systems: Advances, challenges, and future prospects

With the increased demand for bio-based products and the rapid development of biomanufacturing technologies, biocatalytic reactions including microorganisms and enzyme based, have become promising approaches. Prior to the scale-up of production process, environmental and economic feasibility analysis are essential for the development of a sustainable and intelligent bioeconomy in the context of industry 4.0. To achieve these goals, process simulation supports system optimization, improves energy and resource utilization efficiencies, and supports digital bioprocessing. However, due to the insufficient understanding of cellular metabolism and interaction mechanisms, there is still a lack of rational and transparent simulation tools to efficiently simulate, control, and optimize microbial/enzymatic reaction processes. Therefore, there is an urgent need to develop frameworks that integrate kinetic modeling, process simulation, and sustainability analysis for bioreaction simulations and their optimization. This review summarizes and compares the advantages and disadvantages of different process simulation software and models in simulating biocatalytic processes, identifies the limitations of traditional reaction kinetics models, and proposes the requirement of simulations close to real reactions. In addition, we explore the current state of kinetic modeling at the microscopic scale and how process simulation can be linked to kinetic models of cellular metabolism and computational fluid dynamics modeling. Finally, this review discusses the requirement of sensitivity analysis and how machine learning can assist with optimization of simulations to improve energy efficiency and product yields from techno-economic and life cycle assessment perspectives.

Universal and unique strategies for the production of polyunsaturated fatty acids in industrial oleaginous microorganisms

Polyunsaturated fatty acids (PUFAs), especially docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (ARA), are beneficial for reducing blood cholesterol and enhancing memory. Traditional PUFA production relies on extraction from plants and animals, which is unsustainable. Thus, using microorganisms as lipid-producing factories holds promise as an alternative way for PUFA production. Several oleaginous microorganisms have been successfully industrialized to date. These can be divided into universal and specialized hosts according to the products range of biosynthesis. The Yarrowia lipolytica is universal oleaginous host that has been engineered to produce a variety of fatty acids, such as γ-linolenic acid (GLA), EPA, ARA and so on. By contrast, the specialized host are used to produce only certain fatty acids, such as ARA in Mortierella alpina, EPA in Nannochloropsis, and DHA in Thraustochytrids. The metabolic engineering and fermentation strategies for improving PUFA production in universal and specialized hosts are different, which is the subject of this review. In addition, the widely applicable strategies for microbial lipid production that are not specific to individual hosts were also reviewed.

From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology

The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.

Current advances for omics-guided process optimization of microbial manufacturing

Currently, microbial manufacturing is widely used in various fields, such as food, medicine and energy, for its advantages of greenness and sustainable development. Process optimization is the committed step enabling the commercialization of microbial manufacturing products. However, the present optimization processes mainly rely on experience or trial-and-error method ignoring the intrinsic connection between cellular physiological requirement and production performance, so in many cases the productivity of microbial manufacturing could not been fully exploited at economically feasible cost. Recently, the rapid development of omics technologies facilitates the comprehensive analysis of microbial metabolism and fermentation performance from multi-levels of molecules, cells and microenvironment. The use of omics technologies makes the process optimization more explicit, boosting microbial manufacturing performance and bringing significant economic benefits and social value. In this paper, the traditional and omics technologies-guided process optimization of microbial manufacturing are systematically reviewed, and the future trend of process optimization is prospected.

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.