Rewiring metabolic flux to simultaneously improve malate production and eliminate by-product succinate accumulation by Myceliophthora thermophila

Although a high titre of malic acid is achieved by filamentous fungi, by-product succinic acid accumulation leads to a low yield of malic acid and is unfavourable for downstream processing. Herein, we conducted a series of metabolic rewiring strategies in a previously constructed Myceliophthora thermophila to successfully improve malate production and abolish succinic acid accumulation. First, a pyruvate carboxylase CgPYC variant with increased activity was obtained using a high-throughput system and introduced to improve malic acid synthesis. Subsequently, shifting metabolic flux to malate synthesis from mitochondrial metabolism by deleing mitochondrial carriers of pyruvate and malate, led to a 53.7% reduction in succinic acid accumulation. The acceleration of importing cytosolic succinic acid into the mitochondria for consumption further decreased succinic acid formation by 53.3%, to 2.12 g/L. Finally, the importer of succinic acid was discovered and used to eliminate by-product accumulation. In total, malic acid production was increased by 26.5%, relative to the start strain JG424, to 85.23 g/L and 89.02 g/L on glucose and Avicel, respectively, in the flasks. In a 5-L fermenter, the titre of malic acid reached 182.7 g/L using glucose and 115.8 g/L using raw corncob, without any by-product accumulation. This study would accelerate the industrial production of biobased malic acid from renewable plant biomass.

Recent Advances and Challenges in Enzymatic Depolymerization and Recycling of PET Wastes

Poly (ethylene terephthalate) (PET) is one of the most commonly used plastics in daily life and various industries. Enzymatic depolymerization and recycling of post-consumer PET (pc-PET) provides a promising strategy for the sustainable circular economy of polymers. Great protein engineering efforts have been devoted to improving the depolymerization performance of PET hydrolytic enzymes (PHEs). In this review, we first discuss the mechanisms and challenges of enzymatic PET depolymerization. Subsequently, we summarize the state-of-the-art engineering of PHEs including rational design, machine learning, and directed evolution for improved depolymerization performance, and highlight the advances in screening methods of PHEs. We further discuss several factors that affect the enzymatic depolymerization efficiency. We conclude with our perspective on the opportunities and challenges in bio-recycling and bio-upcycling of PET wastes.

Designing gene manipulation schedules for high throughput parallel construction of objective strains

Recent advances in biofoundries have enabled the construction of a large quantity of strains in parallel, accelerating the design-build-test-learn (DBTL) cycles for strain development. However, the construction of a large number of strains by iterative gene manipulation is still time-consuming and costly, posing a challenge for the development of commercial strains. Common gene manipulations among different objective strains open up the possibility of reducing cost and time for strain construction in biofoundries by optimizing genetic manipulation schedules. A method is introduced consisting of two complementary algorithms for designing optimal parent-children manipulation schedules for strain construction: greedy search of common ancestor strains (GSCAS) and minimizing total manipulations (MTM). By reusing common ancestor strains, the number of strains to be constructed can be effectively reduced, resulting in a tree-like structure of descendants instead of linear lineages for each strain. The GSCAS algorithm can quickly find common ancestor strains and clusters them together based on their genetic makeup, and the MTM algorithm subsequently minimize the genetic manipulations required, resulting in a further reduction in the total number of genetic manipulations. The effectiveness of our method is demonstrated through a case study of 94 target strains, where GSCAS reduces an average of 36% of the total gene manipulations, and MTM reduces an additional 10%. The performance of both algorithms is robust among case studies with different average occurrences of gene manipulations across objective strains. Our method potentially improves cost efficiency and accelerate the development of commercial strains significantly. The implementation of the methods can be freely accessed via https://gscas-mtm.biodesign.ac.cn/.

CAVE: a cloud-based platform for analysis and visualization of metabolic pathways

Flux balance analysis (FBA) is an important method for calculating optimal pathways to produce industrially important chemicals in genome-scale metabolic models (GEMs). However, for biologists, the requirement of coding skills poses a significant obstacle to using FBA for pathway analysis and engineering target identification. Additionally, a time-consuming manual drawing process is often needed to illustrate the mass flow in an FBA-calculated pathway, making it challenging to detect errors or discover interesting metabolic features. To solve this problem, we developed CAVE, a cloud-based platform for the integrated calculation, visualization, examination and correction of metabolic pathways. CAVE can analyze and visualize pathways for over 100 published GEMs or user-uploaded GEMs, allowing for quicker examination and identification of special metabolic features in a particular GEM. Additionally, CAVE offers model modification functions, such as gene/reaction removal or addition, making it easy for users to correct errors found in pathway analysis and obtain more reliable pathways. With a focus on the design and analysis of optimal pathways for biochemicals, CAVE complements existing visualization tools based on manually drawn global maps and can be applied to a broader range of organisms for rational metabolic engineering. CAVE is available at https://cave.biodesign.ac.cn/.