Metabolic engineering最新文献

A bio-based production of chemical building blocks from renewable, sustainable and non-food substrates is one key element to fight climate crisis. Lactic acid, one such chemical building block is currently produced from first generation feedstocks such as glucose and sucrose, both requiring land and water resources. In this study we aimed for lactic acid production from methanol by utilizing Komagataella phaffii as a production platform. Methanol, a single carbon source has potential as a sustainable substrate as technology allows (electro)chemical hydrogenation of CO₂ for methanol production. Here we show that expression of the Lactiplantibacillus plantarum derived lactate dehydrogenase leads to L-lactic acid production in Komagataella phaffii, however, production resulted in low titers and cells subsequently consumed lactic acid again. Gene expression analysis of the methanol-utilizing genes AOX1, FDH1 and DAS2 showed that the presence of lactic acid downregulates transcription of the aforementioned genes, thereby repressing the methanol-utilizing pathway. For activation of the methanol-utilizing pathway in the presence of lactic acid, we constructed strains deficient in transcriptional repressors Nrg1, Mig1-1, and Mig1-2 as well as strains with overrepresentation of transcriptional activators Mxr1 and Mit1. While loss of transcriptional repressors had no significant impact on lactic acid production, overexpression of both transcriptional activators, MXR1 and MIT1, increased lactic acid titers from 4 g L⁻¹ to 17 g L⁻¹ in bioreactor cultivations.

Nanobodies are single-domain antibody fragments that have garnered considerable use as diagnostic and therapeutic agents as well as research tools. However, obtaining pure VHHs, like many proteins, can be laborious and inconsistent. High level cytoplasmic expression in E. coli can be challenging due to improper folding and insoluble aggregation caused by reduction of the conserved disulfide bond. We report a systems engineering approach leveraging engineered strains of E. coli, in combination with a two-stage process and simplified downstream purification, enabling improved, robust, soluble cytoplasmic nanobody expression, as well as rapid cell autolysis and purification. This approach relies on the dynamic control over the reduction potential of the cytoplasm, incorporates lysis enzymes for purification, and can also integrate dynamic expression of protein folding catalysts. Collectively, the engineered system results in more robust growth and protein expression, enabling efficient scalable nanobody production, and purification from high throughput microtiter plates, to routine shake flask cultures and larger instrumented bioreactors. We expect this system will expedite VHH development.

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

Subcellular compartmentalization is a crucial evolution characteristic of eukaryotic cells, providing inherent advantages for the construction of artificial biological systems to efficiently produce natural products. The establishment of an artificial protein transport system represents a pivotal initial step towards developing efficient artificial biological systems. Peroxisome has been demonstrated as a suitable subcellular compartment for the biosynthesis of terpenes in yeast. In this study, an artificial protein transporter ScPEX5* was firstly constructed by fusing the N-terminal sequence of PEX5 from S. cerevisiae and the C-terminal sequence of PEX5. Subsequently, an artificial protein transport system including the artificial signaling peptide YQSYY and its enhancing upstream 9 amino acid (9AA) residues along with ScPEX5* was demonstrated to exhibit orthogonality to the internal transport system of peroxisomes in S. cerevisiae. Furthermore, a library of 9AA residues was constructed and selected using high throughput pigment screening system to obtain an optimized signaling peptide (oPTS1*). Finally, the ScPEX5*-oPTS1* system was employed to construct yeast cell factories capable of producing the sesquiterpene α-humulene, resulting in an impressive α-humulene titer of 17.33 g/L and a productivity of 0.22 g/L/h achieved through fed-batch fermentation in a 5 L bioreactor. This research presents a valuable tool for the construction of artificial peroxisome cell factories and effective strategies for synthesizing other natural products in yeast.

Cysteine and cystine are essential amino acids present in mammalian cell cultures. While contributing to biomass synthesis, recombinant protein production, and antioxidant defense mechanisms, cysteine poses a major challenge in media formulations owing to its poor stability and oxidation to cystine, a cysteine dimer. Due to its poor solubility, cystine can cause precipitation of feed media, formation of undesired products, and consequently, reduce cysteine bioavailability. In this study, a highly soluble cysteine containing dipeptide dimer, Ala-Cys-Cys-Ala (ACCA), was evaluated as a suitable alternative to cysteine and cystine in CHO cell cultures. Replacing cysteine and cystine in basal medium with ACCA did not sustain cell growth. However, addition of ACCA at 4 mM and 8 mM to basal medium containing cysteine and cystine boosted cell growth up to 15% and 27% in CHO-GS and CHO–K1 batch cell cultures respectively and led to a proportionate increase in IgG titer. ¹³C-Metabolic flux analysis revealed that supplementation of ACCA reduced glycolytic fluxes by 20% leading to more efficient glucose metabolism in CHO–K1 cells. In fed-batch cultures, ACCA was able to replace cysteine and cystine in feed medium. Furthermore, supplementation of ACCA at high concentrations in basal medium eliminated the need for any cysteine equivalents in feed medium and increased cell densities and viabilities in fed-batch cultures without any significant impact on IgG charge variants. Taken together, this study demonstrates the potential of ACCA to improve CHO cell growth, productivity, and metabolism while also facilitating the formulation of cysteine- and cystine-free feed media. Such alternatives to cysteine and cystine will pave the way for enhanced biomanufacturing by increasing cell densities in culture and extending the storage of highly concentrated feed media as part of achieving intensified bioproduction processes.

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.

CRISPR-based high-throughput genome-wide loss-of-function screens are a valuable approach to functional genetics and strain engineering. The yeast Komagataella phaffii is a host of particular interest in the biopharmaceutical industry and as a metabolic engineering host for proteins and metabolites. Here, we design and validate a highly active 6-fold coverage genome-wide sgRNA library for this biotechnologically important yeast containing 30,848 active sgRNAs targeting over 99% of its coding sequences. Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in K. phaffii, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guide's targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of K. phaffii essential genes, many of which relate to metabolic engineering targets, including protein production, secretion, and glycosylation. The high activity, genome-wide CRISPR library developed here enables functional genomic screening in K. phaffii, applied here to gene essentiality classification, and promises to enable other genetic screens.

Heme has attracted considerable attention due to its indispensable biological roles and applications in healthcare and artificial foods. The development and utilization of edible microorganisms instead of animals to produce heme is the most promising method to promote the large-scale industrial production and safe application of heme. However, the cytotoxicity of heme severely restricts its efficient synthesis by microorganisms, and the cytotoxic mechanism is not fully understood. In this study, the effect of heme toxicity on Saccharomyces cerevisiae was evaluated by enhancing its synthesis using metabolic engineering. The results showed that the accumulation of heme after the disruption of heme homeostasis caused serious impairments in cell growth and metabolism, as demonstrated by significantly poor growth, mitochondrial damage, cell deformations, and chapped cell surfaces, and these features which were further associated with substantially elevated reactive oxygen species (ROS) levels within the cell (mainly H₂O₂ and superoxide anion radicals). To improve cellular tolerance to heme, 5 rounds of laboratory evolution were performed, increasing heme production by 7.3-fold and 4.2-fold in terms of the titer (38.9 mg/L) and specific production capacity (1.4 mg/L/OD₆₀₀), respectively. Based on comparative transcriptomic analyses, 32 genes were identified as candidates that can be modified to enhance heme production by more than 20% in S. cerevisiae. The combined overexpression of 5 genes (SPS22, REE1, PHO84, HEM4 and CLB2) was shown to be an optimal method to enhance heme production. Therefore, a strain with enhanced heme tolerance and ROS quenching ability (R5-M) was developed that could generate 380.5 mg/L heme with a productivity of 4.2 mg/L/h in fed-batch fermentation, with S. cerevisiae strains being the highest producers reported to date. These findings highlight the importance of improving heme tolerance for the microbial production of heme and provide a solution for efficient heme production by engineered yeasts.