Improving ribonucleic acid production in Saccharomyces pastorianus via in silico genome-scale metabolic network model

Ogataea polymorpha as a next-generation chassis for industrial biotechnology

Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry.

Development of a genome-scale metabolic model for the lager hybrid yeast S. pastorianus to understand the evolution of metabolic pathways in industrial settings

In silico tools such as genome-scale metabolic models have shown to be powerful for metabolic engineering of microorganisms. Saccharomyces pastorianus is a complex aneuploid hybrid between the mesophilic Saccharomyces cerevisiae and the cold-tolerant Saccharomyces eubayanus. This species is of biotechnological importance because it is the primary yeast used in lager beer fermentation and is also a key model for studying the evolution of hybrid genomes, including expression pattern of ortholog genes, composition of protein complexes, and phenotypic plasticity. Here, we created the iSP_1513 GSMM for S. pastorianus CBS1513 to allow top-down computational approaches to predict the evolution of metabolic pathways and to aid strain optimization in production processes. The iSP_1513 comprises 4,062 reactions, 1,808 alleles, and 2,747 metabolites, and takes into account the functional redundancy in the gene-protein-reaction rule caused by the presence of orthologous genes. Moreover, a universal algorithm to constrain GSMM reactions using transcriptome data was developed as a python library and enabled the integration of temperature as parameter. Essentiality data sets, growth data on various carbohydrates and volatile metabolites secretion were used to validate the model and showed the potential of media engineering to improve specific flavor compounds. The iSP_1513 also highlighted the different contributions of the parental sub-genomes to the oxidative and non-oxidative parts of the pentose phosphate pathway. Overall, the iSP_1513 GSMM represent an important step toward understanding the metabolic capabilities, evolutionary trajectories, and adaptation potential of S. pastorianus in different industrial settings.

IMPORTANCE

Genome-scale metabolic models (GSMM) have been successfully applied to predict cellular behavior and design cell factories in several model organisms, but no models to date are currently available for hybrid species due to their more complex genetics and general lack of molecular data. In this study, we generated a bespoke GSMM, iSP_1513, for this industrial aneuploid hybrid Saccharomyces pastorianus, which takes into account the aneuploidy and functional redundancy from orthologous parental alleles. This model will (i) help understand the metabolic capabilities and adaptive potential of S. pastorianus (domestication processes), (ii) aid top-down predictions for strain development (industrial biotechnology), and (iii) allow predictions of evolutionary trajectories of metabolic pathways in aneuploid hybrids (evolutionary genetics).

Self-controlled in silico gene knockdown strategies to enhance the sustainable production of heterologous terpenoid by Saccharomyces cerevisiae

Microbial bioengineering is a growing field for producing plant natural products (PNPs) in recent decades, using heterologous metabolic pathways in host cells. Once heterologous metabolic pathways have been introduced into host cells, traditional metabolic engineering techniques are employed to enhance the productivity and yield of PNP biosynthetic routes, as well as to manage competing pathways. The advent of computational biology has marked the beginning of a novel epoch in strain design through in silico methods. These methods utilize genome-scale metabolic models (GEMs) and flux optimization algorithms to facilitate rational design across the entire cellular metabolic network. However, the implementation of in silico strategies can often result in an uneven distribution of metabolic fluxes due to the rigid knocking out of endogenous genes, which can impede cell growth and ultimately impact the accumulation of target products. In this study, we creatively utilized synthetic biology to refine in silico strain design for efficient PNPs production. OptKnock simulation was performed on the GEM of Saccharomyces cerevisiae OA07, an engineered strain for oleanolic acid (OA) bioproduction that has been reported previously. The simulation predicted that the single deletion of fol1, fol2, fol3, abz1, and abz2, or a combined knockout of hfd1, ald2 and ald3 could improve its OA production. Consequently, strains EK1∼EK7 were constructed and cultivated. EK3 (OA07△fol3), EK5 (OA07△abz1), and EK6 (OA07△abz2) had significantly higher OA titers in a batch cultivation compared to the original strain OA07. However, these increases were less pronounced in the fed-batch mode, indicating that gene deletion did not support sustainable OA production. To address this, we designed a negative feedback circuit regulated by malonyl-CoA, a growth-associated intermediate whose synthesis served as a bypass to OA synthesis, at fol3, abz1, abz2, and at acetyl-CoA carboxylase-encoding gene acc1, to dynamically and autonomously regulate the expression of these genes in OA07. The constructed strains R_3A, R_5A and R_6A had significantly higher OA titers than the initial strain and the responding gene-knockout mutants in either batch or fed-batch culture modes. Among them, strain R_3A stand out with the highest OA titer reported to date. Its OA titer doubled that of the initial strain in the flask-level fed-batch cultivation, and achieved at 1.23 ± 0.04 g L-1 in 96 h in the fermenter-level fed-batch mode. This indicated that the integration of optimization algorithm and synthetic biology approaches was efficiently rational for PNP-producing strain design.

Biological autoluminescence enables effective monitoring of yeast cell electroporation

The application of pulsed electric fields (PEFs) is becoming a promising tool for application in biotechnology, and the food industry. However, real-time monitoring of the efficiency of PEF treatment conditions is challenging, especially at the industrial scale and in continuous production conditions.  To overcome this challenge, we have developed a straightforward setup capable of real-time detection of yeast biological autoluminescence (BAL) during pulsing. Saccharomyces cerevisiae culture was exposed to 8 pulses of 100 µs width with electric field strength magnitude 2-7 kV cm-1. To assess the sensitivity of our method in detecting yeast electroporation, we conducted a comparison with established methods including impedance measurements, propidium iodide uptake, cell growth assay, and fluorescence microscopy. Our results demonstrate that yeast electroporation can be instantaneously monitored during pulsing, making it highly suitable for industrial applications. Furthermore, the simplicity of our setup facilitates its integration into continuous liquid flow systems. Additionally, we have established quantitative indicators based on a thorough statistical analysis of the data that can be implemented through a dedicated machine interface, providing efficiency indicators for analysis.

Proteomic analysis reveals that the co-ordination of cytosolic and mitochondrial pathways is beneficial for sabinene biosynthesis in engineered Saccharomyces cerevisiae

Reconstruction and optimization of biosynthetic pathways can help to overproduce target chemicals in microbial cell factories based on genetic engineering. However, the perturbation of biosynthetic pathways on cellular metabolism is not well investigated and profiling the engineered microbes remains challenging. The rapid development of omics tools has the potential to characterize the engineered microbial cell factory. Here, we performed label-free quantitative proteomic analysis and metabolomic analysis of engineered sabinene overproducing Saccharomyces cerevisiae strains. Combined metabolic analysis andproteomic analysis of targeted mevalonate (MVA) pathway showed that co-ordination of cytosolic and mitochondrial pathways had balanced metabolism, and genome integration of biosynthetic genes had higher sabinene production with less MVA enzymes. Furthermore, comparative proteomic analysis showed that compartmentalized mitochondria pathway had perturbation on central cellular metabolism. This study provided an omics analysis example for characterizing engineered cell factory, which can guide future regulation of the cellular metabolism and maintaining optimal protein expression levels for the synthesis of target products.