Dynamic metabolic modelling of overproduced protein secretion in Streptomyces lividans using adaptive DFBA

Precise metabolic modeling in post-omics era: accomplishments and perspectives

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

Enhanced protein secretion in reduced genome strains of Streptomyces lividans

BACKGROUND

S. lividans TK24 is a popular host for the production of small molecules and the secretion of heterologous protein. Within its large genome, twenty-nine non-essential clusters direct the biosynthesis of secondary metabolites. We had previously constructed ten chassis strains, carrying deletions in various combinations of specialized metabolites biosynthetic clusters, such as those of the blue actinorhodin (act), the calcium-dependent antibiotic (cda), the undecylprodigiosin (red), the coelimycin A (cpk) and the melanin (mel) clusters, as well as the genes hrdD, encoding a non-essential sigma factor, and matAB, a locus affecting mycelial aggregation. Genome reduction was aimed at reducing carbon flow toward specialized metabolite biosynthesis to optimize the production of secreted heterologous protein.

RESULTS

Two of these S. lividans TK24 derived chassis strains showed ~ 15% reduction in biomass yield, 2-fold increase of their total native secretome mass yield and enhanced abundance of several secreted proteins compared to the parental strain. RNAseq and proteomic analysis of the secretome suggested that genome reduction led to cell wall and oxidative stresses and was accompanied by the up-regulation of secretory chaperones and of secDF, a Sec-pathway component. Interestingly, the amount of the secreted heterologous proteins mRFP and mTNFα, by one of these strains, was 12 and 70% higher, respectively, than that secreted by the parental strain.

CONCLUSION

The current study described a strategy to construct chassis strains with enhanced secretory abilities and proposed a model linking the deletion of specialized metabolite biosynthetic clusters to improved production of secreted heterologous proteins.

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.

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.