etiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis

Thermodynamic tools for more efficient biotechnological processes: an example in poly-(3-hydroxybutyrate) production from carbon monoxide

Modern biotechnology requires the integration of several disciplines, with thermodynamics being a crucial one. Experimental approaches frequently used in biotechnology, such as rewiring of metabolic networks or culturing of micro-organisms in engineered environments, can benefit from the application of thermodynamic tools. In this paper, we provide an overview of several thermodynamic tools that are useful for the design and optimization of biotechnological processes, and we demonstrate their potential application in the production of poly-(3-hydroxybutyrate) (PHB) from carbon monoxide (CO). We discuss how these tools can aid in the design of metabolic engineering strategies, the calculation of expected yields, the assessment of the thermodynamic feasibility of the targeted conversions, the identification of potential thermodynamic bottlenecks, and the selection of genetic engineering targets. Although we illustrate these tools using the specific example of PHB production from CO, they can be applied to other substrates and products.

Pan-genome-scale metabolic modeling of Bacillus subtilis reveals functionally distinct groups

Bacillus subtilis is an important industrial and environmental microorganism known to occupy many niches and produce many compounds of interest. Although it is one of the best-studied organisms, much of this focus including the reconstruction of genome-scale metabolic models has been placed on a few key laboratory strains. Here, we substantially expand these prior models to pan-genome-scale, representing 481 genomes of B. subtilis with 2,315 orthologous gene clusters, 1,874 metabolites, and 2,239 reactions. Furthermore, we incorporate data from carbon utilization experiments for eight strains to refine and validate its metabolic predictions. This comprehensive pan-genome model enables the assessment of strain-to-strain differences related to nutrient utilization, fermentation outputs, robustness, and other metabolic aspects. Using the model and phenotypic predictions, we divide B. subtilis strains into five groups with distinct patterns of behavior that correlate across these features. The pan-genome model offers deep insights into B. subtilis' metabolism as it varies across environments and provides an understanding as to how different strains have adapted to dynamic habitats.

IMPORTANCE

As the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism. Bacillus subtilis strain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481 B. subtilis strains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application.

A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction

Given the extensive heterogeneity and variability, understanding cellular functions and regulatory mechanisms through the analysis of multi-omics datasets becomes extremely challenging. Here, a comprehensive modeling framework of multi-omics machine learning and metabolic network models are proposed that covers various cellular biological processes across multiple scales. This model on an extensive normalized compendium of Bacillus subtilis is validated, which encompasses gene expression data from environmental perturbations, transcriptional regulation, signal transduction, protein translation, and growth measurements. Comparison with high-throughput experimental data shows that EM_iBsu1209-ME, constructed on this basis, can accurately predict the expression of 605 genes and the synthesis of 23 metabolites under different conditions. This study paves the way for the construction of comprehensive biological databases and high-performance multi-omics metabolic models to achieve accurate predictive analysis in exploring complex mechanisms of cell genotypes and phenotypes.

Combinatorial metabolic engineering of Bacillus subtilis for menaquinone-7 biosynthesis

Menaquinone-7 (MK-7), a form of vitamin K2, supports bone health and prevents arterial calcification. Microbial fermentation for MK-7 production has attracted widespread attention because of its low cost and short production cycles. However, insufficient substrate supply, unbalanced precursor synthesis, and low catalytic efficiency of key enzymes severely limited the efficiency of MK-7 synthesis. In this study, utilizing Bacillus subtilis BSAT01 (with an initial MK-7 titer of 231.0 mg/L) obtained in our previous study, the glycerol metabolism pathway was first enhanced to increase the 3-deoxy-arabino-heptulonate 7-phosphate (DHAP) supply, which led to an increase in MK-7 titer to 259.7 mg/L. Subsequently, a combination of knockout strategies predicted by the genome-scale metabolic model etiBsu1209 was employed to optimize the central carbon metabolism pathway, and the resulting strain showed an increase in MK-7 production from 259.7 to 318.3 mg/L. Finally, model predictions revealed the methylerythritol phosphate pathway as the major restriction pathway, and the pathway flux was increased by heterologous introduction (Introduction of Dxs derived from Escherichia coli) and fusion expression (End-to-end fusion of two enzymes by a linker peptide), resulting in a strain with a titer of 451.0 mg/L in a shake flask and 474.0 mg/L in a 50-L bioreactor. This study achieved efficient MK-7 synthesis in B. subtilis, laying the foundation for large-scale MK-7 bioproduction.

Cell factory design with advanced metabolic modelling empowered by artificial intelligence

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.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Targeting the Bottlenecks in Levan Biosynthesis Pathway in Bacillus subtilis and Strain Optimization by Computational Modeling and Omics Integration

Levan is a fructan polymer with many industrial applications such as the formulation of hydrogels, drug delivery, and wound healing, among others. To this end, metabolic systems engineering is a valuable method to improve the yield of a specific metabolite in a wide range of bacterial and eukaryotic organisms. In this study, we report a systems biology approach integrating genomics data for the Bacillus subtilis model, wherein the metabolic pathway for levan biosynthesis is unpacked. We analyzed a revised genome-scale enzyme-constrained metabolic model (ecGEM) and performed simulations to increase levan biopolymer production capacity in B. subtilis. We used the model ec_iYO844_lvn to (1) identify the essential genes and bottlenecks in levan production, and (2) specifically design an engineered B. subtilis strain capable of producing higher levan yields. The FBA and FVA analysis showed the maximal growth rate of the organism up to 0.624 hr-1 at 20 mmol gDw-1 hr-1 of sucrose intake. Gene knockout analyses were performed to identify gene knockout targets to increase the levan flux in B. subtilis. Importantly, we found that the pgk and ctaD genes are the two target genes for the knockout. The perturbation of these two genes has flux gains for levan production reactions with 1.3- and 1.4-fold the relative flux span in the mutant strains, respectively, compared to the wild type. In all, this work identifies the bottlenecks in the production of levan and possible ways to overcome them. Our results provide deeper insights on the bacterium's physiology and new avenues for strain engineering.

Bacillus sp. as a microbial cell factory: Advancements and future prospects

Bacillus sp. is one of the most distinctive gram-positive bacteria, able to grow efficiently using cheap carbon sources and secrete a variety of useful substances, which are widely used in food, pharmaceutical, agricultural and environmental industries. At the same time, Bacillus sp. is also recognized as a safe genus with a relatively clear genetic background, which is conducive to the industrial production of target metabolites. In this review, we discuss the reasons why Bacillus sp. has been so extensively studied and summarize its advances in systems and synthetic biology, engineering strategies to improve microbial cell properties, and industrial applications in several metabolic engineering applications. Finally, we present the current challenges and possible solutions to provide a reliable basis for Bacillus sp. as a microbial cell factory.