Metabolic engineering design to enhance (R,R)-2,3-butanediol production from glycerol in Bacillus subtilis based on flux balance analysis

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models

Background

Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually.

Results

We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends.

Conclusions

MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

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.

Loss of function of Hog1 improves glycerol assimilation in Saccharomyces cerevisiae

We previously isolated a mutant of Saccharomyces cerevisiae strain 85_9 whose glycerol assimilation was improved through adaptive laboratory evolution. To investigate the mechanism for this improved glycerol assimilation, genome resequencing of the 85_9 strain was performed, and the mutations in the open reading frame of HOG1, SIR3, SSB2, and KGD2 genes were found. Among these, a frameshift mutation in the HOG1 open reading frame was responsible for the improved glycerol assimilation ability of the 85_9 strain. Moreover, the HOG1 gene disruption improved glycerol assimilation. As HOG1 encodes a mitogen-activated protein kinase (MAPK), which is responsible for the signal transduction cascade in response to osmotic stress, namely the high osmolarity glycerol (HOG) pathway, we investigated the effect of the disruption of PBS2 gene encoding MAPK kinase for Hog1 MAPK on glycerol assimilation, revealing that PBS2 disruption can increase glycerol assimilation. These results indicate that loss of function of Hog1 improves glycerol assimilation in S. cerevisiae. However, single disruption of the SSK2, SSK22 and STE11 genes encoding protein kinases responsible for Pbs2 phosphorylation in the HOG pathway did not increase glycerol assimilation, while their triple disruption partially improved glycerol assimilation in S. cerevisiae. In addition, the HOG1 frameshift mutation did not improve glycerol assimilation in the STL1-overexpressing RIM15 disruptant strain, which was previously constructed with high glycerol assimilation ability. Furthermore, the effectiveness of the HOG1 disruptant as a bioproduction host was validated, indicating that the HOG1 CYB2 double disruptant can produce L-lactic acid from glycerol.

Improving acetoin production through construction of a genome-scale metabolic model

Acetoin was widely used in food, medicine, and other industries, because of its unique fragrance. Bacillus amyloliquefaciens was recognized as a safe strain and a promising acetoin producer in fermentation. However, due to the complexity of its metabolic network, it had not been fully utilized. Therefore, a genome-scale metabolic network model (iJYQ746) of B. amyloliquefaciens was constructed in this study, containing 746 genes, 1736 reactions, and 1611 metabolites. The results showed that Mg²⁺, Mn²⁺, and Fe²⁺ have inhibitory effects on acetoin. When the stirring speed was 400 rpm, the maximum titer was 49.8 g L^-1. Minimization of metabolic adjustments (MOMA) was used to identify potential metabolic modification targets 2-oxoglutarate aminotransferase (serC, EC 2.6.1.52) and glucose-6-phosphate isomerase (pgi, EC 5.3.1.9). These targets could effectively accumulate acetoin by increasing pyruvate content, and the acetoin synthesis rate was increased by 610% and 10%, respectively. This provides a theoretical basis for metabolic engineering to reasonably transform B. amyloliquefaciens and produce acetoin.

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

Investigation of two metabolic engineering approaches for (R,R)-2,3-butanediol production from glycerol in Bacillus subtilis

BACKGROUND

Flux Balance Analysis (FBA) is a well-known bioinformatics tool for metabolic engineering design. Previously, we have successfully used single-level FBA to design metabolic fluxes in Bacillus subtilis to enhance (R,R)-2,3-butanediol (2,3-BD) production from glycerol. OptKnock is another powerful technique for devising gene deletion strategies to maximize microbial growth coupling with improved biochemical production. It has never been used in B. subtilis. In this study, we aimed to compare the use of single-level FBA and OptKnock for designing enhanced 2,3-BD production from glycerol in B. subtilis.

RESULTS

Single-level FBA and OptKnock were used to design metabolic engineering approaches for B. subtilis to enhance 2,3-BD production from glycerol. Single-level FBA indicated that deletion of ackA, pta, lctE, and mmgA would improve the production of 2,3-BD from glycerol, while OptKnock simulation suggested the deletion of ackA, pta, mmgA, and zwf. Consequently, strains LM01 (single-level FBA-based) and MZ02 (OptKnock-based) were constructed, and their capacity to produce 2,3-BD from glycerol was investigated. The deletion of multiple genes did not negatively affect strain growth and glycerol utilization. The highest 2,3-BD production was detected in strain LM01. Strain MZ02 produced 2,3-BD at a similar level as the wild type, indicating that the OptKnock prediction was erroneous. Two-step FBA was performed to examine the reason for the erroneous OptKnock prediction. Interestingly, we newly found that zwf gene deletion in strain MZ02 improved lactate production, which has never been reported to date. The predictions of single-level FBA for strain MZ02 were in line with experimental findings.

CONCLUSIONS

We showed that single-level FBA is an effective approach for metabolic design and manipulation to enhance 2,3-BD production from glycerol in B. subtilis. Further, while this approach predicted the phenotypes of generated strains with high precision, OptKnock prediction was not accurate. We suggest that OptKnock modelling predictions be evaluated by using single-level FBA to ensure the accuracy of metabolic pathway design. Furthermore, the zwf gene knockout resulted in the change of metabolic fluxes to enhance the lactate productivity.

Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories

Metabolic engineering principles have long been applied to explore the metabolic networks of complex microbial cell factories under a variety of environmental constraints for effective deployment of the microorganisms in the optimal production of biochemicals like biofuels, polymers, amino acids, recombinant proteins. One of the methodologies used for analyzing microbial metabolic networks is the Flux Balance Analysis (FBA), which employs applications of optimization techniques for forecasting biomass growth and metabolic flux distribution of industrially important products under specified environmental conditions. The in silico flux simulations are instrumental for designing the production-specific microbial cell factories. However, FBA has some inherent limitations. The present review emphasizes how the incorporation of additional kinetic, thermodynamic, expression and regulatory constraints and integration of omics data into the classical FBA platform improve the prediction accuracy of FBA. A programmed comparison of the simulated data with the experimental observations is presented for supporting the claim. The review further accounts for the successful implementation of classical FBA in biotechnological applications and identifies areas in which classical FBA fails to make correct predictions. The analysis of the predictive capabilities of the different FBA strategies presented here is expected to help researchers in finding new avenues in engineering highly efficient microbial metabolic pathways and identify the key metabolic bottlenecks during the process. Based on the appropriate metabolic network design, fermentation engineers will be able to effectively design the bioreactors and optimize large-scale biochemical production through suitable pathway modifications.

Research progress of pathway and genome evolution in microbes

Microbes can produce valuable natural products widely applied in medicine, food and other important fields. Nevertheless, it is usually challenging to achieve ideal industrial yields due to low production rate and poor toxicity tolerance. Evolution is a constant mutation and adaptation process used to improve strain performance. Generally speaking, the synthesis of natural products in microbes is often intricate, involving multiple enzymes or multiple pathways. Individual evolution of a certain enzyme often fails to achieve the desired results, and may lead to new rate-limiting nodes that affect the growth of microbes. Therefore, it is inevitable to evolve the biosynthetic pathways or the whole genome. Here, we reviewed the pathway-level evolution including multi-enzyme evolution, regulatory elements engineering, and computer-aided engineering, as well as the genome-level evolution based on several tools, such as genome shuffling and CRISPR/Cas systems. Finally, we also discussed the major challenges faced by in vivo evolution strategies and proposed some potential solutions.