Targeting the Bottlenecks in Levan Biosynthesis Pathway in Bacillus subtilis and Strain Optimization by Computational Modeling and Omics Integration.
Aruldoss ImmanuelRagothaman M YennamalliVenkatasubramanian UlaganathanPublished in: Omics : a journal of integrative biology (2024)
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.
Keyphrases
- genome wide
- bacillus subtilis
- wild type
- drug delivery
- genome wide identification
- dna methylation
- genome wide analysis
- copy number
- wound healing
- escherichia coli
- bioinformatics analysis
- transcription factor
- risk assessment
- blood pressure
- single cell
- cancer therapy
- heavy metals
- molecular dynamics
- body mass index
- heart rate
- extracellular matrix
- deep learning
- physical activity
- tissue engineering
- data analysis