Identification of unstructured model for subtilin production through Bacillus subtilis using hybrid genetic algorithm

Engineering Bacillus subtilis J46 for efficient utilization of galactose through adaptive laboratory evolution

Efficient utilization of galactose by microorganisms can lead to the production of valuable bio-products and improved metabolic processes. While Bacillus subtilis has inherent pathways for galactose metabolism, there is potential for enhancement via evolutionary strategies. This study aimed to boost galactose utilization in B. subtilis using adaptive laboratory evolution (ALE) and to elucidate the genetic and metabolic changes underlying the observed enhancements. The strains of B. subtilis underwent multiple rounds of adaptive laboratory evolution (approximately 5000 generations) in an environment that favored the use of galactose. This process resulted in an enhanced specific growth rate of 0.319 ± 0.005 h-1, a significant increase from the 0.03 ± 0.008 h-1 observed in the wild-type strains. Upon selecting the evolved strain BSGA14, a comprehensive whole-genome sequencing revealed the presence of 63 single nucleotide polymorphisms (SNPs). Two of them, located in the coding sequences of the genes araR and glcR, were found to be the advantageous mutations after reverse engineering. The strain with these two accumulated mutations, BSGALE4, exhibited similar specific growth rate on galactose to the evolved strain BSGA14 (0.296 ± 0.01 h-1). Furthermore, evolved strain showed higher productivity of protease and β-galactosidase in mock soybean biomass medium. ALE proved to be a potent tool for enhancing galactose metabolism in B. subtilis. The findings offer valuable insights into the potential of evolutionary strategies in microbial engineering and pave the way for industrial applications harnessing enhanced galactose conversion.

Green synthesis of bioemulsifier and exopolysaccharides by Brevibacillus borstelensis and process parameters optimization using response surface model, genetic algorithm and NSGA

Bioemulsifier and exopolysaccharides are industrially important biomolecules produced by microorganisms using green technology. They have applications in food, biomedical, pharmaceutical and cosmetic industries and hence high yield of both products becomes necessary. The current study showed that Brevibacillus borstelensis has a potential to produce bioemulsifier and exopolysaccharide simultaneously but yield of both products is limited. In this study, CCD-RSM has been used as experimental design to increase concentration of both products. Concentrations of glucose, monosodium glutamate, yeast extract and magnesium sulphate were process variables and concentrations of bioemulsifiers, exopolysaccharides and biomass were responses. 30 experimental runs were performed and the models from CCD were optimized by genetic algorithm and NSGA. The results from modelling and optimization techniques were compared along with validation of models. The predicted values from optimization techniques were better than experimental values. The study concluded that NSGA is most suitable to optimize multiple responses simultaneously when compared to RSM and genetic algorithm. The optimum conditions for production were 22 g/l glucose, 14 g/l monosodium glutamate, 6 g/l yeast extract and 0.6 g/l magnesium sulphate with maximum yield of 6.1, 17.6 and 2.8 g/l bioemulsifier, exopolysaccharide and biomass, respectively. Knowledge of optimum concentrations of carbon and nitrogen source will help to utilize industrial and agricultural wastes for production of both products. They have applications in environmental bioremediation by clearing oil spills. Bioemulsifiers also help in heavy metal removal from hazardous waste. Hence this will result in environmental bioremediation by utilization of wastes by employing products generated from wastes.

Cell-to-Cell Variability in Protein Expression during Viral Infection: Monte-Carlo Simulation and Validation based on Confocal Imaging

One of the major challenges is to identify the statistical model underlying the heterogeneity in viral protein expression in single cells. In this endeavor, we propose a computational tool to address the cell-to-cell variability in protein expression by random variate generation following probability distributions. Here, we show that statistical modeling using the probability density function of various distribution offers considerable potential for providing stochastic inputs to Monte Carlo simulation. Specifically, we present the ranking between three distribution families including gamma, normal and Weibull distribution using a comparison of cumulative frequency obtained from experiment and simulation. The major contribution of the proposed simulation method is to identify the underlying statistical model in kinetic parameters that capture the variability in protein expression in single cells obtained through imaging using confocal microscopy.

Mini Review on Antimicrobial Peptides, Sources, Mechanism and Recent Applications

Antimicrobial peptides in recent years have gained increased interest among scientists, health professionals and the pharmaceutical companies owing to their therapeutic potential. These are low molecular weight proteins with broad range antimicrobial and immuno modulatory activities against infectious bacteria (Gram positive and Gram negative), viruses and fungi. Inability of micro-organisms to develop resistance against most of the antimicrobial peptide has made them as an efficient product which can greatly impact the new era of antimicrobials. In addition to this these peptides also demonstrates increased efficacy, high specificity, decreased drug interaction, low toxicity, biological diversity and direct attacking properties. Pharmaceutical industries are therefore conducting appropriate clinical trials to develop these peptides as potential therapeutic drugs. More than 60 peptide drugs have already reached the market and several hundreds of novel therapeutic peptides are in preclinical and clinical development. Rational designing can be used further to modify the chemical and physical properties of existing peptides. This mini review will discuss the sources, mechanism and recent therapeutic applications of antimicrobial peptides in treatment of infectious diseases.

Novel antibiofilm chemotherapies target nitrogen from glutamate and glutamine

Bacteria in nature often reside in differentiated communities termed biofilms, which are an active interphase between uni-cellular and multicellular life states for bacteria. Here we demonstrate that the development of B. subtilis biofilms is dependent on the use of glutamine or glutamate as a nitrogen source. We show a differential metabolic requirement within the biofilm; while glutamine is necessary for the dividing cells at the edges, the inner cell mass utilizes lactic acid. Our results indicate that biofilm cells preserve a short-term memory of glutamate metabolism. Finally, we establish that drugs that target glutamine and glutamate utilization restrict biofilm development. Overall, our work reveals a spatial regulation of nitrogen and carbon metabolism within the biofilm, which contributes to the fitness of bacterial complex communities. This acquired metabolic division of labor within biofilm can serve as a target for novel anti-biofilm chemotherapies