Dataset Information

In vitro identification of antimicrobial hemolytic lipopeptide from halotolerant Bacillus by Zymogram, FTIR, and GC mass analysis.

ABSTRACT:

Objectives

The multi-drug resistant bacteria and clinical infections are some of the biggest global concerns, so new drugs are needed. Antimicrobial peptides and lipopeptides are new bioactive agents with great potential that can become a new strategy for clinical applications.

Materials and methods

Some Bacillus strains were isolated based on hemolytic antimicrobial production from the soil. The extracellular proteins were extracted by acidic precipitation and chloroform/methanol method and analyzed by SDS-PAGE electrophoresis and stained with Sudan black. The black fragment was purified and characterized by FTIR, GC/MS, and HPLC analysis to demonstrate the presence of lipids and proteins. The anti-microbial ability and stability of the purified lipopeptide were assayed by the Kirby-Bauer method. Also, it was examined for metal removal.

Results

A new Bacillus halotolerans strain SCM 034 with hemolytic antimicrobial production was isolated. According to GC/MS (detecting C16, C17) and HPLC (detecting leucine, glutamic acid, valine, arginine, glycine, and aspartic acid) data, the black fragment was lipopeptide. Polyacrylamide hydrogel containing lipopeptide and gel purified lipopeptide showed anti-microbial activities against S. aureus and S. cerevisiae that were stable for a few months. Also, the lipopeptide was useful for cation removal and decreased cobalt, nickel, and calcium by 10.81 %, 24.39 %, and 34 %, respectively.

Conclusion

Production of antibacterial lipopeptide hemolysin from this strain is reported for the first time and according to the results, lipopeptides have unique properties with biomedical and pharmaceutical applications. Also, polyacrylamide hydrogel lipopeptide is a promising candidate for wound healing.

SUBMITTER: Etemadzadeh SS

PROVIDER: S-EPMC8244604 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The Bacillus cereus group comprises genetical closely related species with variable toxigenic characteristics. However, detection and differentiation of the B. cereus group species in routine diagnostics can be difficult, expensive and laborious since current species designation is linked to specific phenotypic characteristic or the presence of species-specific genes. Especially the differentiation of Bacillus cereus and Bacillus thuringiensis, the identification of psychrotolerant Bacillus mycoides and Bacillus weihenstephanensis, as well as the identification of emetic B. cereus and Bacillus cytotoxicus, which are both producing highly potent toxins, is of high importance in food microbiology. Thus, we investigated the use of a machine learning approach, based on artificial neural network (ANN) assisted Fourier transform infrared (FTIR) spectroscopy, for discrimination of B. cereus group members. The deep learning tool box of Matlab was employed to construct a one-level ANN, allowing the discrimination of the aforementioned B. cereus group members. This model resulted in 100% correct identification for the training set and 99.5% correct identification overall. The established ANN was applied to investigate the composition of B. cereus group members in soil, as a natural habitat of B. cereus, and in food samples originating from foodborne outbreaks. These analyses revealed a high complexity of B. cereus group populations, not only in soil samples but also in the samples from the foodborne outbreaks, highlighting the importance of taking multiple isolates from samples implicated in food poisonings. Notable, in contrast to the soil samples, no bacteria belonging to the psychrotolerant B. cereus group members were detected in the food samples linked to foodborne outbreaks, while the overall abundancy of B. thuringiensis did not significantly differ between the sample categories. None of the isolates was classified as B. cytotoxicus, fostering the hypothesis that the latter species is linked to very specific ecological niches. Overall, our work shows that machine learning assisted (FTIR) spectroscopy is suitable for identification of B. cereus group members in routine diagnostics and outbreak investigations. In addition, it is a promising tool to explore the natural habitats of B. cereus group, such as soil.