Optimization of anti-MRSA compound production by Streptomyces sp. AR05 using an integrated RSM-ANN-GA approach
The emergence of multidrug-resistant pathogens, such as methicillin-resistant Staphylococcus aureus (MRSA), poses a significant threat to the global public health. Streptomyces species have been recognized as a prolific source of bioactive secondary metabolites, including antimicrobial compounds....
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
ResearchersLinks, Ltd
2024-09-01
|
| Series: | Novel Research in Microbiology Journal |
| Subjects: | |
| Online Access: | https://nrmj.journals.ekb.eg/article_378857_dcf07c00a6d99e1e1cc8b19c68f82b99.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The emergence of multidrug-resistant pathogens, such as methicillin-resistant
Staphylococcus aureus (MRSA), poses a significant threat to the global public health.
Streptomyces species have been recognized as a prolific source of bioactive secondary
metabolites, including antimicrobial compounds. In this study, we aimed to optimize the
production of anti-MRSA compounds by Streptomyces sp. AR05; a strain isolated from
hydrocarbon-contaminated soil, using an integrated approach combining response surface
methodology (RSM), artificial neural networks (ANN), and genetic algorithms (GA). The
strain was identified through 16S rRNA gene sequencing and exhibited significant genetic
similarity to Streptomyces kurssanovii and Streptomyces ostreogriseus. Using the PlackettBurman design, the most important variables affecting the anti-MRSA activity were found to
be peptone, CaCO3, and pH. These factors were optimized using Box-Behnken design, while
RSM and ANN were utilized for modeling the experimental data. The predicted accuracy of
ANN model was higher than that of the RSM model, with lower values of mean absolute
percentage error (MAPE) and root mean square error (RMSE). Sensitivity analysis of the
ANN model identified peptone as the most influential factor, followed by pH and CaCO3. The
ANN model was further optimized using GA, and the optimized conditions (5.34 g/ l peptone,
1.54 g/ l CaCO3, pH 6.07) were experimentally validated, resulting in a 48.87 % increase in
anti-MRSA activity compared to the initial conditions. The developed RSM-ANN-GA approach demonstrated the potential for enhancing the production of valuable antibacterial
compounds from Streptomyces species and contributed to the global efforts to combat
antimicrobial resistance. |
|---|---|
| ISSN: | 2537-0286 2537-0294 |