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....
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ResearchersLinks, Ltd
2024-09-01
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| Series: | Novel Research in Microbiology Journal |
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| Online Access: | https://nrmj.journals.ekb.eg/article_378857_dcf07c00a6d99e1e1cc8b19c68f82b99.pdf |
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| author | Fateh Merouane Amani Kifadji Racha Mansouri Meroua Safa Mechouche Chemes El-Houda Messaad Anfal Bellebcir |
| author_facet | Fateh Merouane Amani Kifadji Racha Mansouri Meroua Safa Mechouche Chemes El-Houda Messaad Anfal Bellebcir |
| author_sort | Fateh Merouane |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0c4c728c56f646aa8d14d336bc5b3bf6 |
| institution | DOAJ |
| issn | 2537-0286 2537-0294 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | ResearchersLinks, Ltd |
| record_format | Article |
| series | Novel Research in Microbiology Journal |
| spelling | doaj-art-0c4c728c56f646aa8d14d336bc5b3bf62025-08-20T03:03:45ZengResearchersLinks, LtdNovel Research in Microbiology Journal2537-02862537-02942024-09-01852555257910.21608/NRMJ.2024.314901.1695Optimization of anti-MRSA compound production by Streptomyces sp. AR05 using an integrated RSM-ANN-GA approachFateh Merouane0Amani Kifadji1Racha Mansouri2Meroua Safa Mechouche3Chemes El-Houda Messaad4Anfal Bellebcir5Biotechnology Laboratory, Higher National School of Biotechnology Taoufik KHAZNADAR, Constantine-3 University, Ali Mendjeli, 25100 Constantine, AlgeriaLaboratory of Plant Biology and Environment, Faculty of Sciences, Badji Mokhtar University. 23000 Annaba, AlgeriaFaculty of Medicine, Paris-Saclay University, 91190 Paris, FranceUniversity Lille, CNRS, University Polytechnique Hauts-de-France, UMR 8520, IEMN, F59000, Lille, FranceLaboratory of Biodiversity and Biotechnological Technics for the Valuation of Plant Resources (BTB-VRV), Faculty of Sciences, SNV Department, Mohamed Boudiaf University, 28000 M’sila, AlgeriaBiotechnology Laboratory, Higher National School of Biotechnology Taoufik KHAZNADAR, Constantine-3 University, Ali Mendjeli, 25100 Constantine, AlgeriaThe 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.https://nrmj.journals.ekb.eg/article_378857_dcf07c00a6d99e1e1cc8b19c68f82b99.pdfstreptomyces sp. ar05anti-mrsa compoundsresponse surface methodologyartificial neural networkgenetic algorithmoptimization |
| spellingShingle | Fateh Merouane Amani Kifadji Racha Mansouri Meroua Safa Mechouche Chemes El-Houda Messaad Anfal Bellebcir Optimization of anti-MRSA compound production by Streptomyces sp. AR05 using an integrated RSM-ANN-GA approach Novel Research in Microbiology Journal streptomyces sp. ar05 anti-mrsa compounds response surface methodology artificial neural network genetic algorithm optimization |
| title | Optimization of anti-MRSA compound production by Streptomyces sp. AR05 using an integrated RSM-ANN-GA approach |
| title_full | Optimization of anti-MRSA compound production by Streptomyces sp. AR05 using an integrated RSM-ANN-GA approach |
| title_fullStr | Optimization of anti-MRSA compound production by Streptomyces sp. AR05 using an integrated RSM-ANN-GA approach |
| title_full_unstemmed | Optimization of anti-MRSA compound production by Streptomyces sp. AR05 using an integrated RSM-ANN-GA approach |
| title_short | Optimization of anti-MRSA compound production by Streptomyces sp. AR05 using an integrated RSM-ANN-GA approach |
| title_sort | optimization of anti mrsa compound production by streptomyces sp ar05 using an integrated rsm ann ga approach |
| topic | streptomyces sp. ar05 anti-mrsa compounds response surface methodology artificial neural network genetic algorithm optimization |
| url | https://nrmj.journals.ekb.eg/article_378857_dcf07c00a6d99e1e1cc8b19c68f82b99.pdf |
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