Bacterial biofilm plays a pivotal role in chronic Staphylococcus aureus (S. aureus) infection and its inhibition may represent an important strategy to develop novel therapeutic agents. The scientific community is continuously searching for natural and “green alternatives” to chemotherapeutic drugs, including essential oils (EOs), assuming the latter not able to select resistant strains, likely due to their multicomponent nature and, hence, multitarget action. Here it is reported the biofilm production modulation exerted by 61 EOs, also investigated for their antibacterial activity on S. aureus strains, including reference and cystic fibrosis patients’ isolated strains. The EOs biofilm modulation was assessed by Christensen method on five S. aureus strains. Chemical composition, investigated by GC/MS analysis, of the tested EOs allowed a correlation between biofilm modulation potency and putative active components by means of machine learning algorithms application. Some EOs inhibited biofilm growth at 1.00% concentration, although lower concentrations revealed dierent biological profile. Experimental data led to select antibiofilm EOs based on their ability to inhibit S. aureus biofilm growth, which were characterized for their ability to alter the biofilm organization by means of SEM studies.

Essential oils biofilm modulation activity, chemical and machine learning analysis. Application on staphylococcus aureus isolates from cystic fibrosis patients / Papa, Rosanna; Garzoli, Stefania; Vrenna, Gianluca; Sabatino, Manuela; Sapienza, Filippo Umberto; Relucenti, Michela; Donfrancesco, Orlando; Vita Fiscarelli, Ersilia; Artini, Marco; Selan, Laura; Ragno, Rino. - In: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES. - ISSN 1661-6596. - 21:23(2020), pp. 1-20. [10.3390/ijms21239258]

Essential oils biofilm modulation activity, chemical and machine learning analysis. Application on staphylococcus aureus isolates from cystic fibrosis patients

Rosanna Papa;Stefania Garzoli;Gianluca Vrenna;Manuela Sabatino;Filippo Sapienza;Michela Relucenti;Orlando Donfrancesco;Marco Artini;Laura Selan
;
Rino Ragno
2020

Abstract

Bacterial biofilm plays a pivotal role in chronic Staphylococcus aureus (S. aureus) infection and its inhibition may represent an important strategy to develop novel therapeutic agents. The scientific community is continuously searching for natural and “green alternatives” to chemotherapeutic drugs, including essential oils (EOs), assuming the latter not able to select resistant strains, likely due to their multicomponent nature and, hence, multitarget action. Here it is reported the biofilm production modulation exerted by 61 EOs, also investigated for their antibacterial activity on S. aureus strains, including reference and cystic fibrosis patients’ isolated strains. The EOs biofilm modulation was assessed by Christensen method on five S. aureus strains. Chemical composition, investigated by GC/MS analysis, of the tested EOs allowed a correlation between biofilm modulation potency and putative active components by means of machine learning algorithms application. Some EOs inhibited biofilm growth at 1.00% concentration, although lower concentrations revealed dierent biological profile. Experimental data led to select antibiofilm EOs based on their ability to inhibit S. aureus biofilm growth, which were characterized for their ability to alter the biofilm organization by means of SEM studies.
2020
essential oil; GC-MS analysis; machine learning; classification algorithms; scanning electron microscopy; cystic fibrosis; antibacterial; antibiofilm; Staphylococcus aureus
01 Pubblicazione su rivista::01a Articolo in rivista
Essential oils biofilm modulation activity, chemical and machine learning analysis. Application on staphylococcus aureus isolates from cystic fibrosis patients / Papa, Rosanna; Garzoli, Stefania; Vrenna, Gianluca; Sabatino, Manuela; Sapienza, Filippo Umberto; Relucenti, Michela; Donfrancesco, Orlando; Vita Fiscarelli, Ersilia; Artini, Marco; Selan, Laura; Ragno, Rino. - In: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES. - ISSN 1661-6596. - 21:23(2020), pp. 1-20. [10.3390/ijms21239258]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1464626
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