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Bacterial Outer-Membrane-Mimicking Giant Unilamellar Vesicle Model for Detecting Antimicrobial Permeability

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dc.contributor.author Nandi, S
dc.contributor.author Nair, K S
dc.contributor.author Bajaj, H
dc.date.accessioned 2023-11-04T12:14:37Z
dc.date.available 2023-11-04T12:14:37Z
dc.date.issued 2023-04-25
dc.identifier.citation Langmuir;39(16): 5891-5900 en_US
dc.identifier.uri https://doi.org/10.1021/acs.langmuir.3c00378
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/4583
dc.description.abstract The construction of bacterial outer membrane models with native lipids like lipopolysaccharide (LPS) is a barrier to understanding antimicrobial permeability at the membrane interface. Here, we engineer bacterial outer membrane (OM)- mimicking giant unilamellar vesicles (GUVs) by constituting LPS under different pH conditions and assembled GUVs with controlled dimensions. We quantify the LPS reconstituted in GUV membranes and reveal their arrangement in the leaflets of the vesicles. Importantly, we demonstrate the applications of OM vesicles by exploring antimicrobial permeability activity across membranes. Model peptides, melittin and magainin-2, are examined where both peptides exhibit lower membrane activity in OM vesicles than vesicles devoid of LPS. Our findings reveal the mode of action of antimicrobial peptides in bacterial-membrane-mimicking models. Notably, the critical peptide concentration required to elicit activity on model membranes correlates with the cell inhibitory concentrations that revalidate our models closely mimic bacterial membranes. In conclusion, we provide an OM-mimicking model capable of quantifying antimicrobial permeability across membranes. en_US
dc.language.iso en en_US
dc.publisher ACS Publications en_US
dc.subject Unilamellar Vesicle en_US
dc.subject Antimicrobial Permeability en_US
dc.title Bacterial Outer-Membrane-Mimicking Giant Unilamellar Vesicle Model for Detecting Antimicrobial Permeability en_US
dc.type Article en_US


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  • 2023
    Research articles authored by NIIST researchers published in 2023

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