Literature DB >> 30521892

The novel cationic cell-penetrating peptide PEP-NJSM is highly active against Staphylococcus epidermidis biofilm.

Sami Mnif1, Marwa Jardak2, Imen Graiet3, Salwa Abid4, Dorra Driss2, Najla Kharrat2.   

Abstract

A cationic cell-penetrating peptide PEP-NJSM was identified in human virus proteomes by a screening of charge clusters in protein sequences generating Cell-Penetrating Peptides (CPP). PEP-NJSM was selectively active against Gram-positive Staphylococcus epidermidis as antibacterial agent with MIC value of 128 μM compared to the Gram-negative Pseudomonas aeruginosa strain with MIC value exceeded 512 μM. The selected peptide exhibited an important anti-biofilm activity even at sub-MIC levels. PEP-NJSM could prevent biofilm formation and increase the mortality of cells inside mature S. epidermidis biofilm. The results demonstrated that PEP-NJSM presented an important anti-adherent activity. It showed a S. epidermidis inhibition of biofilm formation >84% at a concentration of 256 μM (2 X MIC) and remained active even at a concentration of 4 μM with 32% of inhibition. The eradication of the established biofilm was observed at a concentration of 256 μM with 55.7% of biofilm eradication. The peptide was active against mature biofilm even at low concentration of 0.5 μM with approximately 22.9% of eradication. PEP-NJSM exhibited low hemolytic activity and cytotoxicity against mammalian cells. Our results demonstrate that PEP-NJSM could have a potential role in the treatment of diseases related to Staphylococcus epidermidis infection.
Copyright © 2018. Published by Elsevier B.V.

Entities:  

Keywords:  Anti-biofilm; Antibacterial; CPP/AMP; Fluorescence microscopy; SEM

Mesh:

Substances:

Year:  2018        PMID: 30521892     DOI: 10.1016/j.ijbiomac.2018.12.008

Source DB:  PubMed          Journal:  Int J Biol Macromol        ISSN: 0141-8130            Impact factor:   6.953


  5 in total

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Journal:  Appl Microbiol Biotechnol       Date:  2019-08-14       Impact factor: 4.813

4.  Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space.

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Journal:  Sci Rep       Date:  2021-04-07       Impact factor: 4.379

Review 5.  Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity.

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Journal:  Pharmaceuticals (Basel)       Date:  2022-03-08
  5 in total

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