4.7 Article

Influence of Different Cell-Penetrating Peptides on the Antimicrobial Efficiency of PNAs in Streptococcus pyogenes

Journal

MOLECULAR THERAPY-NUCLEIC ACIDS
Volume 18, Issue -, Pages 444-454

Publisher

CELL PRESS
DOI: 10.1016/j.omtn.2019.09.010

Keywords

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Funding

  1. Ministerium fur Bildung, Wissenschaft und Kultur, Mecklenburg-Vorpommern [ESF/14-BM-A55-0010/16]
  2. University Medicine Rostock [Forun 889008]

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Streptococcus pyogenes is an exclusively human pathogen causing a wide range of clinical manifestations from mild superficial infections to severe, life-threatening, invasive diseases. S. pyogenes is consistently susceptible toward penicillin, but therapeutic failure of penicillin treatment has been reported frequently. At the same time, streptococcal resistance to alternative antibiotics, e.g., macrolides, is common. To reduce the application of antibiotics for treatment of S. pyogenes infections, it is mandatory to develop novel therapeutic strategies. Antisense peptide nucleic acids (PNAs) are synthetic DNA derivatives widely applied for hybridization-based microbial diagnostics. They have a high potential as therapeutic agents, because PNA antisense targeting of essential genes was shown to reduce growth of several pathogenic bacterial species. Spontaneous cellular uptake of PNAs is restricted in eukaryotes and in bacteria. To overcome this problem, PNAs can be coupled to cell-penetrating peptides (CPPs) that support PNA translocation over the cell membrane. In bacteria, the efficiency of CPP-mediated PNA uptake is species specific. Previously, HIV-1 transactivator of transcription (HIV-1 TAT) peptide-coupled anti-gyrA PNA was shown to inhibit growth of S. pyogenes. Here, we investigate the effect of 18 CPP-coupled anti-gyrA PNAs on S. pyogenes growth and virulence. HIV-1 TAT, oligolysine (K8), and (RXR)(4)XB peptide-coupled anti-gyrA PNAs efficiently abolished bacterial growth in vitro. Consistently, treatment with these three CPP-PNAs increased survival of larvae in a Galleria mellonella infection model.

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