Evidence map›Paper›PMID 38075915›Full record

ArticleFrontiers in microbiology2023

Development of sensitizer peptide-fused endolysin Lys1S-L9P acting against multidrug-resistant gram-negative bacteria.

Su Min Son, Joonbeom Kim, Sangryeol Ryu

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Avian PathogenicVeterinary sciences · 2025
    Review
  5. Article
  6. Review
  7. Article
  8. Limitation of the Lytic Effect of Bacteriophages onInternational journal of microbiology · 2025
    Review
  9. Review
  10. Review
  11. Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Su Min Son *Department of Food and Animal Biotechnology, Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.
Joonbeom Kim *Department of Food and Animal Biotechnology, Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.
Sangryeol RyuDepartment of Food and Animal Biotechnology, Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of multidrug-resistant (MDR) bacteria poses a major threat to public health, garnering attention to novel antibiotic replacements. Endolysin, a bacteriophage-derived cell wall-degrading enzyme, is a promising alternative to conventional antibiotics. However, it is challenging to control Gram-negative bacteria due to the presence of the outer membrane that shields the peptidoglycan layer from enzymatic degradation. To overcome this threshold, we constructed the fusion endolysin Lys1S-L9P by combining endolysin LysSPN1S with KL-L9P, a sensitizer peptide known to extend efficacy of antibiotics by perturbing the outer membrane of Gram-negative bacteria. In addition, we established a new endolysin purification procedure that increases solubility allowing a 4-fold increase in production yield of Lys1S-L9P. The sensitizer peptide-fused endolysin Lys1S-L9P exhibited high bactericidal effects against many MDR Gram-negative pathogens and was more effective in eradicating biofilms compared to LysSPN1S. Moreover, Lys1S-L9P showed potential for clinical use, maintaining stability at various storage temperatures without cytotoxicity against human cells. In the

Indexed as

antimicrobial agentendolysinfusion endolysingram-negative bacteriamultidrug resistancesensitizer peptide

Identifiers

PMID38075915
PMCPMC10701683

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.