Back

Novel insights into the biosynthesis and diversity of leaderless multipeptide bacteriocins

Oftedal, T. F.; Hermansen, S.; Haslund, E. A.; Telke, A. A.; Thorstensen, T.; Ovchinnikov, K. V.; Diep, D. B.; Kjos, M.

2026-09-07 microbiology
10.64898/2026.09.04.749385 bioRxiv
Show abstract

Garvicin KS (GarKS) is a three-peptide, leaderless, broad-spectrum bacteriocin that is active against a wide range of Gram-positive bacteria, including several foodborne pathogens and antibiotic-resistant strains. This bacteriocin is considered a candidate for application in food preservation and medical treatments; however, key knowledge about the producer strain, regulation of production, and the mechanism of action is still lacking for leaderless, multipeptide bacteriocins, including GarKS. A hybrid sequencing strategy was used to obtain a high-quality closed genome assembly of the native GarKS producer Lactococcus garvieae KS1546, which showed that the bacteriocin was encoded on a 50 kb plasmid (pKS50). Comparative analysis with updated sequence databases indicates that the producer strain should be reclassified as Lactococcus petauri. The roles of the biosynthetic genes, putatively encoding a transcriptional regulator (gakR) and immunity protein (gakI), were examined using heterologous expression. Removal of gakR resulted in a 6-fold decrease in GarKS production, and expression of gakI caused a 250-fold decrease in susceptibility to GarKS, with varying degrees of cross-immunity to other multipeptide bacteriocins. Isolation and whole-genome sequencing of spontaneous GarKS mutants in L. lactis showed that resistance levels are low, but that ythA, a PspC-domain-containing protein involved in a phage stress response pathway is involved in the GarKS susceptible phenotype. To decipher conserved features involved in production of these bacteriocins, we finally used genome mining to identify 11 candidates for new multipeptide bacteriocins; four of them were obtained synthetically and confirmed to be bioactive peptides inhibiting important pathogens including Listeria monocytogenes and enterococci.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.