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Rule-based omics mining reveals antimicrobial macrocyclic peptides against drug-resistant clinical isolates
Research Highlights/2024.06.22

AbstractAntimicrobial resistance remains a significant global threat, driving up mortality rates worldwide. Ribosomally synthesized and post-translati...

Abstract

Antimicrobial resistance remains a significant global threat, driving up mortality rates worldwide. Ribosomally synthesized and post-translationally modified peptides have emerged as a promising source of novel peptide antibiotics due to their diverse chemical structures. Here, we report the discovery of new aminovinyl-(methyl)cysteine (Avi(Me)Cys)-containing peptide antibiotics through a synergistic approach combining biosynthetic rule-based omics mining and heterologous expression. We first bioinformatically identify 1172 RiPP biosynthetic gene clusters (BGCs) responsible for Avi(Me)Cys-containing peptides formation from a vast pool of over 50,000 bacterial genomes. Subsequently, we successfully establish the connection between three identified BGCs and the biosynthesis of five peptide antibiotics via biosynthetic rule-guided metabolic analysis. Notably, we discover a class V lanthipeptide, massatide A, which displays excellent activity against gram-positive pathogens, including drug-resistant clinical isolates like linezolid-resistant S. aureus and methicillin-resistant S. aureus, with a minimum inhibitory concentration of 0.25 μg/mL. The remarkable performance of massatide A in an animal infection model, coupled with a relatively low risk of resistance and favorable safety profile, positions it as a promising candidate for antibiotic development. Our study highlights the potential of Avi(Me)Cys-containing peptides in expanding the arsenal of antibiotics against multi-drug-resistant bacteria, offering promising drug leads in the ongoing battle against infectious diseases.

Title

Rule-based omics mining reveals antimicrobial macrocyclic peptides against drug-resistant clinical isolates

Authors

Zhuo Cheng, Bei-Bei He, Kangfan Lei, Ying Gao, Yuqi Shi, Zheng Zhong, Hongyan Liu, Runze Liu, Haili Zhang, Song Wu, Wenxuan Zhang, Xiaoyu Tang & Yong-Xin Li

Journal

Nature Communications 15, 4901 (2024)

DOI

https://doi.org/10.1038/s41467-024-49215-y

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