Culture-independent metagenomic approaches have proven to be effective tools for identifying previously hidden biosynthetic gene clusters (BGCs) encoding novel natural products with potential medical relevance. However, producing these compounds remains challenging as metagenomic BGCs often originate from organisms phylogenetically distant from available heterologous hosts. Lasso peptides, a subclass of ribosomally synthesized and post-translationally modified peptide (RiPP) natural products, exhibit diverse bioactivities, yet no lasso peptide has previously been discovered directly from a metagenome. Here, we report the discovery and heterologous expression of the first soil metagenome-derived lasso peptide. Expression of its biosynthetic gene cluster in Escherichia coli, followed by mass spectrometry analysis, strongly supported the predicted amino acid sequence and lasso structure of the peptide. Notably, this lasso peptide is the first to feature asparagine as the ring-forming residue at position one. Taxonomic analysis of the corresponding BGC identified an uncultivated member of the Steroidobacterales family (Gammaproteobacteria) as the closest known relative of the potential native host. These findings underscore the potential of metagenomic genome mining to reveal structurally novel RiPPs and to expand our understanding of the natural diversity of lasso peptides.

Phylogenetic analysis is widely used to predict enzyme function, yet building annotated and reusable trees is labor-intensive and requires extensive knowledge about the specific enzymes. Existing reso...

To improve annotation in non-targeted metabolomics studies, authors develop a Multiplexed Chemical Metabolomics (MCheM) platform, combining post-column derivatization with integrated data processing. ...

The members of CMFI can breathe a sigh of relief: On January 1, 2026, the Tübingen Cluster of Excellence will enter its second funding period with a duration of seven years. This was announced by the ...

Abstract. Accurate and accessible phylogenetic analysis is essential for understanding microbial taxonomy and evolution, which are integral to microbiology

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The clustering of tandem mass spectra (MS/MS) is a crucial computational step to deduplicate repeated acquisitions in data-dependent experiments. This technique is essential in untargeted metabolomics, particularly with high-throughput mass spectrometers capable of generating hundreds of MS/MS spectra per second. Despite advancements in MS/MS clustering algorithms in proteomics, their performance in metabolomics has not been extensively evaluated due to the lack of database search tools with false discovery rate control for molecule identification. To bridge this gap, this study introduces the MS1-retention time (MS-RT) method to assess MS/MS clustering performance in metabolomics data sets. Here, we validate MS-RT by comparing MS-RT to established proteomics clustering evaluation approaches that utilize database search identifications. Additionally, we evaluate the performance of several MS/MS clustering tools on metabolomics data sets, highlighting their advantages and drawbacks. This MS-RT method and the MS/MS clustering tool benchmarking will provide valuable real world practical recommendations for tools and set the stage for future advancements in metabolomics MS/MS clustering.

Introduction The identification of lipids is a cornerstone of lipidomics, and due to the specific characteristics of lipids, it requires dedicated analysis workflows. Identifying novel lipids and lipi...

Version 8.0.0 UniDec. Please cite Marty et al. Anal. Chem. 2015. DOI: 10.1021/acs.analchem.5b00140 if you use UniDec in publications. Check out a video tutorial: https://www.youtube.com/watch?v=e33...

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