NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters

Abstract

Microbial specialized metabolites are an important source of and inspiration for many pharmaceutical, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra to their corresponding previously experimentally validated biosynthetic genes (e.g. via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to combine NPOmix with MassQL for mining siderophores that can be reproduced by NPOmix users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.

Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

“…Along with advances in metagenomics and increased accessibility of high-quality Metagenome-Assembled Genome (MAG), important future developments in metabolomics tools for aquatic microbial ecology may include the better integration of MAGs and genome mining strategies with metabolomic data. New tools and infrastructures like NPOmix and The Paired Omics Data Platform pave the way to connect thereby molecules to their biosynthesis genes [124][125][126]147]. It is estimated that two-thirds of the work on a multi-omic projects goes to data processing and integration, highlighting how the current data analysis approaches are a time and energy consuming processes, with much room for improvement [148].…”

Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS

“…Currently, two types of tools bridge metabolomics and genomic data. Correlation-based approaches like NPlinker 35 , NPOmix 36 , and other metabolomics methods 37 – 39 focus on linking gene cluster families (GCFs) with molecular families (MFs) or spectra, based on co-occurrence of molecular features and BGCs. Conversely, feature-based approaches like GNP 18 , MetaMiner 40 , Seq2RiPP 41 , and NRPminer 42 strive to associate BGCs with mass spectra by predicting the hypothetical structure of BGC products, followed by in silico mass spectral database search.…”