• Tiago F. Leão
  • Mingxun Wang
  • Ricardo da Silva
  • Alexey Gurevich
  • Anelize Bauermeister
  • Paulo Wender P. Gomes
  • Asker Brejnrod
  • Evgenia Glukhov
  • Allegra T. Aron
  • Joris J. R. Louwen
  • Hyun Woo Kim
  • Raphael Reher
  • Marli F. Fiore
  • Justin J.J. van der Hooft
  • Lena Gerwick
  • William H. Gerwick
  • Nuno Bandeira
  • Pieter C. Dorrestein
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.
Original languageEnglish
Article numberpgac257
JournalPNAS Nexus
Early online date26 Nov 2022
StateE-pub ahead of print - 26 Nov 2022

    Research areas

  • genomics, mass spectrometry, machine learning, Specialized metabolites, biosynthetic gene clusters

ID: 100483223