Enhanced correlation-based linking of biosynthetic gene clusters to their metabolic products through chemical class matching

Microbiome

Software

Joris J. R. Louwen¹  Marnix H. Medema¹  Justin J. J. van der Hooft² 

[1] Bioinformatics Group, Wageningen University & Research, 6708 PB, Wageningen, the Netherlands;Bioinformatics Group, Wageningen University & Research, 6708 PB, Wageningen, the Netherlands;Department of Biochemistry, University of Johannesburg, 2006, Johannesburg, South Africa;

关键词: Multi-omics;    Genome mining;    Genomics;    Metabolome mining;    Metabolomics;    Chemical compound classification;    Natural products;    Specialised metabolites;   

DOI  :  10.1186/s40168-022-01444-3

 received in 2022-06-17, accepted in 2022-12-07,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundIt is well-known that the microbiome produces a myriad of specialised metabolites with diverse functions. To better characterise their structures and identify their producers in complex samples, integrative genome and metabolome mining is becoming increasingly popular. Metabologenomic co-occurrence-based correlation scoring methods facilitate the linking of metabolite mass fragmentation spectra (MS/MS) to their cognate biosynthetic gene clusters (BGCs) based on shared absence/presence patterns of metabolites and BGCs in paired omics datasets of multiple strains. Recently, these methods have been made more readily accessible through the NPLinker platform. However, co-occurrence-based approaches usually result in too many candidate links to manually validate. To address this issue, we introduce a generic feature-based correlation method that matches chemical compound classes between BGCs and MS/MS spectra.ResultsTo automatically reduce the long lists of potential BGC-MS/MS spectrum links, we match natural product (NP) ontologies previously independently developed for genomics and metabolomics and developed NPClassScore: an empirical class matching score that we also implemented in the NPLinker platform. By applying NPClassScore on three paired omics datasets totalling 189 bacterial strains, we show that the number of links is reduced by on average 63% as compared to using a co-occurrence-based strategy alone. We further demonstrate that 96% of experimentally validated links in these datasets are retained and prioritised when using NPClassScore.ConclusionThe matching genome-metabolome class ontologies provide a starting point for selecting plausible candidates for BGCs and MS/MS spectra based on matching chemical compound class ontologies. NPClassScore expedites genome/metabolome data integration, as relevant BGC-metabolite links are prioritised, and researchers are faced with substantially fewer proposed BGC-MS/MS links to manually inspect. We anticipate that our addition to the NPLinker platform will aid integrative omics mining workflows in discovering novel NPs and understanding complex metabolic interactions in the microbiome.7Cdzcf-43_gfFdjHUXr8d9Video Abstract

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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