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A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data.

ABSTRACT: MicrobeMASST, a taxonomically-informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbial-derived metabolites and relative producers, without a priori knowledge, will vastly enhance the understanding of microorganisms' role in ecology and human health.

SUBMITTER: Zuffa S

PROVIDER: S-EPMC10418563 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data.

Zuffa Simone S Schmid Robin R Bauermeister Anelize A Gomes Paulo Wender P PWP Caraballo-Rodriguez Andres M AM Abiead Yasin El YE Aron Allegra T AT Gentry Emily C EC Zemlin Jasmine J Meehan Michael J MJ Avalon Nicole E NE Cichewicz Robert H RH Buzun Ekaterina E Terrazas Marvic Carrillo MC Hsu Chia-Yun CY Oles Renee R Ayala Adriana Vasquez AV Zhao Jiaqi J Chu Hiutung H Kuijpers Mirte C M MCM Jackrel Sara L SL Tugizimana Fidele F Nephali Lerato Pertunia LP Dubery Ian A IA Madala Ntakadzeni Edwin NE Moreira Eduarda Antunes EA Costa-Lotufo Leticia Veras LV Lopes Norberto Peporine NP Rezende-Teixeira Paula P Jimenez Paula C PC Rimal Bipin B Patterson Andrew D AD Traxler Matthew F MF de Cassia Pessotti Rita R Alvarado-Villalobos Daniel D Tamayo-Castillo Giselle G Chaverri Priscila P Escudero-Leyva Efrain E Quiros-Guerrero Luis-Manuel LM Bory Alexandre Jean AJ Joubert Juliette J Rutz Adriano A Wolfender Jean-Luc JL Allard Pierre-Marie PM Sichert Andreas A Pontrelli Sammy S Pullman Benjamin S BS Bandeira Nuno N Gerwick William H WH Gindro Katia K Massana-Codina Josep J Wagner Berenike C BC Forchhammer Karl K Petras Daniel D Aiosa Nicole N Garg Neha N Liebeke Manuel M Bourceau Patric P Kang Kyo Bin KB Gadhavi Henna H de Carvalho Luiz Pedro Sorio LPS Dos Santos Mariana Silva MS Pérez-Lorente Alicia Isabel AI Molina-Santiago Carlos C Romero Diego D Franke Raimo R Brönstrup Mark M de León Arturo Vera Ponce AVP Pope Phillip Byron PB Rosa Sabina Leanti La SL Barbera Giorgia La G Roager Henrik M HM Laursen Martin Frederik MF Hammerle Fabian F Siewert Bianka B Peintner Ursula U Licona-Cassani Cuauhtemoc C Rodriguez-Orduña Lorena L Rampler Evelyn E Hildebrand Felina F Koellensperger Gunda G Schoeny Harald H Hohenwallner Katharina K Panzenboeck Lisa L Gregor Rachel R O'Neill Ellis Charles EC Roxborough Eve Tallulah ET Odoi Jane J Bale Nicole J NJ Ding Su S Sinninghe Damsté Jaap S JS Guan Xueli Li XL Cui Jerry J JJ Ju Kou-San KS Silva Denise Brentan DB Silva Fernanda Motta Ribeiro FMR da Silva Gilvan Ferreira GF Koolen Hector H F HHF Grundmann Carlismari C Clement Jason A JA Mohimani Hosein H Broders Kirk K McPhail Kerry L KL Ober-Singleton Sidnee E SE Rath Christopher M CM McDonald Daniel D Knight Rob R Wang Mingxun M Dorrestein Pieter C PC

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MicrobeMASST, a taxonomically-informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbial-derived metabolites and relative producers, without <i>a priori</i> knowledge, will vastly enhance the unde ...[more]

PMID: 37577622

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Project description:BackgroundAssessing the reproducibility of measurements is an important first step for improving the reliability of downstream analyses of high-throughput metabolomics experiments. We define a metabolite to be reproducible when it demonstrates consistency across replicate experiments. Similarly, metabolites which are not consistent across replicates can be labeled as irreproducible. In this work, we introduce and evaluate the use (Ma)ximum (R)ank (R)eproducibility (MaRR) to examine reproducibility in mass spectrometry-based metabolomics experiments. We examine reproducibility across technical or biological samples in three different mass spectrometry metabolomics (MS-Metabolomics) data sets.ResultsWe apply MaRR, a nonparametric approach that detects the change from reproducible to irreproducible signals using a maximal rank statistic. The advantage of using MaRR over model-based methods that it does not make parametric assumptions on the underlying distributions or dependence structures of reproducible metabolites. Using three MS Metabolomics data sets generated in the multi-center Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) study, we applied the MaRR procedure after data processing to explore reproducibility across technical or biological samples. Under realistic settings of MS-Metabolomics data, the MaRR procedure effectively controls the False Discovery Rate (FDR) when there was a gradual reduction in correlation between replicate pairs for less highly ranked signals. Simulation studies also show that the MaRR procedure tends to have high power for detecting reproducible metabolites in most situations except for smaller values of proportion of reproducible metabolites. Bias (i.e., the difference between the estimated and the true value of reproducible signal proportions) values for simulations are also close to zero. The results reported from the real data show a higher level of reproducibility for technical replicates compared to biological replicates across all the three different datasets. In summary, we demonstrate that the MaRR procedure application can be adapted to various experimental designs, and that the nonparametric approach performs consistently well.ConclusionsThis research was motivated by reproducibility, which has proven to be a major obstacle in the use of genomic findings to advance clinical practice. In this paper, we developed a data-driven approach to assess the reproducibility of MS-Metabolomics data sets. The methods described in this paper are implemented in the open-source R package marr, which is freely available from Bioconductor at http://bioconductor.org/packages/marr .

Dataset Information

A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data.

Publications

A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data.

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