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[Preprint]. 2023 Aug 3:rs.3.rs-3189768.
doi: 10.21203/rs.3.rs-3189768/v1.

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

Simone Zuffa  1   2 Robin Schmid  1   2 Anelize Bauermeister  1   2   3 Paulo Wender P Gomes  1   2 Andres M Caraballo-Rodriguez  1   2 Yasin El Abiead  1   2 Allegra T Aron  4 Emily C Gentry  5 Jasmine Zemlin  1   6 Michael J Meehan  1 Nicole E Avalon  7 Robert H Cichewicz  8 Ekaterina Buzun  9 Marvic Carrillo Terrazas  9 Chia-Yun Hsu  9 Renee Oles  9 Adriana Vasquez Ayala  9 Jiaqi Zhao  9 Hiutung Chu  9   10 Mirte C M Kuijpers  11 Sara L Jackrel  11 Fidele Tugizimana  12   13 Lerato Pertunia Nephali  12 Ian A Dubery  12 Ntakadzeni Edwin Madala  14 Eduarda Antunes Moreira  15 Leticia Veras Costa-Lotufo  3 Norberto Peporine Lopes  15 Paula Rezende-Teixeira  3 Paula C Jimenez  16 Bipin Rimal  17 Andrew D Patterson  18 Matthew F Traxler  19 Rita de Cassia Pessotti  19 Daniel Alvarado-Villalobos  20 Giselle Tamayo-Castillo  20   21 Priscila Chaverri  22   23   24 Efrain Escudero-Leyva  25 Luis-Manuel Quiros-Guerrero  26   27 Alexandre Jean Bory  26   27 Juliette Joubert  26   27 Adriano Rutz  26   27   28 Jean-Luc Wolfender  26   27 Pierre-Marie Allard  29   26   27 Andreas Sichert  28 Sammy Pontrelli  28 Benjamin S Pullman  30 Nuno Bandeira  30   1 William H Gerwick  31   1 Katia Gindro  32 Josep Massana-Codina  32 Berenike C Wagner  33 Karl Forchhammer  33 Daniel Petras  34 Nicole Aiosa  35 Neha Garg  35   36 Manuel Liebeke  37 Patric Bourceau  37 Kyo Bin Kang  38 Henna Gadhavi  39   40 Luiz Pedro Sorio de Carvalho  39   41 Mariana Silva Dos Santos  42 Alicia Isabel Pérez-Lorente  43 Carlos Molina-Santiago  43 Diego Romero  43 Raimo Franke  44 Mark Brönstrup  44   45 Arturo Vera Ponce de León  46 Phillip Byron Pope  47   46 Sabina Leanti La Rosa  46 Giorgia La Barbera  48 Henrik M Roager  48 Martin Frederik Laursen  49 Fabian Hammerle  50 Bianka Siewert  50 Ursula Peintner  51 Cuauhtemoc Licona-Cassani  52 Lorena Rodriguez-Orduña  52 Evelyn Rampler  53 Felina Hildebrand  53   54 Gunda Koellensperger  53   55 Harald Schoeny  53 Katharina Hohenwallner  53   54 Lisa Panzenboeck  53   54 Rachel Gregor  56 Ellis Charles O'Neill  57 Eve Tallulah Roxborough  57 Jane Odoi  58 Nicole J Bale  59 Su Ding  59 Jaap S Sinninghe Damsté  59 Xueli Li Guan  60 Jerry J Cui  61 Kou-San Ju  61   62   63   64 Denise Brentan Silva  65 Fernanda Motta Ribeiro Silva  65 Gilvan Ferreira da Silva  66 Hector H F Koolen  67 Carlismari Grundmann  68 Jason A Clement  69 Hosein Mohimani  70 Kirk Broders  71 Kerry L McPhail  72 Sidnee E Ober-Singleton  73 Christopher M Rath  74 Daniel McDonald  75 Rob Knight  75   30   76 Mingxun Wang  77 Pieter C Dorrestein  1   2
Affiliations

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

Simone Zuffa et al. Res Sq. .

Update in

  • microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data.
    Zuffa S, Schmid R, Bauermeister A, P Gomes PW, Caraballo-Rodriguez AM, El Abiead Y, Aron AT, Gentry EC, Zemlin J, Meehan MJ, Avalon NE, Cichewicz RH, Buzun E, Terrazas MC, Hsu CY, Oles R, Ayala AV, Zhao J, Chu H, Kuijpers MCM, Jackrel SL, Tugizimana F, Nephali LP, Dubery IA, Madala NE, Moreira EA, Costa-Lotufo LV, Lopes NP, Rezende-Teixeira P, Jimenez PC, Rimal B, Patterson AD, Traxler MF, Pessotti RC, Alvarado-Villalobos D, Tamayo-Castillo G, Chaverri P, Escudero-Leyva E, Quiros-Guerrero LM, Bory AJ, Joubert J, Rutz A, Wolfender JL, Allard PM, Sichert A, Pontrelli S, Pullman BS, Bandeira N, Gerwick WH, Gindro K, Massana-Codina J, Wagner BC, Forchhammer K, Petras D, Aiosa N, Garg N, Liebeke M, Bourceau P, Kang KB, Gadhavi H, de Carvalho LPS, Silva Dos Santos M, Pérez-Lorente AI, Molina-Santiago C, Romero D, Franke R, Brönstrup M, Vera Ponce de León A, Pope PB, La Rosa SL, La Barbera G, Roager HM, Laursen MF, Hammerle F, Siewert B, Peintner U, Licona-Cassani C, Rodriguez-Orduña L, Rampler E, Hildebrand F, Koellensperger G, Schoeny H, Hohenwallner K, Panzenboeck L, Gregor R, O'Neill EC, Roxborough ET, Odoi J, Bale NJ, Ding S, Sinninghe Damsté JS, Guan XL, Cui JJ, Ju KS, Silva DB, Si… See abstract for full author list ➔ Zuffa S, et al. Nat Microbiol. 2024 Feb;9(2):336-345. doi: 10.1038/s41564-023-01575-9. Epub 2024 Feb 5. Nat Microbiol. 2024. PMID: 38316926 Free PMC article.

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.

Keywords: bacteria; fungi; metabolomics; microbiome; search tool.

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Conflict of interest statement

Disclosures PCD is an advisor to Cybele, consulted for MSD animal health in 2023, and he is a Co-founder and scientific advisor for Ometa Labs, Arome, and Enveda with prior approval by UC San Diego. MW is a Co-founder of Ometa labs. There are no known conflicts of interest in this work by the USDA, Agricultural Research Service, National Center for Agricultural Utilization Research, Mycotoxin Prevention and Applied Microbiology Research Unit. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.

Figures

Figure 1.
Figure 1.. The microbeMASST search tool and reference database.
a) Community contributions of data and knowledge to GNPS, ReDU, and MassIVE from 2014 to 2022 were used to generate the microbeMASST reference database. Additionally, a public invitation to deposit data in June 2022 resulted in the further deposition of LC-MS/MS files from 25 different laboratories from 15 different countries across the globe, leading to the curation of a total of 60,781 LC-MS/MS files of microbial monoculture extracts. b) MicrobeMASST comprises 1,858 unique lineages, across three different domains of life, mapped to 541 unique strains, 1,336 species, 539 genera, 264 families, 109 orders, 41 classes, and 16 phyla. c) Examples of medically-relevant small molecules known to be produced by bacteria or fungi. Lovastatin, a cholesterol lowering drug originally isolated from Aspergillus genus, salinosporamide A, a Phase III candidate to treat glioblastoma produced by Salinispora tropica, and commendamide, a human G-protein–coupled receptor agonist. d) MicrobeMASST search outputs of the three different molecules of interest confirm that they were exclusively found in monocultures of the only known producers. Pie charts display the proportion of MS/MS matches found in the deposited reference database. Blue indicates a match with a monoculture, while yellow represents a nonmatch. Searches were performed using MS/MS spectra deposited in the GNPS reference library: lovastatin (CCMSLIB00005435737), salinosporamide A (CCMSLIB00010013003), and commendamide (CCMSLIB00004679239).
Figure 2.
Figure 2.. MicrobeMASST can identify microbial MS/MS spectra within mouse and human datasets.
a) Workflow to extract microbial MS/MS spectra from biochemical profiles of 29 different tissues and biofluids of SPF mice that are not observed in GF mice. The MS/MS spectra unique to SPF mice (10,047) were searched with microbeMASST. A total of 3,262 MS/MS spectra had a match; those MS/MS also matching human cell lines were removed, leaving a total of 2,425 putative microbial MS/MS spectra (see Methods to download .mgf file). b) The presence of the 2,425 MS/MS spectra was evaluated in an additional animal study looking at antibiotics usage. A total of 512 MS/MS spectra, out of the 621 overlapping, was exclusively found in animals not receiving antibiotics. c) UpSet plot of the distribution of the detected MS/MS spectra (512) across bacterial phyla. Terpenoids were more commonly observed across phyla while amino acids and peptides appeared to be more phylum specific d) The 512 MS/MS spectra were searched in human datasets and 455 were found to have a match. These MS/MS spectra were present in both healthy individuals and individuals affected by different diseases. e) Most of the MS/MS spectra (n=411) matched fecal samples (n=110,973 matches), followed by blood, oral cavity, breast milk, urine, and several other organs. Abbreviations: CSF, cerebral spinal fluid; HIV, human immunodeficiency virus; PBI, primary bacterial infectious disease; SD, sleep disorder; AD, Alzheimer’s disease; IS, ischemic stroke; KD, Kawasaki disease; IBD, inflammatory bowel disease; T2D, type II diabetes.

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