Functional metabolomics of the human scalp: a metabolic niche for Staphylococcus epidermidis
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
Grantová podpora
California Research Alliance (CARA)
BASF | BASF Corporation
PubMed
38206014
PubMed Central
PMC10878091
DOI
10.1128/msystems.00356-23
Knihovny.cz E-zdroje
- Klíčová slova
- metabolite annotation, metabolomics, multi-omics integration, scalp, skin microbiome,
- MeSH
- chromatografie kapalinová MeSH
- lidé MeSH
- metabolomika metody MeSH
- skalp * MeSH
- Staphylococcus epidermidis * MeSH
- tandemová hmotnostní spektrometrie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Although metabolomics data acquisition and analysis technologies have become increasingly sophisticated over the past 5-10 years, deciphering a metabolite's function from a description of its structure and its abundance in a given experimental setting is still a major scientific and intellectual challenge. To point out ways to address this "data to knowledge" challenge, we developed a functional metabolomics strategy that combines state-of-the-art data analysis tools and applied it to a human scalp metabolomics data set: skin swabs from healthy volunteers with normal or oily scalp (Sebumeter score 60-120, n = 33; Sebumeter score > 120, n = 41) were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), yielding four metabolomics data sets for reversed phase chromatography (C18) or hydrophilic interaction chromatography (HILIC) separation in electrospray ionization (ESI) + or - ionization mode. Following our data analysis strategy, we were able to obtain increasingly comprehensive structural and functional annotations, by applying the Global Natural Product Social Networking (M. Wang, J. J. Carver, V. V. Phelan, L. M. Sanchez, et al., Nat Biotechnol 34:828-837, 2016, https://doi.org/10.1038/nbt.3597), SIRIUS (K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, et al., Nat Methods 16:299-302, 2019, https://doi.org/10.1038/s41592-019-0344-8), and MicrobeMASST (S. ZuffaS, R. Schmid, A. Bauermeister, P. W, P. Gomes, et al., bioRxiv:rs.3.rs-3189768, 2023, https://doi.org/10.21203/rs.3.rs-3189768/v1) tools. We finally combined the metabolomics data with a corresponding metagenomic sequencing data set using MMvec (J. T. Morton, A. A. Aksenov, L. F. Nothias, J. R. Foulds, et. al., Nat Methods 16:1306-1314, 2019, https://doi.org/10.1038/s41592-019-0616-3), gaining insights into the metabolic niche of one of the most prominent microbes on the human skin, Staphylococcus epidermidis.IMPORTANCESystems biology research on host-associated microbiota focuses on two fundamental questions: which microbes are present and how do they interact with each other, their host, and the broader host environment? Metagenomics provides us with a direct answer to the first part of the question: it unveils the microbial inhabitants, e.g., on our skin, and can provide insight into their functional potential. Yet, it falls short in revealing their active role. Metabolomics shows us the chemical composition of the environment in which microbes thrive and the transformation products they produce. In particular, untargeted metabolomics has the potential to observe a diverse set of metabolites and is thus an ideal complement to metagenomics. However, this potential often remains underexplored due to the low annotation rates in MS-based metabolomics and the necessity for multiple experimental chromatographic and mass spectrometric conditions. Beyond detection, prospecting metabolites' functional role in the host/microbiome metabolome requires identifying the biological processes and entities involved in their production and biotransformations. In the present study of the human scalp, we developed a strategy to achieve comprehensive structural and functional annotation of the metabolites in the human scalp environment, thus diving one step deeper into the interpretation of "omics" data. Leveraging a collection of openly accessible software tools and integrating microbiome data as a source of functional metabolite annotations, we finally identified the specific metabolic niche of Staphylococcus epidermidis, one of the key players of the human skin microbiome.
BASF Beauty Care Solutions France S A S Lyon Cedex France
BASF Corporation Research Triangle Park North Carolina USA
BASF Corporation Tarrytown New York USA
BASF Metabolome Solutions GmbH Berlin Germany
Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Prague Czechia
Zobrazit více v PubMed
Fan Y, Pedersen O. 2021. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol 19:55–71. doi:10.1038/s41579-020-0433-9 PubMed DOI
Byrd AL, Belkaid Y, Segre JA. 2018. The human skin microbiome. Nat Rev Microbiol 16:143–155. doi:10.1038/nrmicro.2017.157 PubMed DOI
Nakatsuji T, Chen TH, Butcher AM, Trzoss LL, Nam S-J, Shirakawa KT, Zhou W, Oh J, Otto M, Fenical W, Gallo RL. 2018. A commensal strain of Staphylococcus epidermidis protects against skin neoplasia. Sci Adv 4:eaao4502. doi:10.1126/sciadv.aao4502 PubMed DOI PMC
Dührkop K, Fleischauer M, Ludwig M, Aksenov AA, Melnik AV, Meusel M, Dorrestein PC, Rousu J, Böcker S. 2019. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat Methods 16:299–302. doi:10.1038/s41592-019-0344-8 PubMed DOI
Aksenov AA, da Silva R, Knight R, Lopes NP, Dorrestein PC. 2017. Global chemical analysis of biology by mass spectrometry. Nat Rev Chem 1:1–20. doi:10.1038/s41570-017-0054 DOI
Morton JT, Aksenov AA, Nothias LF, Foulds JR, Quinn RA, Badri MH, Swenson TL, Van Goethem MW, Northen TR, Vazquez-Baeza Y, Wang M, Bokulich NA, Watters A, Song SJ, Bonneau R, Dorrestein PC, Knight R. 2019. Learning representations of microbe-metabolite interactions. Nat Methods 16:1306–1314. doi:10.1038/s41592-019-0616-3 PubMed DOI PMC
Cai Y, Zhou Z, Zhu Z-J. 2023. Advanced analytical and Informatic strategies for metabolite annotation in untargeted metabolomics. TrAC Trends in Analytical Chemistry 158:116903. doi:10.1016/j.trac.2022.116903 DOI
Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, Nguyen DD, Watrous J, Kapono CA, Luzzatto-Knaan T, et al. . 2016. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat Biotechnol 34:828–837. doi:10.1038/nbt.3597 PubMed DOI PMC
Dührkop K, Nothias L-F, Fleischauer M, Reher R, Ludwig M, Hoffmann MA, Petras D, Gerwick WH, Rousu J, Dorrestein PC, Böcker S. 2021. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol 39:462–471. doi:10.1038/s41587-020-0740-8 PubMed DOI
Zuffa S, Schmid R, Bauermeister A, Gomes PWP, Caraballo-Rodriguez AM, Abiead YE, Aron AT, Gentry EC, Zemlin J, Meehan MJ, et al. . 2023. A taxonomically-informed mass spectrometry search tool for microbial metabolomics data. bioRxiv:rs.3.rs-3189768. doi:10.21203/rs.3.rs-3189768/v1 PubMed DOI PMC
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. 2003. Cytoscape: a software environment for integrated models of Biomolecular interaction networks. Genome Res 13:2498–2504. doi:10.1101/gr.1239303 PubMed DOI PMC
Bouslimani A, Porto C, Rath CM, Wang M, Guo Y, Gonzalez A, Berg-Lyon D, Ackermann G, Moeller Christensen GJ, Nakatsuji T, Zhang L, Borkowski AW, Meehan MJ, Dorrestein K, Gallo RL, Bandeira N, Knight R, Alexandrov T, Dorrestein PC. 2015. Molecular cartography of the human skin surface in 3D. Proc Natl Acad Sci USA 112:2120–2129. doi:10.1073/pnas.1424409112 PubMed DOI PMC
Schmid R, Heuckeroth S, Korf A, Smirnov A, Myers O, Dyrlund TS, Bushuiev R, Murray KJ, Hoffmann N, Lu M, et al. . 2023. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol 41:447–449. doi:10.1038/s41587-023-01690-2 PubMed DOI PMC
Schmid R, Petras D, Nothias L-F, Wang M, Aron AT, Jagels A, Tsugawa H, Rainer J, Garcia-Aloy M, Dührkop K, et al. . 2021. Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nat Commun 12:3832. doi:10.1038/s41467-021-23953-9 PubMed DOI PMC
Nothias L-F, Petras D, Schmid R, Dührkop K, Rainer J, Sarvepalli A, Protsyuk I, Ernst M, Tsugawa H, Fleischauer M, et al. . 2020. Feature-based molecular networking in the GNPS analysis environment. Nat Methods 17:905–908. doi:10.1038/s41592-020-0933-6 PubMed DOI PMC
Ludwig M, Nothias L-F, Dührkop K, Koester I, Fleischauer M, Hoffmann MA, Petras D, Vargas F, Morsy M, Aluwihare L, Dorrestein PC, Böcker S. 2020. Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. Nat Mach Intell 2:629–641. doi:10.1038/s42256-020-00234-6 DOI
Dührkop K, Shen H, Meusel M, Rousu J, Böcker S. 2015. Searching molecular structure databases with tandem mass spectra using CSI:Fingerid. Proc Natl Acad Sci USA 112:12580–12585. doi:10.1073/pnas.1509788112 PubMed DOI PMC
Johansson I, Svensson M. 2001. Surfactants based on fatty acids and other natural hydrophobes. Curr Opin Colloid Interface Sci 6:178–188. doi:10.1016/S1359-0294(01)00076-0 DOI
Kruis AJ, Bohnenkamp AC, Patinios C, van Nuland YM, Levisson M, Mars AE, van den Berg C, Kengen SWM, Weusthuis RA. 2019. Microbial production of short and medium chain esters: enzymes, pathways, and applications. Biotechnol Adv 37:107407. doi:10.1016/j.biotechadv.2019.06.006 PubMed DOI
Ludovici M, Kozul N, Materazzi S, Risoluti R, Picardo M, Camera E. 2018. Influence of the sebaceous gland density on the stratum corneum lipidome. Sci Rep 8:11500. doi:10.1038/s41598-018-29742-7 PubMed DOI PMC
Wang M, Jarmusch AK, Vargas F, Aksenov AA, Gauglitz JM, Weldon K, Petras D, da Silva R, Quinn R, Melnik AV, et al. . 2020. Mass spectrometry searches using MASST. Nat Biotechnol 38:23–26. doi:10.1038/s41587-019-0375-9 PubMed DOI PMC
Brown MM, Horswill AR. 2020. Staphylococcus epidermidis—skin friend or foe? PLoS Pathog 16:e1009026. doi:10.1371/journal.ppat.1009026 PubMed DOI PMC
Dambrova M, Makrecka-Kuka M, Kuka J, Vilskersts R, Nordberg D, Attwood MM, Smesny S, Sen ZD, Guo AC, Oler E, Tian S, Zheng J, Wishart DS, Liepinsh E, Schiöth HB. 2022. Acylcarnitines: nomenclature, biomarkers, therapeutic potential, drug targets, and clinical trials. Pharmacol Rev 74:506–551. doi:10.1124/pharmrev.121.000408 PubMed DOI
Pappas A. 2009. Epidermal surface lipids. Dermatoendocrinol 1:72–76. doi:10.4161/derm.1.2.7811 PubMed DOI PMC
Geiger O, López-Lara IM, Sohlenkamp C. 2013. Phosphatidylcholine biosynthesis and function in bacteria. Biochim Biophys Acta 1831:503–513. doi:10.1016/j.bbalip.2012.08.009 PubMed DOI
Hamanaka S, Hara M, Nishio H, Otsuka F, Suzuki A, Uchida Y. 2002. Human epidermal glucosylceramides are major precursors of stratum corneum ceramides. J Invest Dermatol 119:416–423. doi:10.1046/j.1523-1747.2002.01836.x PubMed DOI
Nguyen AV, Soulika AM. 2019. The Dynamics of the skin’s immune system. Int J Mol Sci 20:1811. doi:10.3390/ijms20081811 PubMed DOI PMC
Protsyuk I, Melnik AV, Nothias L-F, Rappez L, Phapale P, Aksenov AA, Bouslimani A, Ryazanov S, Dorrestein PC, Alexandrov T. 2018. 3D molecular cartography using LC-MS facilitated by optimus and ’Ili software. Nat Protoc 13:134–154. doi:10.1038/nprot.2017.122 PubMed DOI
Bushnell B. 2014. BBmap: a fast, accurate, splice-aware Aligner. Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States).
Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359. doi:10.1038/nmeth.1923 PubMed DOI PMC
Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, Tett A, Huttenhower C, Segata N. 2015. MetaPhlan2 for enhanced metagenomic taxonomic profiling. Nat Methods 12:902–903. doi:10.1038/nmeth.3589 PubMed DOI
Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, Gatto L, Fischer B, Pratt B, Egertson J, et al. . 2012. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 30:918–920. doi:10.1038/nbt.2377 PubMed DOI PMC
Pluskal T, Castillo S, Villar-Briones A, Oresic M. 2010. Mzmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11:395. doi:10.1186/1471-2105-11-395 PubMed DOI PMC
Schmid R, Petras D, Nothias L-F, Wang M, Aron AT, Jagels A, Tsugawa H, Rainer J, Garcia-Aloy M, Dührkop K, et al. . 2020. Ion identity molecular networking in the GNPS environment. Bioinformatics. Cold Spring Harbor Laboratory. doi:10.1101/2020.05.11.088948 PubMed DOI PMC
Du X, Smirnov A, Pluskal T, Jia W, Sumner S. 2020. Metabolomics data preprocessing using ADAP and MZmine 2. Methods Mol Biol 2104:25–48. doi:10.1007/978-1-0716-0239-3_3 PubMed DOI PMC
Böcker S, Letzel MC, Lipták Z, Pervukhin A. 2009. SIRIUS: decomposing Isotope patterns for metabolite identification. Bioinformatics 25:218–224. doi:10.1093/bioinformatics/btn603 PubMed DOI PMC
Böcker S, Dührkop K. 2016. Fragmentation trees reloaded. J Cheminform 8:5. doi:10.1186/s13321-016-0116-8 PubMed DOI PMC
Kim H, Wang M, Leber C, Nothias L-F, Reher R, Kang KB, van der Hooft JJJ, Dorrestein P, Gerwick W, Cottrell G. 2020. NPClassifier: a deep neural network-based structural classification tool for natural products. ChemRxiv. doi:10.26434/chemrxiv.12885494 PubMed DOI PMC
Ripley BD. 2001. The R project in statistical computing. MSOR connections the newsletter of the LTSN maths. doi:10.11120/msor.2001.01010023 DOI
Petras D, Phelan VV, Acharya D, Allen AE, Aron AT, Bandeira N, Bowen BP, Belle-Oudry D, Boecker S, Cummings DA, et al. . 2022. GNPS dashboard: collaborative exploration of mass spectrometry data in the web browser. Nat Methods 19:134–136. doi:10.1038/s41592-021-01339-5 PubMed DOI PMC
Deutsch EW, Perez-Riverol Y, Carver J, Kawano S, Mendoza L, Van Den Bossche T, Gabriels R, Binz P-A, Pullman B, Sun Z, Shofstahl J, Bittremieux W, Mak TD, Klein J, Zhu Y, Lam H, Vizcaíno JA, Bandeira N. 2021. Universal spectrum Identifier for mass spectra. Nat Methods 18:768–770. doi:10.1038/s41592-021-01184-6 PubMed DOI PMC
Bittremieux W, Chen C, Dorrestein PC, Schymanski EL, Schulze T, Neumann S, Meier R, Rogers S, Wang M. 2020. Universal MS/MS visualization and retrieval with the metabolomics spectrum resolver web service. bioRxiv. doi:10.1101/2020.05.09.086066 DOI