Abstract
The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kjærbølling I, Mortensen UH, Vesth T, Andersen MR (2019) Strategies to establish the link between biosynthetic gene clusters and secondary metabolites. Fungal Genet Biol 130:107–121. https://doi.org/10.1016/j.fgb.2019.06.001
Ramírez-Rendon D, Passari AK, Ruiz-Villafán B et al (2022) Impact of novel microbial secondary metabolites on the pharma industry. Appl Microbiol Biotechnol 106:1855–1878. https://doi.org/10.1007/s00253-022-11821-5
Gruber-Dorninger C, Novak B, Nagl V, Berthiller F (2017) Emerging mycotoxins: beyond traditionally determined food contaminants. J Agric Food Chem 65:7052–7070. https://doi.org/10.1021/acs.jafc.6b03413
Caesar LK, Kellogg JJ, Kvalheim OM, Cech NB (2019) Opportunities and limitations for untargeted mass spectrometry metabolomics to identify biologically active constituents in complex natural product mixtures. J Nat Prod 82:469–484. https://doi.org/10.1021/acs.jnatprod.9b00176
Wolfender J-L, Nuzillard J-M, van der Hooft JJJ et al (2019) Accelerating metabolite identification in natural product research: toward an ideal combination of liquid chromatography–high-resolution tandem mass spectrometry and NMR profiling, in silico databases, and chemometrics. Anal Chem 91:704–742. https://doi.org/10.1021/acs.analchem.8b05112
Sun Y, Liu W-C, Shi X et al (2021) Inducing secondary metabolite production of Aspergillus sydowii through microbial co-culture with Bacillus subtilis. Microb Cell Factories 20:42. https://doi.org/10.1186/s12934-021-01527-0
Wakefield J, Hassan HM, Jaspars M et al (2017) Dual induction of new microbial secondary metabolites by fungal bacterial co-cultivation. Front Microbiol 8:1284. https://doi.org/10.3389/fmicb.2017.01284
Thornburg CC, Britt JR, Evans JR et al (2018) NCI program for natural product discovery: a publicly-accessible library of natural product fractions for high-throughput screening. ACS Chem Biol 13:2484–2497. https://doi.org/10.1021/acschembio.8b00389
Reher R, Kim HW, Zhang C et al (2020) A convolutional neural network-based approach for the rapid annotation of molecularly diverse natural products. J Am Chem Soc 142:4114–4120. https://doi.org/10.1021/jacs.9b13786
Misra BB, van der Hooft JJJ (2016) Updates in metabolomics tools and resources: 2014–2015. Electrophoresis 37:86–110. https://doi.org/10.1002/elps.201500417
Allard P-M, Genta-Jouve G, Wolfender J-L (2017) Deep metabolome annotation in natural products research: towards a virtuous cycle in metabolite identification. Curr Opin Chem Biol 36:40–49. https://doi.org/10.1016/j.cbpa.2016.12.022
Caesar LK, Cech NB (2019) Synergy and antagonism in natural product extracts: when 1 + 1 does not equal 2. Nat Prod Rep 36:869–888. https://doi.org/10.1039/C9NP00011A
Palaniappan K, Chen I-MA, Chu K et al (2020) IMG-ABC v.5.0: an update to the IMG/Atlas of Biosynthetic Gene Clusters Knowledgebase. Nucleic Acids Res 48:D422–D430. https://doi.org/10.1093/nar/gkz932
Hautbergue T, Jamin EL, Debrauwer L et al (2018) From genomics to metabolomics, moving toward an integrated strategy for the discovery of fungal secondary metabolites. Nat Prod Rep 35:147–173. https://doi.org/10.1039/C7NP00032D
Gross H (2007) Strategies to unravel the function of orphan biosynthesis pathways: recent examples and future prospects. Appl Microbiol Biotechnol 75:267–277. https://doi.org/10.1007/s00253-007-0900-5
Bode HB, Bethe B, Höfs R, Zeeck A (2002) Big effects from small changes: possible ways to explore nature’s chemical diversity. ChemBioChem 3:619–627. https://doi.org/10.1002/1439-7633(20020703)3:7<619::AID-CBIC619>3.0.CO;2-9
Zhang X, Hindra EMA (2019) Unlocking the trove of metabolic treasures: activating silent biosynthetic gene clusters in bacteria and fungi. Curr Opin Microbiol 51:9–15. https://doi.org/10.1016/j.mib.2019.03.003
Luo Y, Cobb RE, Zhao H (2014) Recent advances in natural product discovery. Curr Opin Biotechnol 30:230–237. https://doi.org/10.1016/j.copbio.2014.09.002
Pfannenstiel BT, Keller NP (2019) On top of biosynthetic gene clusters: how epigenetic machinery influences secondary metabolism in fungi. Biotechnol Adv 37:107345. https://doi.org/10.1016/j.biotechadv.2019.02.001
Baral B, Akhgari A, Metsä-Ketelä M (2018) Activation of microbial secondary metabolic pathways: avenues and challenges. Synth Syst Biotechnol 3:163–178. https://doi.org/10.1016/j.synbio.2018.09.001
Mattern DJ, Valiante V, Unkles SE, Brakhage AA (2015) Synthetic biology of fungal natural products. Front Microbiol 6:775. https://doi.org/10.3389/fmicb.2015.00775
Ziemert N, Alanjary M, Weber T (2016) The evolution of genome mining in microbes – a review. Nat Prod Rep 33:988–1005. https://doi.org/10.1039/C6NP00025H
Scherlach K, Hertweck C (2021) Mining and unearthing hidden biosynthetic potential. Nat Commun 12:3864. https://doi.org/10.1038/s41467-021-24133-5
Locey KJ, Lennon JT (2016) Scaling laws predict global microbial diversity. Proc Natl Acad Sci 113:5970–5975. https://doi.org/10.1073/pnas.1521291113
Hawksworth DL, Lücking R (2017) Fungal diversity revisited: 2.2 to 3.8 million species. In: Heitman J, Howlett BJ, Crous PW et al (eds) The fungal kingdom. ASM Press, Washington, DC, pp 79–95
Patti GJ, Yanes O, Siuzdak G (2012) Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13:263–269. https://doi.org/10.1038/nrm3314
Brunetti AE, Carnevale Neto F, Vera MC et al (2018) An integrative omics perspective for the analysis of chemical signals in ecological interactions. Chem Soc Rev 47:1574–1591. https://doi.org/10.1039/C7CS00368D
Covington BC, McLean JA, Bachmann BO (2017) Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites. Nat Prod Rep 34:6–24. https://doi.org/10.1039/C6NP00048G
Blaženović I, Kind T, Ji J, Fiehn O (2018) Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites 8:31. https://doi.org/10.3390/metabo8020031
Tawfike AF, Viegelmann C, Edrada-Ebel R (2013) Metabolomics and dereplication strategies in natural products. In: Roessner U, Dias DA (eds) Metabolomics tools for natural product discovery, Methods in molecular biology, vol 1055. Humana, Totowa, pp 227–244
Wolfender J-L, Rudaz S, Hae Choi Y, Kyong Kim H (2013) Plant metabolomics: from holistic data to relevant biomarkers. Curr Med Chem 20:1056–1090. https://doi.org/10.2174/092986713805288932
Genilloud O (2019) Natural products discovery and potential for new antibiotics. Curr Opin Microbiol 51:81–87. https://doi.org/10.1016/j.mib.2019.10.012
Krug D, Müller R (2014) Secondary metabolomics: the impact of mass spectrometry-based approaches on the discovery and characterization of microbial natural products. Nat Prod Rep 31:768–783. https://doi.org/10.1039/c3np70127a
Barkal LJ, Theberge AB, Guo C-J et al (2016) Microbial metabolomics in open microscale platforms. Nat Commun 7:10610. https://doi.org/10.1038/ncomms10610
Fenaille F, Barbier Saint-Hilaire P, Rousseau K, Junot C (2017) Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: where do we stand? J Chromatogr A 1526:1–12. https://doi.org/10.1016/j.chroma.2017.10.043
Atanasov AG, Zotchev SB, Dirsch VM, Supuran CT (2021) Natural products in drug discovery: advances and opportunities. Nat Rev Drug Discov 20:200–216. https://doi.org/10.1038/s41573-020-00114-z
Mapelli V, Olsson L, Nielsen J (2008) Metabolic footprinting in microbiology: methods and applications in functional genomics and biotechnology. Trends Biotechnol 26:490–497. https://doi.org/10.1016/j.tibtech.2008.05.008
Hubert J, Nuzillard J-M, Renault J-H (2017) Dereplication strategies in natural product research: how many tools and methodologies behind the same concept? Phytochem Rev 16:55–95. https://doi.org/10.1007/s11101-015-9448-7
Alves S, Rathahao-Paris E, Tabet JC (2013) Potential of Fourier transform mass spectrometry for high-throughput metabolomics analysis. In: Rolin D (ed) Metabolomics coming of age with its technological diversity, Advances in botanical research, vol vol 67. Academic, Amsterdam, pp 219–302
Ghosh R, Bu G, Nannenga BL, Sumner LW (2021) Recent developments toward integrated metabolomics technologies (UHPLC-MS-SPE-NMR and MicroED) for higher-throughput confident metabolite identifications. Front Mol Biosci 8:720955. https://doi.org/10.3389/fmolb.2021.720955
Henke MT, Kelleher NL (2016) Modern mass spectrometry for synthetic biology and structure-based discovery of natural products. Nat Prod Rep 33:942–950. https://doi.org/10.1039/C6NP00024J
Wu C, Choi YH, van Wezel GP (2016) Metabolic profiling as a tool for prioritizing antimicrobial compounds. J Ind Microbiol Biotechnol 43:299–312. https://doi.org/10.1007/s10295-015-1666-x
Caesar LK, Montaser R, Keller NP, Kelleher NL (2021) Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities. Nat Prod Rep 38:2041–2065. https://doi.org/10.1039/D1NP00036E
Bertrand S, Schumpp O, Bohni N et al (2013) De novo production of metabolites by fungal co-culture of Trichophyton rubrum and Bionectria ochroleuca. J Nat Prod 76:1157–1165. https://doi.org/10.1021/np400258f
De Vijlder T, Valkenborg D, Lemière F et al (2017) A tutorial in small molecule identification via electrospray ionization-mass spectrometry: the practical art of structural elucidation. Mass Spectrom Rev 37:607–629. https://doi.org/10.1002/mas.21551
Stricker T, Bonner R, Lisacek F, Hopfgartner G (2021) Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification. Anal Bioanal Chem 413:503–517. https://doi.org/10.1007/s00216-020-03019-3
Machushynets NV, Elsayed SS, Du C et al (2022) Discovery of actinomycin L, a new member of the actinomycin family of antibiotics. Sci Rep 12:2813. https://doi.org/10.1038/s41598-022-06736-0
Nielsen KF, Månsson M, Rank C et al (2011) Dereplication of microbial natural products by LC-DAD-TOFMS. J Nat Prod 74:2338–2348. https://doi.org/10.1021/np200254t
Schmid R, Petras D, Nothias L-F et al (2021) Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nat Commun 12:3832. https://doi.org/10.1038/s41467-021-23953-9
Cajka T, Fiehn O (2016) Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal Chem 88:524–545. https://doi.org/10.1021/acs.analchem.5b04491
Percival B, Gibson M, Leenders J et al (2020) Univariate and multivariate statistical approaches to the analysis and interpretation of NMR-based metabolomics datasets of increasing complexity. In: Wilson PB, Grootveld M (eds) Computational techniques for analytical chemistry and bioanalysis. The Royal Society of Chemistry, London, pp 1–40
Mohimani H, Gurevich A, Shlemov A et al (2018) Dereplication of microbial metabolites through database search of mass spectra. Nat Commun 9:4035. https://doi.org/10.1038/s41467-018-06082-8
Langlykke A (1980) Foreword to CRC handbook of antibiotic compounds. CRC, Boca Raton
Van Middlesworth F, Cannell RJP (1998) Dereplication and partial identification of natural products. In: Cannell RJP (ed) Natural products isolation, Methods in biotechnology, vol 4. Humana, Totowa, pp 279–327
Beutler JA, Alvarado AB, Schaufelberger DE et al (1990) Dereplication of phorbol bioactives: Lyngbya majuscula and Croton cuneatus. J Nat Prod 53:867–874. https://doi.org/10.1021/np50070a014
Phelan VV (2020) Feature-based molecular networking for metabolite annotation. In: Li S (ed) Computational methods and data analysis for metabolomics, Methods in molecular biology, vol 2104. Humana, New York, pp 227–243
Aron AT, Gentry EC, McPhail KL et al (2020) Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat Protoc 15:1954–1991. https://doi.org/10.1038/s41596-020-0317-5
Gaudêncio SP, Pereira F (2015) Dereplication: racing to speed up the natural products discovery process. Nat Prod Rep 32:779–810. https://doi.org/10.1039/C4NP00134F
Přichystal J, Schug KA, Lemr K et al (2016) Structural analysis of natural products. Anal Chem 88:10338–10346. https://doi.org/10.1021/acs.analchem.6b02386
Kluger B, Lehner S, Schuhmacher R (2015) Metabolomics and secondary metabolite profiling of filamentous fungi. In: Zeilinger S, Martín J-F, García-Estrada C (eds) Fungal biology: biosynthesis and molecular genetics of fungal secondary metabolites, vol Vol 2. Springer, New York, pp 81–101
Sumner LW, Amberg A, Barrett D et al (2007) Proposed minimum reporting standards for chemical analysis. Metabolomics 3:211–221. https://doi.org/10.1007/s11306-007-0082-2
Schymanski EL, Jeon J, Gulde R et al (2014) Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ Sci Technol 48:2097–2098. https://doi.org/10.1021/es5002105
Rochat B (2017) Proposed confidence scale and ID score in the identification of known-unknown compounds using high resolution MS data. J Am Soc Mass Spectrom 28:709–723. https://doi.org/10.1007/s13361-016-1556-0
Wolfender J-L, Litaudon M, Touboul D, Queiroz EF (2019) Innovative omics-based approaches for prioritisation and targeted isolation of natural products – new strategies for drug discovery. Nat Prod Rep 36:855–868. https://doi.org/10.1039/C9NP00004F
van Santen JA, Kautsar SA, Medema MH, Linington RG (2021) Microbial natural product databases: moving forward in the multi-omics era. Nat Prod Rep 38:264–278. https://doi.org/10.1039/D0NP00053A
Johnson SR, Lange BM (2015) Open-Access metabolomics databases for natural product research: present capabilities and future potential. Front Bioeng Biotechnol 3:22. https://doi.org/10.3389/fbioe.2015.00022
Sorokina M, Steinbeck C (2020) Review on natural products databases: where to find data in 2020. J Cheminform 12:20. https://doi.org/10.1186/s13321-020-00424-9
Laatsch H (2014) AntiBase 2014: the natural compound identifier. Wiley-VHC, Weinheim
van Santen JA, Jacob G, Singh AL et al (2019) The natural products Atlas: an open access knowledge base for microbial natural products discovery. ACS Cent Sci 5:1824–1833. https://doi.org/10.1021/acscentsci.9b00806
Klementz D, Döring K, Lucas X et al (2016) StreptomeDB 2.0—an extended resource of natural products produced by streptomycetes. Nucleic Acids Res 44:D509–D514. https://doi.org/10.1093/nar/gkv1319
Sorokina M, Merseburger P, Rajan K et al (2021) COCONUT online: Collection of Open Natural Products database. J Cheminform 13:2. https://doi.org/10.1186/s13321-020-00478-9
Petrova ON, Lamarre I, Fasani F et al (2020) Soluble guanylate cyclase inhibitors discovered among natural compounds. J Nat Prod 83:3642–3651. https://doi.org/10.1021/acs.jnatprod.0c00854
King ZA, Lu J, Dräger A et al (2016) BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44:D515–D522. https://doi.org/10.1093/nar/gkv1049
Gilson MK, Liu T, Baitaluk M et al (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44:D1045–D1053. https://doi.org/10.1093/nar/gkv1072
Pilón-Jiménez B, Saldívar-González F, Díaz-Eufracio B, Medina-Franco J (2019) BIOFACQUIM: a Mexican compound database of natural products. Biomolecules 9:31. https://doi.org/10.3390/biom9010031
Pence HE, Williams A (2010) ChemSpider: an online chemical information resource. J Chem Educ 87:1123–1124. https://doi.org/10.1021/ed100697w
Shen J, Xu X, Cheng F et al (2003) Virtual screening on natural products for discovering active compounds and target information. Curr Med Chem 10:2327–2342. https://doi.org/10.2174/0929867033456729
Kong D-X, Jiang Y-Y, Zhang H-Y (2010) Marine natural products as sources of novel scaffolds: achievement and concern. Drug Discov Today 15:884–886. https://doi.org/10.1016/j.drudis.2010.09.002
Wishart DS, Feunang YD, Guo AC et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082. https://doi.org/10.1093/nar/gkx1037
Wang M, Carver JJ, Phelan VV et al (2016) Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 34:828–837. https://doi.org/10.1038/nbt.3597
Wishart DS, Feunang YD, Marcu A et al (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46:D608–D617. https://doi.org/10.1093/nar/gkx1089
Chen Y, de Bruyn KC, Kirchmair J (2017) Data resources for the computer-guided discovery of bioactive natural products. J Chem Inf Model 57:2099–2111. https://doi.org/10.1021/acs.jcim.7b00341
Zhang R, Lin J, Zou Y et al (2019) Chemical space and biological target network of anti-inflammatory natural products. J Chem Inf Model 59:66–73. https://doi.org/10.1021/acs.jcim.8b00560
Singh P, Bast F (2015) Screening of multi-targeted natural compounds for receptor tyrosine kinases inhibitors and biological evaluation on cancer cell lines, in silico and in vitro. Med Oncol 32:233. https://doi.org/10.1007/s12032-015-0678-8
Kanehisa M, Furumichi M, Tanabe M et al (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361. https://doi.org/10.1093/nar/gkw1092
Lei J, Zhou J (2002) A marine natural product database. J Chem Inf Comput Sci 42:742–748. https://doi.org/10.1021/ci010111x
Blunt JW, Carroll AR, Copp BR et al (2018) Marine natural products. Nat Prod Rep 35:8–53. https://doi.org/10.1039/C7NP00052A
Horai H, Arita M, Kanaya S et al (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 45:703–714. https://doi.org/10.1002/jms.1777
Haug K, Cochrane K, Nainala VC et al (2019) MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res 48:D440–D444. https://doi.org/10.1093/nar/gkz1019
Wajiki M, Yamamoto T, Maruki-Uchida H et al (2022) Effect of sucrose on amino acid absorption of whey: a randomized crossover trial. Metabolites 12:282. https://doi.org/10.3390/metabo12040282
Caspi R, Billington R, Fulcher CA et al (2018) The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res 46:D633–D639. https://doi.org/10.1093/nar/gkx935
Guijas C, Montenegro-Burke JR, Domingo-Almenara X et al (2018) METLIN: a technology platform for identifying knowns and unknowns. Anal Chem 90:3156–3164. https://doi.org/10.1021/acs.analchem.7b04424
Li Y, Kind T, Folz J et al (2021) Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification. Nat Methods 18:1524–1531. https://doi.org/10.1038/s41592-021-01331-z
Verstraete A, Hyde E (2020) Une méthode de dépistage des NPS utilisant les plateformes collaboratives HighresNPS et Mzcloud. Toxicol Anal Clin 32:S47–S48. https://doi.org/10.1016/j.toxac.2020.09.017
Monga M, Sausville E (2002) Developmental Therapeutics Program at the NCI: molecular target and drug discovery process. Leukemia 16:520–526. https://doi.org/10.1038/sj.leu.2402464
Linstrom P (2017) NIST chemistry WebBook – SRD 69. National Institute of Standards and Technology. https://doi.org/10.18434/T4D303. Accessed 26 May 2023
Kuhn S, Schlörer NE (2015) Facilitating quality control for spectra assignments of small organic molecules: nmrshiftdb2 – a free in-house NMR database with integrated LIMS for academic service laboratories. Magn Reson Chem 53:582–589. https://doi.org/10.1002/mrc.4263
Zeng X, Zhang P, He W et al (2018) NPASS: natural product activity and species source database for natural product research, discovery and tool development. Nucleic Acids Res 46:D1217–D1222. https://doi.org/10.1093/nar/gkx1026
Capecchi A, Reymond J-L (2020) Assigning the origin of microbial natural products by chemical space map and machine learning. Biomolecules 10:1385. https://doi.org/10.3390/biom10101385
Tomiki T, Saito T, Ueki M et al (2006) RIKEN Natural Products Encyclopedia (RIKEN NPEdia), a Chemical Database of RIKEN Natural Products Depository (RIKEN NPDepo). J Comput Aided Chem 7:157–162. https://doi.org/10.2751/jcac.7.157
Williamson AE, Ylioja PM, Robertson MN et al (2016) Open source drug discovery: highly potent antimalarial compounds derived from the tres cantos arylpyrroles. ACS Cent Sci 2:687–701. https://doi.org/10.1021/acscentsci.6b00086
Huang W, Brewer LK, Jones JW et al (2018) PAMDB: a comprehensive Pseudomonas aeruginosa metabolome database. Nucleic Acids Res 46:D575–D580. https://doi.org/10.1093/nar/gkx1061
Kim S, Thiessen PA, Bolton EE et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213. https://doi.org/10.1093/nar/gkv951
Banerjee P, Erehman J, Gohlke B-O et al (2015) Super Natural II—a database of natural products. Nucleic Acids Res 43:D935–D939. https://doi.org/10.1093/nar/gku886
Ibrahim MAA, Abdeljawaad KAA, Abdelrahman AHM, Hegazy M-EF (2021) Natural-like products as potential SARS-CoV-2 M pro inhibitors: in-silico drug discovery. J Biomol Struct Dyn 39:5722–5734. https://doi.org/10.1080/07391102.2020.1790037
Gu J, Gui Y, Chen L et al (2013) Use of natural products as chemical library for drug discovery and network pharmacology. PLoS ONE 8:e62839. https://doi.org/10.1371/journal.pone.0062839
Ramirez-Gaona M, Marcu A, Pon A et al (2017) YMDB 2.0: a significantly expanded version of the yeast metabolome database. Nucleic Acids Res 45:D440–D445. https://doi.org/10.1093/nar/gkw1058
Sterling T, Irwin JJ (2015) ZINC 15 – ligand discovery for everyone. J Chem Inf Model 55:2324–2337. https://doi.org/10.1021/acs.jcim.5b00559
Ulrich EL, Akutsu H, Doreleijers JF et al (2007) BioMagResBank. Nucleic Acids Res 36:D402–D408. https://doi.org/10.1093/nar/gkm957
López-Pérez JL, Therón R, del Olmo E, Díaz D (2007) NAPROC-13: a database for the dereplication of natural product mixtures in bioassay-guided protocols. Bioinformatics 23:3256–3257. https://doi.org/10.1093/bioinformatics/btm516
Steinbeck C, Krause S, Kuhn S (2003) NMRShiftDBConstructing a free chemical information system with open-source components. J Chem Inf Comput Sci 43:1733–1739. https://doi.org/10.1021/ci0341363
Steinbeck C, Kuhn S (2004) NMRShiftDB – compound identification and structure elucidation support through a free community-built web database. Phytochemistry 65:2711–2717. https://doi.org/10.1016/j.phytochem.2004.08.027
Yamamoto O, Someno K, Wasada N et al (1988) An integrated spectral data base system including IR, MS, 1H-NMR, 13C-NMR, ESR and Raman spectra. Anal Sci 4:233–239. https://doi.org/10.2116/analsci.4.233
Zani CL, Carroll AR (2017) Database for rapid dereplication of known natural products using data from MS and fast NMR experiments. J Nat Prod 80:1758–1766. https://doi.org/10.1021/acs.jnatprod.6b01093
Wishart DS, Sayeeda Z, Budinski Z et al (2022) NP-MRD: the natural products magnetic resonance database. Nucleic Acids Res 50:D665–D677. https://doi.org/10.1093/nar/gkab1052
Asakura K (2015) A NMR spectral database of natural products “CH-NMR-NP”. J Synth Org Chem Japan 73:1247–1252. https://doi.org/10.5059/yukigoseikyokaishi.73.1247
de Medeiros LS, Abreu LM, Nielsen A et al (2015) Dereplication-guided isolation of depsides thielavins S–T and lecanorins D–F from the endophytic fungus Setophoma sp. Phytochemistry 111:154–162. https://doi.org/10.1016/j.phytochem.2014.12.020
de Medeiros L, da Silva J, Abreu L et al (2015) Dichlorinated and Brominated Rugulovasines, Ergot Alkaloids produced by Talaromyces wortmannii. Molecules 20:17627–17644. https://doi.org/10.3390/molecules200917627
Marner M, Patras MA, Kurz M et al (2020) Molecular networking-guided discovery and characterization of Stechlisins, a group of cyclic lipopeptides from a Pseudomonas sp. J Nat Prod 83:2607–2617. https://doi.org/10.1021/acs.jnatprod.0c00263
Harinantenaina Rakotondraibe L, Rasolomampianina R, Park H-Y et al (2015) Antiproliferative and antiplasmodial compounds from selected Streptomyces species. Bioorg Med Chem Lett 25:5646–5649. https://doi.org/10.1016/j.bmcl.2015.07.103
Tawfike AF, Romli M, Clements C et al (2019) Isolation of anticancer and anti-trypanosome secondary metabolites from the endophytic fungus Aspergillus flocculus via bioactivity guided isolation and MS based metabolomics. J Chromatogr B 1106–1107:71–83. https://doi.org/10.1016/j.jchromb.2018.12.032
van Santen JA, Poynton EF, Iskakova D et al (2022) The Natural Products Atlas 2.0: a database of microbially-derived natural products. Nucleic Acids Res 50:D1317–D1323. https://doi.org/10.1093/nar/gkab941
Jones MR, Pinto E, Torres MA et al (2021) CyanoMetDB, a comprehensive public database of secondary metabolites from cyanobacteria. Water Res 196:117017. https://doi.org/10.1016/j.watres.2021.117017
Davis LJ, Maldonado AC, Khin M et al (2022) Aulosirazoles B and C from the Cyanobacterium Nostoc sp. UIC 10771: analogues of an isothiazolonaphthoquinone scaffold that activate nuclear transcription Factor FOXO3a in ovarian cancer cells. J Nat Prod 85:540–546. https://doi.org/10.1021/acs.jnatprod.1c01030
Iwasaki A, Kurisawa N, Wang T et al (2021) Lingaoamide, a cyclic heptapeptide from a Chinese freshwater cyanobacterium Oscillatoria sp. Tetrahedron Lett 75:153214. https://doi.org/10.1016/j.tetlet.2021.153214
Montenegro-Burke JR, Guijas C, Siuzdak G (2020) METLIN: a tandem mass spectral library of standards. In: Li S (ed) Computational methods and data analysis for metabolomics, Methods in molecular biology, vol 2104. Humana, New York, pp 149–163
Fox Ramos AE, Evanno L, Poupon E et al (2019) Natural products targeting strategies involving molecular networking: different manners, one goal. Nat Prod Rep 36:960–980. https://doi.org/10.1039/C9NP00006B
Qin G-F, Zhang X, Zhu F et al (2022) MS/MS-based molecular networking: an efficient approach for natural products dereplication. Molecules 28:157. https://doi.org/10.3390/molecules28010157
Leao TF, Clark CM, Bauermeister A et al (2021) Quick-start infrastructure for untargeted metabolomics analysis in GNPS. Nat Metab 3:880–882. https://doi.org/10.1038/s42255-021-00429-0
Yang JY, Sanchez LM, Rath CM et al (2013) Molecular networking as a dereplication strategy. J Nat Prod 76:1686–1699. https://doi.org/10.1021/np400413s
Watrous J, Roach P, Alexandrov T et al (2012) Mass spectral molecular networking of living microbial colonies. Proc Natl Acad Sci 109:E1743–E1752. https://doi.org/10.1073/pnas.1203689109
Perez De Souza L, Alseekh S, Brotman Y, Fernie AR (2020) Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation. Expert Rev Proteomics 17:243–255. https://doi.org/10.1080/14789450.2020.1766975
Beniddir MA, Bin KK, Genta-Jouve G et al (2021) Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches. Nat Prod Rep 38:1967–1993. https://doi.org/10.1039/D1NP00023C
Olivon F, Elie N, Grelier G et al (2018) MetGem software for the generation of molecular networks based on the t-SNE algorithm. Anal Chem 90:13900–13908. https://doi.org/10.1021/acs.analchem.8b03099
Hoang TPT, Roullier C, Evanno L et al (2023) Nature-inspired chemistry of complex alkaloids: combining targeted molecular networking approach and semisynthetic strategy to access rare communesins in a marine-derived Penicillium expansum. Chem Eur J:e202300103. https://doi.org/10.1002/chem.202300103
Bauermeister A, Mannochio-Russo H, Costa-Lotufo LV et al (2022) Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol 20:143–160. https://doi.org/10.1038/s41579-021-00621-9
Domingo-Almenara X, Siuzdak G (2020) Metabolomics data processing using XCMS. In: Li S (ed) Computational methods and data analysis for metabolomics, Methods in molecular biology, vol 2104. Humana, New York, pp 11–24
Du X, Smirnov A, Pluskal T et al (2020) Metabolomics data preprocessing using ADAP and MZmine 2. In: Li S (ed) Computational methods and data analysis for metabolomics, Methods in molecular biology, vol 2104. Humana, New York, pp 25–48
Pathmasiri W, Kay K, McRitchie S, Sumner S (2020) Analysis of NMR metabolomics data. In: Li S (ed) Computational methods and data analysis for metabolomics, Methods in molecular biology, vol 2104. Humana, New York, pp 61–97
Kumar K, Schweiggert R, Patz C-D (2020) Introducing a novel procedure for peak alignment in one-dimensional 1 H-NMR spectroscopy: a prerequisite for chemometric analyses of wine samples. Anal Methods 12:3626–3636. https://doi.org/10.1039/D0AY01011A
Perez de Souza L, Naake T, Tohge T, Fernie AR (2017) From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics. Gigascience 6:gix037. https://doi.org/10.1093/gigascience/gix037
Jacob D, Deborde C, Lefebvre M et al (2017) NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics. Metabolomics 13:36. https://doi.org/10.1007/s11306-017-1178-y
Röst HL, Sachsenberg T, Aiche S et al (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13:741–748. https://doi.org/10.1038/nmeth.3959
Huan T, Forsberg EM, Rinehart D et al (2017) Systems biology guided by XCMS Online metabolomics. Nat Methods 14:461–462. https://doi.org/10.1038/nmeth.4260
Katajamaa M, Miettinen J, Orešič M (2006) MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22:634–636. https://doi.org/10.1093/bioinformatics/btk039
Ye D, Li X, Shen J, Xia X (2022) Microbial metabolomics: from novel technologies to diversified applications. TrAC Trends Anal Chem 148:116540. https://doi.org/10.1016/j.trac.2022.116540
Johnson CH, Ivanisevic J, Benton HP, Siuzdak G (2015) Bioinformatics: the next frontier of metabolomics. Anal Chem 87:147–156. https://doi.org/10.1021/ac5040693
Naake T, Gaquerel E, Fernie AR (2020) Annotation of specialized metabolites from high-throughput and high-resolution mass spectrometry metabolomics. In: Li S (ed) Computational methods and data analysis for metabolomics, Methods in molecular biology, vol 2104. Humana, New York, pp 209–225
Rasche F, Svatoš A, Maddula RK et al (2011) Computing fragmentation trees from tandem mass spectrometry data. Anal Chem 83:1243–1251. https://doi.org/10.1021/ac101825k
da Silva RR, Wang M, Nothias L-F et al (2018) Propagating annotations of molecular networks using in silico fragmentation. PLoS Comput Biol 14:e1006089. https://doi.org/10.1371/journal.pcbi.1006089
Scheubert K, Hufsky F, Böcker S (2013) Computational mass spectrometry for small molecules. J Cheminform 5:12. https://doi.org/10.1186/1758-2946-5-12
Tsugawa H, Kind T, Nakabayashi R et al (2016) Hydrogen rearrangement rules: computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Anal Chem 88:7946–7958. https://doi.org/10.1021/acs.analchem.6b00770
Triastuti A, Haddad M, Barakat F et al (2021) Dynamics of chemical diversity during co-cultures: an integrative time-scale metabolomics study of fungal Endophytes Cophinforma mamane and Fusarium solani. Chem Biodivers 18:e2000672. https://doi.org/10.1002/cbdv.202000672
Verdegem D, Lambrechts D, Carmeliet P, Ghesquière B (2016) Improved metabolite identification with MIDAS and MAGMa through MS/MS spectral dataset-driven parameter optimization. Metabolomics 12:98. https://doi.org/10.1007/s11306-016-1036-3
Wolf S, Schmidt S, Müller-Hannemann M, Neumann S (2010) In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinformatics 11:148. https://doi.org/10.1186/1471-2105-11-148
Longnecker K, Kujawinski EB (2017) Mining mass spectrometry data: using new computational tools to find novel organic compounds in complex environmental mixtures. Org Geochem 110:92–99. https://doi.org/10.1016/j.orggeochem.2017.05.008
Ruttkies C, Schymanski EL, Wolf S et al (2016) MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J Cheminform 8:3. https://doi.org/10.1186/s13321-016-0115-9
Fahy E, Sud M, Cotter D, Subramaniam S (2007) LIPID MAPS online tools for lipid research. Nucleic Acids Res 35:W606–W612. https://doi.org/10.1093/nar/gkm324
Ludwig M, Fleischauer M, Dührkop K et al (2020) De novo molecular formula annotation and structure elucidation using SIRIUS 4. In: Li S (ed) Computational methods and data analysis for metabolomics, Methods in molecular biology, vol 2104. Humana, New York, pp 185–207
Peres E, Souza M, Sousa T et al (2023) Dereplication of sclerotiorin-like azaphilones produced by Penicillium meliponae using LC-MS/MS analysis and molecular networking. J Braz Chem Soc 00:1–15. https://doi.org/10.21577/0103-5053.20230027
Dührkop K, Fleischauer M, Ludwig M et al (2019) SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat Methods 16:299–302. https://doi.org/10.1038/s41592-019-0344-8
Cohen LJ, Esterhazy D, Kim S-H et al (2017) Commensal bacteria make GPCR ligands that mimic human signalling molecules. Nature 549:48–53. https://doi.org/10.1038/nature23874
Hoffmann MA, Nothias L-F, Ludwig M et al (2022) High-confidence structural annotation of metabolites absent from spectral libraries. Nat Biotechnol 40:411–421. https://doi.org/10.1038/s41587-021-01045-9
Dührkop K, Nothias L-F, Fleischauer M et al (2021) Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol 39:462–471. https://doi.org/10.1038/s41587-020-0740-8
Klamrak A, Nabnueangsap J, Puthongking P, Nualkaew N (2021) Synthesis of ferulenol by engineered Escherichia coli: structural elucidation by using the in silico tools. Molecules 26:6264. https://doi.org/10.3390/molecules26206264
Atencio LA, Boya P CA, Martin HC et al (2020) Genome mining, microbial interactions, and molecular networking reveals new dibromoalterochromides from strains of Pseudoalteromonas of Coiba National Park-Panama. Mar Drugs 18:456. https://doi.org/10.3390/md18090456
Sequeira P, Rothkegel M, Domingos P et al (2022) Untargeted metabolomics sheds light on the secondary metabolism of fungi triggered by choline-based ionic liquids. Front Microbiol 13:946286. https://doi.org/10.3389/fmicb.2022.946286
van der Hooft JJJ, Wandy J, Barrett MP et al (2016) Topic modeling for untargeted substructure exploration in metabolomics. Proc Natl Acad Sci 113:13738–13743. https://doi.org/10.1073/pnas.1608041113
Maimone NM, de Oliveira LFP, Santos SN, de Lira SP (2021) Elicitation of Streptomyces lunalinharesii secondary metabolism through co-cultivation with Rhizoctonia solani. Microbiol Res 251:126836. https://doi.org/10.1016/j.micres.2021.126836
Pham HT, Lee KH, Jeong E et al (2021) Species prioritization based on spectral dissimilarity: a case study of polyporoid fungal species. J Nat Prod 84:298–309. https://doi.org/10.1021/acs.jnatprod.0c00977
Rogers S, Ong CW, Wandy J et al (2019) Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra. Faraday Discuss 218:284–302. https://doi.org/10.1039/C8FD00235E
Liu Y, Ding L, Shi Y et al (2022) Molecular networking-driven discovery of antibacterial perinadines, new tetracyclic alkaloids from the marine sponge-derived Fungus Aspergillus sp. ACS Omega 7:9909–9916. https://doi.org/10.1021/acsomega.2c00402
Andersen AJC, Hansen PJ, Jørgensen K, Nielsen KF (2016) Dynamic cluster analysis: an unbiased method for identifying A + 2 element containing compounds in liquid chromatographic high-resolution time-of-flight mass spectrometric data. Anal Chem 88:12461–12469. https://doi.org/10.1021/acs.analchem.6b03902
Roullier C, Guitton Y, Valery M et al (2016) Automated detection of natural halogenated compounds from LC-MS profiles–application to the isolation of bioactive chlorinated compounds from marine-derived fungi. Anal Chem 88:9143–9150. https://doi.org/10.1021/acs.analchem.6b02128
Flores-Bocanegra L, Al Subeh ZY, Egan JM et al (2022) Dereplication of fungal metabolites by NMR-based compound networking using MADByTE. J Nat Prod 85:614–624. https://doi.org/10.1021/acs.jnatprod.1c00841
Zhang C, Idelbayev Y, Roberts N et al (2017) Small Molecule Accurate Recognition Technology (SMART) to enhance natural products research. Sci Rep 7:14243. https://doi.org/10.1038/s41598-017-13923-x
Hu J-Q, Wang J-J, Li Y-L et al (2021) Combining NMR-based metabolic profiling and genome mining for the accelerated discovery of Archangiumide, an Allenic Macrolide from the Myxobacterium Archangium violaceum SDU8. Org Lett 23:2114–2119. https://doi.org/10.1021/acs.orglett.1c00265
Emwas A-H et al (2019) NMR spectroscopy for metabolomics research. Metabolites 9(7):123. https://doi.org/10.3390/metabo9070123
Hao J et al (2012) BATMAN – an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics 15:2088–20901. https://doi.org/10.1093/bioinformatics/bts308
Ravanbakhsh S et al (2015) Accurate, fully-automated NMR spectral profiling for metabolomics. PLoS ONE 5:e0124219. https://doi.org/10.1371/journal.pone.0124219
Röhnisch HE et al (2018) AQuA: an automated quantification algorithm for high-throughput NMR-based metabolomics and its application in human plasma. Anal Chem 3:2095–2102. https://doi.org/10.1021/acs.analchem.7b04324
Tardivel PJC et al (2017) ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics 10:109. https://doi.org/10.1007/s11306-017-1244-5
Bingol K et al (2016) Comprehensive metabolite identification strategy using multiple two-dimensional NMR spectra of a complex mixture implemented in the COLMARm web server. Anal Chem 24:12411–12418. https://doi.org/10.1021/acs.analchem.6b03724
Egan JM et al (2021) Development of an NMR-based platform for the direct structural annotation of complex natural products mixtures. J Nat Prod 4:1044–1055. 10.1021/acs.jnatprod.0c01076
Xia J et al (2008) MetaboMiner – semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics 1:507008. https://doi.org/10.1186/1471-2105-9-507
Bruguière A et al (2020) MixONat, a software for the dereplication of mixtures based on 13 C NMR spectroscopy. Anal Chem 13:8793–8801. https://doi.org/10.1021/acs.analchem.0c00193
Zhang C et al (2017) Small Molecule Accurate Recognition Technology (SMART) to enhance natural products research. Sci Rep 1:14243. https://doi.org/10.1038/s41598-017-13923-x
Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R (2015) Metabolomics for laboratory diagnostics. J Pharm Biomed Anal 113:108–120. https://doi.org/10.1016/j.jpba.2014.12.017
Chong J, Xia J (2020) Using MetaboAnalyst 4.0 for metabolomics data analysis, interpretation, and integration with other omics data. In: Li S (ed) Computational methods and data analysis for metabolomics, Methods in molecular biology, vol vol 2104. Humana, New York, pp 337–360
Ghosh T, Zhang W, Ghosh D, Kechris K (2020) Predictive modeling for metabolomics data. In: Li S (ed) Computational methods and data analysis for metabolomics, Methods in molecular biology, vol 2104. Humana, New York, pp 313–336
Sinha R, Sharma B, Dangi AK, Shukla P (2019) Recent metabolomics and gene editing approaches for synthesis of microbial secondary metabolites for drug discovery and development. World J Microbiol Biotechnol 35:166. https://doi.org/10.1007/s11274-019-2746-2
Debik J, Sangermani M, Wang F et al (2022) Multivariate analysis of NMR-based metabolomic data. NMR Biomed 35:e4638. https://doi.org/10.1002/nbm.4638
Abdelmohsen U, Cheng C, Viegelmann C et al (2014) Dereplication strategies for targeted isolation of new antitrypanosomal actinosporins A and B from a marine sponge associated-Actinokineospora sp. EG49. Mar Drugs 12:1220–1244. https://doi.org/10.3390/md12031220
Huang S-M, Toh W, Benke PI et al (2014) MetaboNexus: an interactive platform for integrated metabolomics analysis. Metabolomics 10:1084–1093. https://doi.org/10.1007/s11306-014-0648-8
Pang Z, Chong J, Zhou G et al (2021) MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res 49:W388–W396. https://doi.org/10.1093/nar/gkab382
Takaki M, Williams DE, Freire VF et al (2022) Metabolomics reveals a 26-membered macrolactone produced by Endophytic Colletotrichum spp. from Alcatrazes Island, Brazil. Org Lett 24:9381–9385. https://doi.org/10.1021/acs.orglett.2c03531
Deutsch JM, Mandelare-Ruiz P, Yang Y et al (2022) Metabolomics approaches to dereplicate natural products from coral-derived bioactive bacteria. J Nat Prod 85:462–478. https://doi.org/10.1021/acs.jnatprod.1c01110
Kešnerová L, Mars RAT, Ellegaard KM et al (2017) Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol 15:e2003467. https://doi.org/10.1371/journal.pbio.2003467
Hou Y, Braun DR, Michel CR et al (2012) Microbial strain prioritization using metabolomics tools for the discovery of natural products. Anal Chem 84:4277–4283. https://doi.org/10.1021/ac202623g
Wu C, Zacchetti B, Ram AFJ et al (2015) Expanding the chemical space for natural products by Aspergillus-Streptomyces co-cultivation and biotransformation. Sci Rep 5:10868. https://doi.org/10.1038/srep10868
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
de Medeiros, L.S. et al. (2023). Discovering New Natural Products Using Metabolomics-Based Approaches. In: Pacheco Fill, T. (eds) Microbial Natural Products Chemistry. Advances in Experimental Medicine and Biology(), vol 1439. Springer, Cham. https://doi.org/10.1007/978-3-031-41741-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-41741-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-41740-5
Online ISBN: 978-3-031-41741-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)