Skip to main content
SpringerLink
Log in

Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Mass spectrometry-based plant metabolomics allow large-scale analysis of a wide range of compounds and the discovery of potential new active metabolites with minimal sample preparation. Despite recent tools for molecular networking, many metabolites remain unknown. Our objective is to show the complementarity of collision cross section (CCS) measurements and calculations for metabolite annotation in a real case study. Thus, a systematic and high-throughput investigation of root, bark, branch, and leaf of the Gabonese plant Zhanthoxylum heitzii was performed through ultra-high performance liquid chromatography high-resolution tandem mass spectrometry (UHPLC-QTOF/MS). A feature-based molecular network (FBMN) was employed to study the distribution of metabolites in the organs of the plants and discover potential new components. In total, 143 metabolites belonging to the family of alkaloids, lignans, polyphenols, fatty acids, and amino acids were detected and a semi-quantitative analysis in the different organs was performed. A large proportion of medical plant phytochemicals is often characterized by isomerism and, in the absence of reference compounds, an additional dimension of gas phase separation can result in improvements to both quantitation and compound annotation. The inclusion of ion mobility in the ultra-high performance liquid chromatography mass spectrometry workflow (UHPLC-IMS-MS) has been used to collect experimental CCS values in nitrogen and helium (CCSN2 and CCSHe) of Zhanthoxylum heitzii features. Due to a lack of reference data, the investigation of predicted collision cross section has enabled comparison with the experimental values, helping in dereplication and isomer identification. Moreover, in combination with mass spectra interpretation, the comparison of experimental and theoretical CCS values allowed annotation of unknown features. The study represents a practical example of the potential of modern mass spectrometry strategies in the identification of medicinal plant phytochemical components.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

All the data are described within the manuscript. The raw data and metadata analyzed during the current study are available from the corresponding author on request. The molecular networking jobs can be publicly accessed at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=813b6121f4a840dbaa2c5d9a969f7056 for positive mode and https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=022432bae4e94699aa73b973c8214451 for negative mode.

References

  1. Kokoska L, Kloucek P, Leuner O, Novy P. Plant-derived products as antibacterial and antifungal agents in human health care. Curr Med Chem. 2019. https://doi.org/10.2174/0929867325666180831144344.

    Article  PubMed  Google Scholar 

  2. Barbieri R, Coppo E, Marchese A, Daglia M, Sobarzo-Sánchez E, Nabavi SF, Nabavi SM. Phytochemicals for human disease: an update on plant-derived compounds antibacterial activity. Microbiol Res. 2017. https://doi.org/10.1016/j.micres.2016.12.003.

    Article  PubMed  Google Scholar 

  3. Fabiani R. Antitumoral properties of natural products. Volume 2, molecules, MDPI edition. 2020. https://doi.org/10.3390/molecules25030650.

  4. Majolo F, Delwing LK, Marmitt DJ, Bustamante-Filho IC, Goettert MI. Medicinal plants and bioactive natural compounds for cancer treatment: important advances for drug discovery. Phytochem Lett. 2019. https://doi.org/10.1016/j.phytol.2019.04.003.

    Article  Google Scholar 

  5. Wolfender JL, Nuzillard JM, Van Der Hooft JJ, Renault JH, Bertrand S. 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. 2018. https://doi.org/10.1021/acs.analchem.8b05112.

    Article  PubMed  Google Scholar 

  6. Luo MD, Zhou ZW, Zhu ZJ. The application of ion mobility-mass spectrometry in untargeted metabolomics: from separation to identification. J Anal Test. 2020. https://doi.org/10.1007/s41664-020-00133-0.

    Article  Google Scholar 

  7. Schauer N, Fernie AR. Plant metabolomics: towards biological function and mechanism. Trends Plant Sci. 2006. https://doi.org/10.1016/j.tplants.2006.08.007.

    Article  PubMed  Google Scholar 

  8. Hall RD. Plant metabolomics: from holistic hope, to hype, to hot topic. New Phytol. 2006. https://doi.org/10.1111/j.1469-8137.2005.01632.x.

    Article  PubMed  Google Scholar 

  9. Šimura J, Antoniadi I, Široká J, Tarkowská DE, Strnad M, Ljung K, Novák O. Plant hormonomics: multiple phytohormone profiling by targeted metabolomics. Plant Phys. 2018. https://doi.org/10.1104/pp.18.00293.

    Article  Google Scholar 

  10. Gachet MS, Schubert A, Calarco S, Boccard J, Gertsch J. Targeted metabolomics shows plasticity in the evolution of signaling lipids and uncovers old and new endocannabinoids in the plant kingdom. Sci Reports. 2017. https://doi.org/10.1038/srep41177.

    Article  Google Scholar 

  11. Vanderplanck M, Glauser G. Integration of non-targeted metabolomics and automated determination of elemental compositions for comprehensive alkaloid profiling in plants. Phytochemistry. 2018. https://doi.org/10.1016/j.phytochem.2018.06.011.

    Article  PubMed  Google Scholar 

  12. Kårlund A, Hanhineva K, Lehtonen M, McDougall GJ, Stewart D, Karjalainen RO. Non-targeted metabolite profiling highlights the potential of strawberry leaves as a resource for specific bioactive compounds. J Sci Food Agric. 2017. https://doi.org/10.1002/jsfa.8027.

    Article  PubMed  Google Scholar 

  13. Perez de Souza L, Alseekh S, Naake T, Fernie A. Mass spectrometry-based untargeted plant metabolomics. Curr Protoc Plant Biol. 2019. https://doi.org/10.1002/cppb.20100.

    Article  PubMed  Google Scholar 

  14. Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie AR. Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat Protoc. 2006. https://doi.org/10.1038/nprot.2006.59.

    Article  PubMed  Google Scholar 

  15. Rivera-Pérez A, Romero-González R, Garrido Frenich A. Feasibility of applying untargeted metabolomics with GC-Orbitrap-HRMS and chemometrics for authentication of black pepper (Piper nigrum L.) and identification of geographical and processing markers. J Agric Food Chem. 2021. https://doi.org/10.1021/acs.jafc.1c01515.

    Article  PubMed  Google Scholar 

  16. Aydoğan C. Recent advances and applications in LC-HRMS for food and plant natural products: a critical review. Anal Bioanal Chem. 2020. https://doi.org/10.1007/s00216-019-02328-6.

    Article  PubMed  Google Scholar 

  17. Wolfender JL, Litaudon M, Touboul D, Ferreira QE. Innovative omics-bases approaches for prioritisation and targeted isolation of natural products—new strategies for drug discovery. Nat Prod Reports. 2019;36:855. https://doi.org/10.1039/c9np00004f.

    Article  CAS  Google Scholar 

  18. Vinaixa M, Schymanski EL, Neumann S, Navarro M, Salek RM, Yanes O. Mass spectral databases for LC/MS-and GC/MS-based metabolomics: state of the field and future prospects. TrAC Trends Anal Chem. 2016. https://doi.org/10.1016/j.trac.2015.09.005.

    Article  Google Scholar 

  19. Smith CA, O’Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G. METLIN: a metabolite mass spectral database. Ther Drug Monit. 2005. https://doi.org/10.1097/01.ftd.0000179845.53213.39.

    Article  PubMed  Google Scholar 

  20. Gerasimoska T, Ljoncheva M, Simjanoska M. MSL-ST: development of mass spectral library search tool to enhance compound identification. Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies. Volume 3: BIOINFORMATICS. 2021. https://doi.org/10.5220/0010424101950203

  21. Nothias LF, Petras D, Schmid R, Dührkop K, Rainer J, Sarvepalli A, Protsyuk I, Ernst M, Tsugawa H, Fleischauer M, Aicheler F. Feature-based molecular networking in the GNPS analysis environment. Nat Methods. 2020. https://doi.org/10.1038/s41592-020-0933-6.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Olivon F, Elie N, Grelier G, Roussi F, Litaudon M, Touboul D. MetGem software for the generation of molecular networks based on the t-SNE algorithm. Anal Chem. 2018. https://doi.org/10.1021/acs.analchem.8b03099.

    Article  PubMed  Google Scholar 

  23. Hoffmann MA, Nothias LF, Ludwig M, Fleischauer M, Gentry EC, Witting M, Dorrestein PC, Dührkop K, Böcker S. High-confidence structural annotation of metabolites absent from spectral libraries. Nat Biotechnol. 2021. https://doi.org/10.1038/s41587-021-01045-9.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Baker ES, Livesay EA, Orton DJ, Moore RJ, Danielson WF III, Prior DC, Ibrahim YM, LaMarche BL, Mayampurath AM, Schepmoes AA, Hopkins DF. An LC-IMS-MS platform providing increased dynamic range for high-throughput proteomic studies. J Proteome Res. 2010. https://doi.org/10.1021/pr900888b.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Chalet C, Hollebrands B, Janssen HG, Augustijns P, Duchateau G. Identification of phase-II metabolites of flavonoids by liquid chromatography–ion-mobility spectrometry–mass spectrometry. Anal Bioanal Chem. 2018. https://doi.org/10.1007/s00216-017-0737-4.

    Article  PubMed  Google Scholar 

  26. Kanu AB, Dwivedi P, Tam M, Matz L, Hill HH Jr. Ion mobility–mass spectrometry. J Mass Spectrom. 2008. https://doi.org/10.1002/jms.1383.

    Article  PubMed  Google Scholar 

  27. Mason EA, Schamp HW Jr. Mobility of gaseous ions in weak electric fields. Ann Phys. 1958. https://doi.org/10.1016/0003-4916(58)90049-6.

    Article  Google Scholar 

  28. Giles K, Pringle SD, Worthington KR, Little D, Wildgoose JL, Bateman RH. Applications of a travelling wave-based radio-frequency-only stacked ring ion guide. Rapid Commun Mass Spectrom. 2004. https://doi.org/10.1002/rcm.1641.

    Article  PubMed  Google Scholar 

  29. Stephan S, Jakob C, Hippler J, Schmitz OJ. A novel four-dimensional analytical approach for analysis of complex samples. Anal Bioanal Chem. 2016. https://doi.org/10.1007/s00216-016-9460-9.

    Article  PubMed  Google Scholar 

  30. Neumann EK, Migas LG, Allen JL, Caprioli RM, Van de Plas R, Spraggins JM. Spatial metabolomics of the human kidney using MALDI trapped ion mobility imaging mass spectrometry. Anal Chem. 2020. https://doi.org/10.1021/acs.analchem.0c02051.

    Article  PubMed  Google Scholar 

  31. Zhang X, Kew K, Reisdorph R, Sartain M, Powell R, Armstrong M, Quinn K, Cruickshank-Quinn C, Walmsley S, Bokatzian S, Darland E. Performance of a high-pressure liquid chromatography-ion mobility-mass spectrometry system for metabolic profiling. Anal Chem. 2017. https://doi.org/10.1021/acs.analchem.6b04628.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Dwivedi P, Wu P, Klopsch SJ, Puzon GJ, Xun L, Hill HH. Metabolic profiling by ion mobility mass spectrometry (IMMS). Metabolomics. 2008. https://doi.org/10.1007/s11306-007-0093-z.

    Article  Google Scholar 

  33. Schroeder M, Meyer SW, Heyman HM, Barsch A, Sumner LW. Generation of a collision cross section library for multi-dimensional plant metabolomics using UHPLC-trapped ion mobility-MS/MS. Metabolites. 2020. https://doi.org/10.3390/metabo10010013.

    Article  Google Scholar 

  34. Zhou Z, Luo M, Chen X, Yin Y, Xiong X, Wang R, Zhu ZJ. Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics. Nat Commun. 2020. https://doi.org/10.1038/s41467-020-18171-8.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Colby SM, Thomas DG, Nuñez JR, Baxter DJ, Glaesemann KR, Brown JM, Pirrung MA, Govind N, Teeguarden JG, Metz TO, Renslow RS. ISiCLE: a quantum chemistry pipeline for establishing in silico collision cross section libraries. Anal Chem. 2019. https://doi.org/10.1021/acs.analchem.8b04567.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Little JL, Cleven CD, Brown SD. Identification of “known unknowns” utilizing accurate mass data and chemical abstracts service databases. J Am Soc Mass Spectrom. 2011. https://doi.org/10.1007/s13361-010-0034-3.

    Article  PubMed  Google Scholar 

  37. Little JL, Williams AJ, Pshenichnov A, Tkachenko V. Identification of “known unknowns” utilizing accurate mass data and ChemSpider. J Am Soc Mass Spectrom. 2012. https://doi.org/10.1007/s13361-011-0265-y.

    Article  PubMed  Google Scholar 

  38. McCullagh M, Goshawk J, Eatough D, Mortishire-Smith RJ, Pereira CA, Yariwake JH, Vissers JP. Profiling of the known-unknown Passiflora variant complement by liquid chromatography-ion mobility-mass spectrometry. Talanta. 2021. https://doi.org/10.1016/j.talanta.2020.121311.

    Article  PubMed  Google Scholar 

  39. Celma A, Sancho JV, Schymanski EL, Fabregat-Safont D, Ibáñez M, Goshawk J, Barknowitz G, Hernández F, Bijlsma L. Improving target and suspect screening high-resolution mass spectrometry workflows in environmental analysis by ion mobility separation. Environ Sci Technol. 2020. https://doi.org/10.1021/acs.est.0c05713.

    Article  PubMed  Google Scholar 

  40. Ollivier S, Fanuel M, Rogniaux H, Ropartz D. Molecular networking of high-resolution tandem ion mobility spectra: a structurally relevant way of organizing data in glycomics? Anal Chem. 2021. https://doi.org/10.1021/acs.analchem.1c01244.

    Article  PubMed  Google Scholar 

  41. Goodman CD, Austarheim I, Mollard V, Mikolo B, Malterud KE, McFadden GI, Wangensteen H. Natural products from Zanthoxylum heitzii with potent activity against the malaria parasite. Malar J. 2016;15(1):1–8. https://doi.org/10.1186/s12936-016-1533-x.

    Article  CAS  Google Scholar 

  42. Ntchapda F, Maguirgue K, Adjia H, Etet PF, Dimo T. Hypolipidemic, antioxidant and anti—atherosclerogenic effects of aqueous extract of Zanthoxylum heitzii stem bark in diet—induced hypercholesterolemic rats. Asian Pac J Trop. 2015. https://doi.org/10.1016/S1995-7645(14)60344-8.

    Article  Google Scholar 

  43. Mokondjimobe E, Miantezila Basilua J, Barkha S, Dzeufiet PD, Chenal H, Otsudi’andjeka JB, Bipolo S, Besse M, Mamadou G, Limas-Nzouzi N, Kamtchouing P. Fagaricine, a new immunorestorative phytomedicine from Zanthoxylum heitzii: preclinical and multicenter cohort clinical studies based on HIV-infected patients in six countries. Phytopharmacology. 2012;2(1):26–45.

    CAS  Google Scholar 

  44. Pieme CA, Santosh GK, Tekwu EM, Askun T, Aydeniz H, Ngogang JY, Bhushan S, Saxena AK. Fruits and barks extracts of Zanthozyllum heitzii a spice from Cameroon induce mitochondrial dependent apoptosis and Go/G1 phase arrest in human leukemia HL-60 cells. Biol Res. 2014. https://doi.org/10.1186/0717-6287-47-54.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Ntchapda F, Kakesse M, Fokam MA, Pancha OM, Abakar D, Dimo T. Evaluation of the diuretic effects of crude stem bark extraction of Zanthoxylum heitzii (Rutaceae) in Wistar rats. J Integr Med. 2015. https://doi.org/10.1016/S2095-4964(15)60188-1.

    Article  PubMed  Google Scholar 

  46. Tian Y, Zhang C, Guo M. Comparative study on alkaloids and their anti-proliferative activities from three Zanthoxylum species. BMC Complement Altern Med. 2017. https://doi.org/10.1186/s12906-017-1966-y.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bongui JB, Blanckaert A, Elomri A, Seguin E. Constituents of Zanthoxylum heitzii (Rutaceae). Biochem Syst Ecol. 2005;33(8):845–7. https://doi.org/10.1016/j.bse.2004.12.019.

    Article  CAS  Google Scholar 

  48. Ngouela S, Tsamo E, Connolly JD. Lignans and other constituents of Zanthoxylum heitzii. Phytochemistry. 1994. https://doi.org/10.1016/S0031-9422(00)90373-X.

    Article  Google Scholar 

  49. Wangensteen H, Ho GT, Tadesse M, Miles CO, Moussavi N, Mikolo B, Malterud KE. A new benzophenanthridine alkaloid and other bioactive constituents from the stem bark of Zanthoxylum heitzii. Fitoterapia. 2016. https://doi.org/10.1016/j.fitote.2016.01.012.

    Article  PubMed  Google Scholar 

  50. Giles K, Williams JP, Campuzano I. Enhancements in travelling wave ion mobility resolution. Rapid Commun Mass Spectrom. 2011. https://doi.org/10.1002/rcm.5013.

    Article  PubMed  Google Scholar 

  51. Yu JS, Nothias LF, Wang M, Kim DH, Dorrestein PC, Kang KB, Yoo HH. Tandem mass spectrometry molecular networking as a powerful and efficient tool for drug metabolism studies. Anal Chem. 2022. https://doi.org/10.1021/acs.analchem.1c04925.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, Nguyen DD, Watrous J, Kapono CA, Luzzatto-Knaan T, Porto C. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol. 2016. https://doi.org/10.1038/nbt.3597.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Katajamaa M, Miettinen J, Orešič M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics. 2006. https://doi.org/10.1093/bioinformatics/btk039.

    Article  PubMed  Google Scholar 

  54. Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinforma. 2010. https://doi.org/10.1186/1471-2105-11-395.

    Article  Google Scholar 

  55. Myers OD, Sumner SJ, Li S, Barnes S, Du X. One step forward for reducing false positive and false negative compound identifications from mass spectrometry metabolomics data: new algorithms for constructing extracted ion chromatograms and detecting chromatographic peaks. Anal Chem. 2017. https://doi.org/10.1021/acs.analchem.7b00947.

    Article  PubMed  Google Scholar 

  56. Mohimani H, Gurevich A, Shlemov A, Mikheenko A, Korobeynikov A, Cao L, Shcherbin E, Nothias LF, Dorrestein PC, Pevzner PA. Dereplication of microbial metabolites through database search of mass spectra. Nature Comm. 2018. https://doi.org/10.1038/s41467-018-06082-8.

    Article  Google Scholar 

  57. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003. https://doi.org/10.1101/gr.1239303.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Bush MF, Campuzano ID, Robinson CV. Ion mobility mass spectrometry of peptide ions: effects of drift gas and calibration strategies. Anal Chem. 2012. https://doi.org/10.1021/ac3014498.

    Article  PubMed  Google Scholar 

  59. Gabelica V, Shvartsburg AA, Afonso C, Barran P, Benesch JLP, Bleiholder C, Bowers MT, Bilbao A, Bush MF, Campbell JL, Campuzano IDG, Causon T, Clowers BH, Creaser CS, De Pauw E, Far J, Fernandez-Lima F, Fjeldsted JC, Giles K, Groessl M, Hogan CJ Jr, Hann S, Kim HI, Kurulugama RT, May JC, McLean JA, Pagel K, Richardson K, Ridgeway ME, Rosu F, Sobott F, Thalassinos K, Valentine SJ, Wyttenbach T. Recommendations for reporting ion mobility mass spectrometry measurements. Mass Spectrom Rev. 2019. https://doi.org/10.1002/mas.21585.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Smith DP, Knapman TW, Campuzano I, Malham RW, Berryman JT, Radford SE, Ashcroft AE. Deciphering drift time measurements from travelling wave ion mobility spectrometry-mass spectrometry studies. Eur J Mass Spectrom. 2009. https://doi.org/10.1255/ejms.947.

    Article  Google Scholar 

  61. Vainio MJ, Johnson MS. Generating conformer ensembles using a multiobjective genetic algorithm. J Chem Inf Model. 2007. https://doi.org/10.1021/ci6005646.

    Article  PubMed  Google Scholar 

  62. Puranen JS, Vainio MJ, Johnson MS. Accurate conformation-dependent molecular electrostatic potentials for high-throughput in silico drug discovery. J Comp Chem. 2010. https://doi.org/10.1002/jcc.21460.

    Article  Google Scholar 

  63. Halgren TA. Merck molecular force field I Basis, form, scope, parameterization, and performance of MMFF94. J Comp Chem. 1996. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6%3c490::AID-JCC1%3e3.0.CO;2-P.

    Article  Google Scholar 

  64. Zeng X, Wang Z, Liu X, Chen M, Fahr A, Zhang K. Predicting the skin-permeating components of externally-applied medicinal herbs: application of a newly constructed linear free-energy relationship equation for human skin permeation. New J Chem. 2018. https://doi.org/10.1039/C8NJ00929E.

    Article  Google Scholar 

  65. Liang M, Zhang W, Hu J, Liu R, Zhang C. Simultaneous analysis of alkaloids from Zanthoxylum nitidum by high performance liquid chromatography–diode array detector–electrospray tandem mass spectrometry. J Pharm Biomed Anal. 2006. https://doi.org/10.1016/j.jpba.2006.03.031.

    Article  PubMed  Google Scholar 

  66. Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, Fan TW, Fiehn O, Goodacre R, Griffin JL, Hankemeier T. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007. https://doi.org/10.1007/s11306-007-0082-2.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Bessonova IA, Faizutdinova ZS, Rashkes YV, Yunusov SY. Mass spectrometry of dubinidine and dubinine. Chem Nat Compd. 1970. https://doi.org/10.1007/BF00564249.

    Article  Google Scholar 

  68. Mbaze LM, Lado JA, Wansi JD, Shiao TC, Chiozem DD, Mesaik MA, Choudhary MI, Lacaille-Dubois MA, Wandji J, Roy R, Sewald N. Oxidative burst inhibitory and cytotoxic amides and lignans from the stem bark of Fagara heitzii (Rutaceae). Phytochemistry. 2009. https://doi.org/10.1016/j.phytochem.2009.08.007.

    Article  PubMed  Google Scholar 

  69. Hanhineva K, Rogachev I, Aura AM, Aharoni A, Poutanen K, Mykkänen H. Identification of novel lignans in the whole grain rye bran by non-targeted LC–MS metabolite profiling. Metabolomics. 2012. https://doi.org/10.1007/s11306-011-0325-0.

    Article  Google Scholar 

  70. Ross DH, Seguin RP, Krinsky AM, Xu L. High-throughput measurement and machine learning-based prediction of collision cross sections for drugs and drug metabolites. bioRxiv. 2021. https://doi.org/10.1101/2021.05.13.443945.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Hines KM, Ross DH, Davidson KL, Bush MF, Xu L. Large-scale structural characterization of drug and drug-like compounds by high-throughput ion mobility-mass spectrometry. Anal Chem. 2017. https://doi.org/10.1021/acs.analchem.7b01709.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Qing Z, Xu Y, Yu L, Liu J, Huang X, Tang Z, Cheng P, Zeng J. Investigation of fragmentation behaviours of isoquinoline alkaloids by mass spectrometry combined with computational chemistry. Sci Rep. 2020. https://doi.org/10.1038/s41598-019-57406-7.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Liu YL, Gao LL, Song TT, Guo T, Chang J. Two new sesquiterpenoid glycosides from the stems of Zanthoxylum armatum DC. Nat Prod Res. 2020. https://doi.org/10.1080/14786419.2019.1607332.

    Article  PubMed  Google Scholar 

  74. Winkler A, Puhl M, Weber H, Kutchan TM, Gruber K, Macheroux P. Berberine bridge enzyme catalyzes the six electron oxidation of (S)-reticuline to dehydroscoulerine. Phytochemistry. 2009. https://doi.org/10.1016/j.phytochem.2009.06.005.

    Article  PubMed  Google Scholar 

  75. Sheen WS, Tsai IL, Teng CM, Ko FN, Chen IS. Indolopyridoquinazoline alkaloids with antiplatelet aggregation activity from Zanthoxylum integrifoliolum. Planta Med. 1996. https://doi.org/10.1055/s-2006-957846.

    Article  PubMed  Google Scholar 

  76. Li W, Sun YN, Yan XT, Yang SY, Kim EJ, Kang HK, Kim YH. Coumarins and lignans from Zanthoxylum schinifolium and their anticancer activities. J Agric Food Chem. 2013. https://doi.org/10.1021/jf403479c.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge Waters Corporation for the kind provision of the UNIFI software, Kevin Giles, PhD, for his advices, and the COBRA team for the support. The authors gratefully acknowledge Dr. Jean-Bernard Bongui for Z. heitzii providing.

Funding

This work has been partially supported by University of Rouen Normandy, INSA Rouen Normandy, the Centre National de la Recherche Scientifique (CNRS), European Regional Development Fund (ERDF), Labex SynOrg (ANR-11-LABX-0029), Carnot Institut I2C, the graduate school for research Xl-Chem (ANR-18-EURE-0020 XL CHEM), and by Region Normandie.

Author information

Authors and Affiliations

  1. Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA (UMR 6014), 76000, Rouen, France

    Valentina Calabrese, Isabelle Schmitz-Afonso, Candice Prevost, Carlos Afonso & Abdelhakim Elomri

Authors
  1. Valentina Calabrese
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isabelle Schmitz-Afonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Candice Prevost
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos Afonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdelhakim Elomri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Isabelle Schmitz-Afonso.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 5.01 MB)

Supplementary file2 (XLSX 31.7 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Calabrese, V., Schmitz-Afonso, I., Prevost, C. et al. Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts. Anal Bioanal Chem 414, 4103–4118 (2022). https://doi.org/10.1007/s00216-022-04059-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-022-04059-7

Keywords

Search

Navigation