Skip to main content
SpringerLink
Log in

Development of an LC–MS multivariate nontargeted methodology for differential analysis of the peptide profile of Asian hornet venom (Vespa velutina nigrithorax): application to the investigation of the impact of collection period variation

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

Abstract

Insect venom is a highly complex mixture of bioactive compounds, containing proteins, peptides, and small molecules. Environmental factors can alter the venom composition and lead to intraspecific variation in its bioactivity properties. The investigation of discriminating compounds caused by variation impacts can be a key to manage sampling and explore the bioactive compounds. The present study reports the development of a peptidomic methodology based on UHPLC–ESI-QTOF–HRMS analysis followed by a nontargeted multivariate analysis to reveal the profile variance of Vespa velutina venom collected in different conditions. The reliability of the approach was enhanced by optimizing certain XCMS data processing parameters and determining the sample peak threshold to eliminate the interfering features. This approach demonstrated a good repeatability and a criterion coefficient of variation (CV) > 30% was set for deleting nonrepeatable features from the matrix. The methodology was then applied to investigate the impact of collection period variation. PCA and PLS-DA models were used and validated by cross-validation and permutation tests. A slight discrimination was found between winter and summer hornet venom in two successive years with 10 common discriminating compounds.

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
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Abdel-Rahman MA, Omran MAA, Abdel-Nabi IM, Nassier OA, Schemerhorn BJ. Neurotoxic and cytotoxic effects of venom from different populations of the Egyptian Scorpio maurus palmatus. Toxicon. 2010;55(2):298–306.

  2. Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Čech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46(W1):W537–44.

    Article  CAS  Google Scholar 

  3. Bai Y, Zhao Q, He M, Ye X, Zhang X. Extensive characterization and differential analysis of endogenous peptides from Bombyx batryticatus using mass spectrometric approach. J Pharm Biomed Anal. 2019;163:78–87.

  4. Bao J, Ding R-B, Jia X, Liang Y, Liu F, Wang K, et al. Fast identification of anticancer constituents in Forsythiae Fructus based on metabolomics approaches. J Pharm Biomed Anal. 2018;154:312–20.

  5. Bernardi RC, Firmino ELB, Mendonça A, Sguarizi-Antonio D, Pereira MC, da Cunha Andrade LH, et al. Intraspecific variation and influence of diet on the venom chemical profile of the Ectatomma brunneum Smith (Formicidae) ant evaluated by photoacoustic spectroscopy. J Photochem Photobiol B. 2017;175:200–6.

  6. Bijlsma S, Bobeldijk I, Verheij ER, Ramaker R, Kochhar S, Macdonald IA, et al. Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal Chem. 2006;78(2):567–74.

    Article  CAS  Google Scholar 

  7. Boekel J, Chilton JM, Cooke IR, Horvatovich PL, Jagtap PD, Käll L, et al. Multi-omic data analysis using Galaxy. Nat Biotechnol. 2015;33:137–9.

    Article  CAS  Google Scholar 

  8. Chen W, Yang X, Yang X, Zhai L, Lu Z, Liu J, et al. Antimicrobial peptides from the venoms of Vespa bicolor Fabricius. Peptides. 2008;29(11):1887–92.

  9. Chen T-B, Zuo Y-H, Dong G-T, Liu L, Zhou H. An integrated strategy for rapid discovery and identification of quality markers in Guanxin Kangtai preparation using UHPLC-TOF/MS and multivariate statistical analysis. Phytomedicine. 2018;44:239–46.

    Article  CAS  Google Scholar 

  10. Cologna CT, dos Cardoso J S, Jourdan E, Degueldre M, Upert G, Gilles N, et al. Peptidomic comparison and characterization of the major components of the venom of the giant ant Dinoponera quadriceps collected in four different areas of Brazil. J Proteome. 2013;94:413–22.

    Article  CAS  Google Scholar 

  11. Cologna CT, Rodrigues RS, Santos J, de Pauw E, Arantes EC, Quinton L. Peptidomic investigation of Neoponera villosa venom by high-resolution mass spectrometry: seasonal and nesting habitat variations. J Venom Anim Toxins Incl Trop Dis. 2018;24:6.

  12. Danneels EL, Van Vaerenbergh M, Debyser G, Devreese B, de Graaf DC. Honeybee venom proteome profile of queens and winter bees as determined by a mass spectrometric approach. Toxins (Basel). 2015;7(11):4468–83.

    Article  CAS  Google Scholar 

  13. Dias NB, de Souza BM, Gomes PC, Palma MS. Peptide diversity in the venom of the social wasp Polybia paulista (Hymenoptera): a comparison of the intra- and inter-colony compositions. Peptides. 2014;51:122–30.

  14. Dunn WB, Broadhurst D, Brown M, Baker PN, Redman CWG, Kenny LC, et al. Metabolic profiling of serum using ultra performance liquid chromatography and the LTQ-Orbitrap mass spectrometry system. J Chromatogr B. 2008;871(2):288–98.

    Article  CAS  Google Scholar 

  15. Eliasson M, Rännar S, Madsen R, Donten MA, Marsden-Edwards E, Moritz T, et al. Strategy for optimizing LC-MS data processing in metabolomics: a design of experiments approach. Anal Chem. 2012;84(15):6869–76.

    Article  CAS  Google Scholar 

  16. Eliyahu D, Ross KG, Haight KL, Keller L, Liebig J. Venom alkaloid and cuticular hydrocarbon profiles are associated with social organization, queen fertility status, and queen genotype in the fire ant Solenopsis invicta. J Chem Ecol. 2011;37(11):1242–54.

  17. Forsberg EM, Huan T, Rinehart D, Benton HP, Warth B, Hilmers B, et al. Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS online. Nat Protoc. 2018;13(4):633–51.

    Article  CAS  Google Scholar 

  18. Gao J-F, Wang J, He Y, Qu Y-F, Lin L-H, Ma X-M, et al. Proteomic and biochemical analyses of short-tailed pit viper (Gloydius brevicaudus) venom: age-related variation and composition–activity correlation. J Proteome. 2014;105:307–22.

  19. Giacomoni F, Le Corguille G, Monsoor M, Landi M, Pericard P, Petera M, et al. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics. 2015;31(9):1493–5.

    Article  CAS  Google Scholar 

  20. Gilar M, Belenky A, Wang BH. High-throughput biopolymer desalting by solid-phase extraction prior to mass spectrometric analysis. J Chromatogr A. 2001;921(1):3–13.

    Article  CAS  Google Scholar 

  21. Good P. Permutation tests: a practical guide to resampling methods for testing hypotheses. Berlin: Springer Science & Business Media; 2013. 288 p.

    Google Scholar 

  22. Gromski PS, Muhamadali H, Ellis DI, Xu Y, Correa E, Turner ML, et al. A tutorial review: metabolomics and partial least squares-discriminant analysis-a marriage of convenience or a shotgun wedding. Anal Chim Acta. 2015;879:10–23.

    Article  CAS  Google Scholar 

  23. Halassy B, Brgles M, Habjanec L, Balija ML, Kurtović T, Marchetti-Deschmann M, et al. Intraspecies variability in Vipera ammodytes ammodytes venom related to its toxicity and immunogenic potential. Comp Biochem Physiol C. 2011;153(2):223–30.

  24. Katajamaa M, Orešič M. Data processing for mass spectrometry-based metabolomics. J Chromatogr A. 2007;1158(1):318–28.

    Article  CAS  Google Scholar 

  25. Kuhl C, Tautenhahn R, Böttcher C, Larson TR, Neumann S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 2012;84(1):283–9.

    Article  CAS  Google Scholar 

  26. Libiseller G, Dvorzak M, Kleb U, Gander E, Eisenberg T, Madeo F, et al. IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics. 2015;16:118.

    Article  Google Scholar 

  27. Lievense R. Pharmaceutical quality by design using JMP®: solving product development and manufacturing problems. Cary: SAS Institute; 2018. 436 p.

    Google Scholar 

  28. Liu Z, Chen S, Zhou Y, Xie C, Zhu B, Zhu H, et al. Deciphering the venomic transcriptome of killer-wasp Vespa velutina. Sci Rep. 2015;5:9454.

  29. Monceau K, Bonnard O, Thiéry D. Vespa velutina: a new invasive predator of honeybees in Europe. J Pest Sci. 2014;87(1):1–16.

  30. Naz S, Vallejo M, García A, Barbas C. Method validation strategies involved in non-targeted metabolomics. J Chromatogr A. 2014;1353:99–105.

    Article  CAS  Google Scholar 

  31. Nystrom GS, Ward MJ, Ellsworth SA, Rokyta DR. Sex-based venom variation in the eastern bark centipede (Hemiscolopendra marginata). Toxicon. 2019;169:45–58.

  32. Ouyang Y, Tong H, Luo P, Kong H, Xu Z, Yin P, et al. A high throughput metabolomics method and its application in female serum samples in a normal menstrual cycle based on liquid chromatography-mass spectrometry. Talanta. 2018;185:483–90.

    Article  CAS  Google Scholar 

  33. Owen MD. Quantitative and temporal changes in honey bee venom—a review. Toxicon. 1983;21:329–32.

    Article  Google Scholar 

  34. Peiren N, Vanrobaeys F, de Graaf DC, Devreese B, Van Beeumen J, Jacobs FJ. The protein composition of honeybee venom reconsidered by a proteomic approach. Biochim Biophys Acta. 2005;1752(1):1–5.

    Article  CAS  Google Scholar 

  35. Pennington MW, Czerwinski A, Norton RS. Peptide therapeutics from venom: current status and potential. Bioorg Med Chem. 2018;26(10):2738–58.

    Article  CAS  Google Scholar 

  36. Piek T. Venoms of the Hymenoptera: biochemical, pharmacological and behavioural aspects. Amsterdam: Elsevier; 2013. 583 p.

    Google Scholar 

  37. Rome Q, Muller FJ, Touret-Alby A, Darrouzet E, Perrard A, Villemant C. Caste differentiation and seasonal changes in Vespa velutina (Hym.: Vespidae) colonies in its introduced range. J Appl Entomol. 2015;139(10):771–82.

  38. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006;78(3):779–87.

    Article  CAS  Google Scholar 

  39. Sookrung N, Wong-din-Dam S, Tungtrongchitr A, Reamtong O, Indrawattana N, Sakolvaree Y, et al. Proteome and allergenome of Asian wasp, Vespa affinis, venom and IgE reactivity of the venom components. J Proteome Res. 2014;13(3):1336–44.

  40. Szymańska E, Saccenti E, Smilde AK, Westerhuis JA. Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics. 2012;8(Suppl 1):3–16.

    Article  Google Scholar 

  41. Touchard A, Dejean A, Escoubas P, Orivel J. Intraspecific variations in the venom peptidome of the ant Odontomachus haematodus (Formicidae: Ponerinae) from French Guiana. J Hymenopt Res. 2018 Mar 20;47:87–101.

  42. Wang X, Zhao X, Gu L, Zhang Y, Bi K, Chen X. Discrimination of aqueous and vinegary extracts of Shixiao San using metabolomics coupled with multivariate data analysis and evaluation of anti-hyperlipidemic effect. Asian J Pharm Sci. 2014;9(1):17–26.

    Article  Google Scholar 

  43. Westerhuis JA, Hoefsloot HCJ, Smit S, Vis DJ, Smilde AK, van Velzen EJJ, et al. Assessment of PLSDA cross validation. Metabolomics. 2008;4(1):81–9.

    Article  CAS  Google Scholar 

  44. Worley B, Powers R. Multivariate analysis in metabolomics. Curr Metabolomics. 2013;1(1):92–107.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37(Web Server issue):W652–60.

    Article  CAS  Google Scholar 

  46. Zheng H, Clausen MR, Dalsgaard TK, Mortensen G, Bertram HC. Time-saving design of experiment protocol for optimization of LC-MS data processing in metabolomic approaches. Anal Chem. 2013;85(15):7109–16.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

We express our acknowledgement to Eric DARROUZET of the Institut de Recherche sur la Biologie de l’Insecte (IRBI, UMR 7261) for providing the Asian hornets.

Funding

We are grateful to the Centre-Val-de-Loire Region (France) and the program ARD 2020 Cosmétosciences for financial support.

Author information

Authors and Affiliations

  1. CNRS, ICOA, UMR 7311, University of Orléans, 45067, Orléans, France

    Thao Nhi Le, David da Silva, Cyril Colas & Benoît Maunit

  2. CNRS, CBM, UPR 4301, University of Orléans, 45071, Orléans, France

    Cyril Colas & Patrick Baril

  3. Faculty of Sciences, IRBI, UMR CNRS 7261, University of Tours, Parc de Grandmont, 37200, Tours, France

    Eric Darrouzet

  4. CHIMEX (groupe L’Oréal), 16 rue Maurice Berteaux, 95500, Le Thillay, France

    Lucie Leseurre

  5. Université Clermont Auvergne, INSERM, IMost, 63000, Clermont-Ferrand, France

    Benoît Maunit

Authors
  1. Thao Nhi Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cyril Colas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eric Darrouzet
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Patrick Baril
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lucie Leseurre
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Benoît Maunit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David da Silva.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving animals

Asian hornets in France are not currently covered by legislation on the protection of animals used for scientific purposes.

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1

(PDF 938 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Le, T.N., da Silva, D., Colas, C. et al. Development of an LC–MS multivariate nontargeted methodology for differential analysis of the peptide profile of Asian hornet venom (Vespa velutina nigrithorax): application to the investigation of the impact of collection period variation. Anal Bioanal Chem 412, 1419–1430 (2020). https://doi.org/10.1007/s00216-019-02372-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-019-02372-2

Keywords

Search

Navigation