Combined OPLS-DA and decision tree as a strategy to identify antimicrobial biomarkers of volatile oils analyzed by gas chromatography-mass spectrometry

Abstract

Bioguided isolation to discriminate antimicrobial compounds from volatile oils is a time- and money-consuming process. Considering the limitations of the classical methods, it would be a great improvement to use chemometric techniques to identify putative biomarkers from volatile oils. For this purpose, antimicrobial assays of volatile oils extracted from different plant species were carried out against Streptococcus mutans. Eight volatile oils that showed different antimicrobial effects (inactive, weakly active, moderately active and very active) were selected in this work. The volatile oils’ composition was determined by GC-MS-based metabolomic analysis. Orthogonal projection to latent structures discriminant analysis and decision tree were carried out to access the metabolites that were highly correlated with a good antimicrobial activity. Initially, the GC-MS metabolomic data were pretreated by different methods such as centering, autoscaling, Pareto scaling, level scaling and power transformation. The level scaling was selected by orthogonal projection to latent structures discriminant analysis as the best pre-treatment according to the validation results. Based on this data, decision tree was also carried out using the same pretreatment. Both techniques (orthogonal projection to latent structures discriminant analysis and decision tree) pointed palmitic acid as a discriminant biomarker for the antimicrobial activity of the volatile oils against S. mutans. Additionally, orthogonal projection to latent structures discriminant analysis and decision tree predicted as “very active” the antimicrobial activity of volatile oils, which did not belong to the training group. This predicted result is in agreement with our experimental result (MIC = 31.25 εg ml−1). The present study can contribute to the development of useful strategies to help identifying antimicrobial constituents of complex oils.

References

  1. Adams, R.P., 2007. Identification of Essential Oil Components by Gas Chromatography/Mass Spectrometry, 4th ed. Allured Publishing Corporation, Illinois.

    Google Scholar 

  2. Avrahami, D., Shai, Y., 2004. A new group of antifungal and antibacterial lipopeptides derived from non-membrane active peptides conjugated to palmitic acid. J. Biol. Chem. 13, 12277–12285.

    Article  Google Scholar 

  3. Bachir, R.G., Benali, M., 2012. Antibacterial activity of the essential oils from the leaves of Eucalyptus globules against Escherichia coli and Staphylococcus aureus. Asian Pac. J. Trop. Biomed. 2, 739–742.

    Article  Google Scholar 

  4. Balouiri, M., Sadiki, M., Ibnsouda, S.K., 2016. Methods for invitro evaluating antimicrobial activity: a review. J. Pharm. Anal. 6, 71–79.

    Article  PubMed Central  Google Scholar 

  5. Barrero, A.F., Quilez Del Moral, J.F., Lara, A., Herrador, M.M., 2005. Antimicrobial activity of sesquiterpenes from essential oil of Juniperus thurifera. Planta Med. 71, 67–71.

    CAS  Article  PubMed Central  Google Scholar 

  6. Bhargava, N., Sharma, G., Bhargava, R., Mathuria, M., 2013. Decision Tree analysis on J48 algorithm for data mining. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 6, 1114–1119.

    Google Scholar 

  7. Boccard, J., Rutledge, D.N.A., 2013. A consensus orthogonal partial least squares discriminant analysis (OPLS-DA) strategy for multiblock OMICS data fusion. Anal. Chim. Acta 769, 30–39.

    CAS  Article  PubMed Central  Google Scholar 

  8. Canales, M., Hernández, T., Rodríguez-Moroy, M.A., Jiménez-Estrada, M., Flores, C.M., Hernández, L.B., Gijón, I.C., Quiroz, S., García, A.M., Avila, G., 2008. Antimicrobial activity of the extracts and essential oil of Viguiera dentata. Pharm. Biol. 46, 719–723.

    CAS  Article  Google Scholar 

  9. Chagas-Paula, D.A., Zhang, T., Da Costa, F.B., Edrada-Ebel, R., 2015a. A metabolomic approach to target compounds from the Asteraceae family for dual COX and LOX inhibition. Metabolites 5, 404–430.

    CAS  Article  PubMed Central  Google Scholar 

  10. Chagas-Paula, D.A., Oliveira, T.B., Zhang, T., Edrada-Ebel, R., Da Costa, F.B., 2015b. Prediction of anti-inflammatory plants and discovery of their biomarkers by machine learning algorithms and metabolomic studies. Planta Med. 81, 450–458.

    CAS  Article  PubMed Central  Google Scholar 

  11. Dahham, S.S., Tabana, Y.M., Iqbal, M.A., Ahamed, M.B.K., Ezzat, M.O., Majid, A.S.A., Majid, A.M.S.A., 2015. The anticancer, antioxidant and antimicrobial properties of the sesquiterpene β-caryophyllene from the essential oil ofAquilaria crassna. Molecules 20, 11808–11829.

    CAS  Article  PubMed Central  Google Scholar 

  12. Endo, A., Shibata, T., Tanaka, H., 2008. Comparison of seven algorithms to predict breast cancer survival. Int. J. Biomed. Soft Comput. Hum. Sci. 13, 11–16.

    Google Scholar 

  13. Eriksson, L., Rosén, J., Johansson, E., Trygg, J., 2012. Orthogonal PLS (OPLS) modeling for improved analysis and interpretation in drug design. Mol. Inform. 31, 414–419.

    CAS  Article  PubMed Central  Google Scholar 

  14. Fiehn, O., 2002. Metabolomics—the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171.

    CAS  Article  PubMed Central  Google Scholar 

  15. Huang, C.B., Alimova, Y., Myers, T.M., Ebersole, J.L., 2011. Short-and medium-chain fatty acids exhibit antimicrobial activity for oral microorganisms. Arch. Oral Biol. 56, 650–654.

    CAS  Article  PubMed Central  Google Scholar 

  16. Ibrahim, H.R., Kato, A., Kobayashi, K., 1991. Antimicrobial effects of lysozyme against gram-negative bacteria due to covalent binding of palmitic acid. J. Agric. Food Chem. 39, 2077–2082.

    CAS  Article  Google Scholar 

  17. Iscan, G., Kirimer, N., Kurkcuoglu, M., Baser, H.C., Demirci, F., 2002. Antimicrobial screening of Mentha piperita essential oils. J. Agric. Food Chem. 50, 3943–3946.

    CAS  Article  PubMed Central  Google Scholar 

  18. Kabara, J.J., Swieczkowski, D.M., Conley, A.J., Truant, J., 1972. Fatty acids and derivatives as antimicrobial agents. Antimicrob. Agents Chemother. 2, 23–28.

    CAS  Article  PubMed Central  Google Scholar 

  19. Kettaneh, N., Berglund, A., Wold, S., 2005. PCA and PLS with very large data sets. Comput. Stat. Data Ann. 48, 69–85.

    Article  Google Scholar 

  20. Kovács, J.K., Horváth, G., Kerényi, M., Kocsis, B., Emody, L., Schneider, G., 2016. A modified bioautographic method for antibacterial component screening against anaerobic and microaerophilic bacteria. J. Microbiol. Methods 123, 13–17.

    Article  PubMed Central  Google Scholar 

  21. Krastanov, A., 2010. Metabolomics—the state of art. Biotechnol. Biotechnol. Equip. 1, 1537–1543.

    Article  Google Scholar 

  22. Maree, J., Kamatou, G., Gibbons, S., Viljoen, A., Vuuren, S.V., 2014. The application of GC-MS combined with chemometrics for the identification of antimicrobial compounds from selected commercial essential oils. Chemometr. Intell. Lab. 13, 172–181.

    Article  Google Scholar 

  23. Onawunmi, G.O., Yisak, W.A., Ogunlana, E.O., 1984. Antibacterial constituents in the essential oil of Cymbopogon citrates (DC.) Stapf. J. Ethnopharmacol. 12, 279–286.

    CAS  Article  PubMed Central  Google Scholar 

  24. Pan, L., Qiu, Y., Chen, T., Lin, J., Chi, Y., Su, M., Zhao, A., Jia, W., 2010. An optimized procedure for metabolomic analysis of rat liver tissue using gas chromatography/time-of-flight mass spectrometry. J. Pharm. Biomed. Anal. 52, 589–596.

    CAS  Article  PubMed Central  Google Scholar 

  25. Rios, J.L., Recio, M.C., 2005. Medicinal plants and antimicrobial activity. J. Ethnophar-macol. 100, 80–84.

    CAS  Article  Google Scholar 

  26. Santos, A.O.S., Ueda-Nakamura, T., Filho, B.P.D., Veiga Junior, V.F., Pinto, A.C., Naka-mura, C.V., 2008. Antimicrobial activity of Brazilian copaiba oils obtained from different species of the Copaifera genus. Mem. Inst. Oswaldo Cruz 103, 277–281.

    Article  PubMed Central  Google Scholar 

  27. Scopus, Elsevier, 2018. https://www.scopus.com/ (accessed 26 February 2018).

  28. Singh, G., Kapoor, I.P.S., Singh, P., Heluani, CS., Lampasona, M.P., Catalan, C.A.N., 2008. Chemistry, antioxidant and antimicrobial investigations on essential oil and oleoresins of Zingiber officinale. Food Chem. Toxicol. 46, 3295–3302.

    CAS  Article  PubMed Central  Google Scholar 

  29. Siroli, L., Patrignani, F., Gardini, F., Lanciotti, R., 2015. Effects of sub-lethal concentrations of thyme and oregano essential oils, carvacrol, thymol, citral and trans-2-hexenal on membrane fatty acid composition and volatile molecule profile of Listeria monocytogenes, Escherichia coli and Salmonella enteritidis. Food Chem. 182, 185–192.

    CAS  Article  PubMed Central  Google Scholar 

  30. Tapp, H.S., Kemsley, E.K., 2009. Note on the practical utility of OPLS. Trends Analyt. Chem. 11, 1322–1327.

    Article  Google Scholar 

  31. Trygg, J., Wold, S., 2002. Orthogonal projections to latent structures (OPLS). J. Chemometrics 16, 119–128.

    CAS  Article  Google Scholar 

  32. Van den Berg, R.A., Hoefsloot, H.C.J., Westerhuis, J.A., Smilde, A.K., van der Werf, M.J., 2006. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 142, 1–15.

    Google Scholar 

  33. Verron, T., Sabatier, R., Joffre, R., 2004. Some theoretical properties of OPLS method. J. Chemometrics 18, 62–68.

    CAS  Article  Google Scholar 

  34. Wiklund, S., Johansson, E., Sjöström, L., Mellerowicz, E.J., Edlund, U., Shockcor, J.P., Gottfries, J., Moritz, T., Trygg, J., 2008. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal. Chem. 80, 115–122.

    CAS  Article  PubMed Central  Google Scholar 

  35. Wold, S., Esbensen, K., Geladi, P., 1987. Principal component analysis. Chemometr. Intell. Lab. 2, 37–52.

    CAS  Article  Google Scholar 

  36. Wold, S., Sjöström, M., Eriksson, L., 2001. PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab. 58, 109–130.

    CAS  Article  Google Scholar 

  37. Yff, B.T.S., Lindsey, K.L., Taylor, M.B., Erasmus, D.G., Jäger, A.L., 2002. The pharmacological screening of Pentanisia prunelloides and the isolation of the antibacterial compound palmitic acid. J. Ethnopharmacol. 79, 101–107.

    CAS  Article  PubMed Central  Google Scholar 

  38. Zarkami, R., 2011. Application of classification tree-J48 to model the presence of roach (Rutilus rutilus) in rivers. Caspian J. Environ. Sci. 2, 189–198.

    Google Scholar 

  39. Zhang, W., Zhu, S., He, S., Wang, Y., 2015. Screening of oil sources by using comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry and multivariate statistical analysis. J. Chromatogr. A 1380, 162–170.

    CAS  Article  PubMed Central  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Fernando B. Da Costa.

Additional information

F.A.S., I.P.S. and F.B.C. contributed in collecting plant samples and their identifications. I.P.S. extracted the VOs and performed the metabolite identification and antimicrobial assays. F.A.S. performed the pretreatments of the GC-MS data and the chemometric analysis. F.A.S. and I.P.S. drafted the manuscript. F.A.S. and I.P.S. contributed equally in this work. N.A.J.C.F. and F.B.C. designed the experiments, supervised the laboratory works and contributed to critical reading of the manuscript. All the authors have read the final manuscript and approved the submission.

Rights and permissions

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

dos Santos, F.A., Sousa, I.P., Furtado, N.A.J.C. et al. Combined OPLS-DA and decision tree as a strategy to identify antimicrobial biomarkers of volatile oils analyzed by gas chromatography-mass spectrometry. Rev. Bras. Farmacogn. 28, 647–653 (2018). https://doi.org/10.1016/j.bjp.2018.08.006

Download citation

Keywords

  • Antimicrobial activity
  • Chemometrics
  • Decision tree
  • Volatile oils
  • Gas chromatography-mass spectrometry
  • Orthogonal projection to latent structures
  • discriminant analysis