Advertisement

Nanotoxicity pp 271-280 | Cite as

Metabolic Fingerprinting of Bacteria Exposed to Nanomaterials, Using Online Databases, NMR, and High-Resolution Mass Spectrometry

  • Theodoros G. Chatzimitakos
  • Constantine D. StalikasEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1894)

Abstract

Nanomaterials are examined more and more for their antibacterial properties. Herein, we propose a method for assessing the bactericidal properties of nanomaterials against Escherichia coli and Staphylococcus aureus, as well as a method to investigate the metabolic alterations occurring to bacteria, induced by their exposure to nanomaterials. Bacterial metabolome is extracted and metabolic fingerprint is recorded by 1H-NMR. Using metabolomic databases, the tentative metabolites in the samples are revealed, which are further confirmed by UHPLC-HRMS. Finally, conducting a pathway analysis, the metabolic network is revealed.

Key words

Metabolomics Nanomaterials Bacteria Escherichia coli Staphylococcus aureus Antibacterial properties Metabolite extraction 1H-NMR UHPLC-HRMS Fingerprint 

References

  1. 1.
    Wang L, Hu C, Shao L (2017) The antimicrobial activity of nanoparticles: present situation and prospects for the future. Int J Nanomedicine 12:1227–1249.  https://doi.org/10.2147/IJN.S121956CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Chatzimitakos TG, Stalikas CD (2016) Qualitative alterations of bacterial metabolome after exposure to metal nanoparticles with bactericidal properties: a comprehensive workflow based on 1H NMR, UHPLC-HRMS, and metabolic databases. J Proteome Res 15(9):3322–3330.  https://doi.org/10.1021/acs.jproteome.6b00489CrossRefPubMedGoogle Scholar
  3. 3.
    Liebeke M, Lalk M (2014) Staphylococcus aureus metabolic response to changing environmental conditions – a metabolomics perspective. Int J Med Microbiol 304(3):222–229.  https://doi.org/10.1016/j.ijmm.2013.11.017CrossRefPubMedGoogle Scholar
  4. 4.
    Kosmides AK, Kamisoglu K, Calvano SE et al (2013) Metabolomic fingerprinting: challenges and opportunities. Crit Rev Biomed Eng 41(3):205–221.  https://doi.org/10.1615/CritRevBiomedEng.2013007736CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Xia J, Wishart DS (2002) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. In: Current protocols in bioinformatics. John Wiley & Sons, Inc, New York, NY.  https://doi.org/10.1002/cpbi.11CrossRefGoogle Scholar
  6. 6.
    Sajed T, Marcu A, Ramirez M et al (2016) ECMDB 2.0: a richer resource for understanding the biochemistry of E. coli. Nucleic Acids Res 44(Database issue):D495–D501.  https://doi.org/10.1093/nar/gkv1060CrossRefPubMedGoogle Scholar
  7. 7.
    Chatzimitakos T, Kallimanis A, Avgeropoulos A, Stalikas CD (2016) Antibacterial, anti-biofouling, and antioxidant prospects of metal-based nanomaterials. Clean (Weinh) 44(7):794–802.  https://doi.org/10.1002/clen.201500366CrossRefGoogle Scholar
  8. 8.
    Chatzimitakos T, Exarchou V, Ordoudi SA, Fiamegos Y, Stalikas C (2016) Ion-pair assisted extraction followed by 1H NMR determination of biogenic amines in food and biological matrices. Food Chem 202:445–450.  https://doi.org/10.1016/j.foodchem.2016.02.013CrossRefPubMedGoogle Scholar
  9. 9.
    Wishart DS, Jewison T, Guo AC, Wilson M et al (2013) HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801–D807.  https://doi.org/10.1093/nar/gks1065CrossRefPubMedGoogle Scholar
  10. 10.
    Cui Q, Lewis IA, Hegeman AD et al (2008) Metabolite identification via the Madison Metabolomics Consortium Database. Nat Biotechnol 26:162.  https://doi.org/10.1038/nbt0208-162CrossRefPubMedGoogle Scholar
  11. 11.
    Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G (2012) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30(9):826–828.  https://doi.org/10.1038/nbt.2348CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    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(7):703–714.  https://doi.org/10.1002/jms.1777CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Theodoros G. Chatzimitakos
    • 1
  • Constantine D. Stalikas
    • 1
    Email author
  1. 1.Department of ChemistryUniversity of IoanninaIoanninaGreece

Personalised recommendations