Advertisement

Metabolomics

, 15:33 | Cite as

Untargeted metabolomics analysis of the upper respiratory tract of ferrets following influenza A virus infection and oseltamivir treatment

  • David J. BealeEmail author
  • Ding Yuan OhEmail author
  • Avinash V. Karpe
  • Celeste Tai
  • Michael S. Dunn
  • Danielle Tilmanis
  • Enzo A. Palombo
  • Aeron C. Hurt
Original Article

Abstract

Introduction

Influenza is a highly contagious respiratory disease that causes high global morbidity and mortality each year. The dynamics of an influenza infection on the host metabolism, and how metabolism is altered in response to neuraminidase inhibitor drug therapy, is still in its infancy but of great importance.

Objectives

We aim to investigate the suitability of ferret nasal wash samples for metabolomics-based analysis and characterization of influenza infections and oseltamivir treatment.

Methods

Virological and metabolic analyses were performed on nasal wash samples collected from ferrets treated with oseltamivir or a placebo. Untargeted metabolomics was performed using a gas chromatography coupled with mass spectrometery (GC-MS) based protocol that comprised a retention time (RT) locked method and the use of a commercial metabolomics library.

Results

Ferret activity was reduced at 2–3 days post infection, which coincided with the highest influenza viral titre. The metabolomics data indicated a shift in metabolism during various stages of infection. The neuraminidase inhibitor oseltamivir created considerable downregulation of energy center metabolites (glucose, sucrose, glycine and glutamine), which generated high levels of branched amino acids. This further increased branched amino acid degradation and deregulation via glycerate-type intermediates and biosynthesis of fatty acids in oseltamivir-treated animals where abrogated weight loss was observed.

Conclusion

Metabolomics was used to profile influenza infection and antiviral drug treatment in ferrets. This has the potential to provide indicators for the early diagnosis of influenza infection and assess the effectiveness of drug therapies.

Keywords

Influenza virus Oseltamivir Metabolomics GC-MS Chemometrics Branched amino acid down-regulation 

Notes

Acknowledgements

The Melbourne WHO Collaborating Centre for Reference and Research on Influenza is supported by the Australian Government Department of Health.

Author contributions

DJB and DYO performed the experiments, data analysis and co-wrote the paper; AVK, CT, MSD and DT provided technical support and performed part of the sample analysis with DJB and DYO; EAP and ACH assisted to devise and supervised the project. All authors contributed to the authorship of the manuscript and have read and approved the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

Supplementary material

11306_2019_1499_MOESM1_ESM.docx (478 kb)
Supplementary material 1 (DOCX 477 KB)

References

  1. Akaike, T., Ando, M., Oda, T., Doi, T., Ijiri, S., Araki, S., & Maeda, H. (1990). Dependence on o2-generation by xanthine oxidase of pathogenesis of influenza virus infection in mice. The Journal of Clinical Investigation, 85, 739–745.CrossRefGoogle Scholar
  2. Azizan, K. A., Baharum, S. N., Ressom, H. W., & Noor, N. M. (2012). GC–MS analysis and PLS-DA validation of the trimethyl silyl-derivatization techniques. American Journal of Applied Sciences, 9, 1124–1136.CrossRefGoogle Scholar
  3. Beale, D., Jones, O., Karpe, A., Dayalan, S., Oh, D., Kouremenos, K., Ahmed, W., & Palombo, E. (2017). A review of analytical techniques and their application in disease diagnosis in breathomics and salivaomics research. International Journal of Molecular Sciences, 18, 24.CrossRefGoogle Scholar
  4. Beale, D., Morrison, P., Key, C., & Palombo, E. (2014). Metabolic profiling of biofilm bacteria known to cause microbial influenced corrosion. Water Science and Technology, 69, 1–8.CrossRefGoogle Scholar
  5. Beale, D. J., Karpe, A. V., McLeod, J. D., Gondalia, S. V., Muster, T. H., Othman, M. Z., Palombo, E. A., & Joshi, D. (2016). An ‘omics’ approach towards the characterisation of laboratory scale anaerobic digesters treating municipal sewage sludge. Water Research, 88, 346–357.CrossRefGoogle Scholar
  6. Beale, D. J., Marney, D., Marlow, D. R., Morrison, P. D., Dunn, M. S., Key, C., & Palombo, E. A. (2013). Metabolomic analysis of cryptosporidium parvum oocysts in water: A proof of concept demonstration. Environmental Pollution, 174, 201–203.CrossRefGoogle Scholar
  7. Berenson, R. J., & Faller, D. V. (2011). Methods and compositions for treating viral or virally-induced conditions in Hemaquest Pharmaceuticals, I., Trustees Of Boston University (Ed). Hemaquest Pharmaceuticals Inc., Boston UniversityGoogle Scholar
  8. Bouvier, N. M., & Lowen, A. C. (2010). Animal models for influenza virus pathogenesis and transmission. Viruses, 2, 1530.CrossRefGoogle Scholar
  9. Chandler, J. D., Hu, X., Ko, E. J., Park, S., Lee, Y. T., Orr, M. L., et al. (2016). Metabolic pathways of lung inflammation revealed by high-resolution metabolomics (hrm) of h1n1 influenza virus infection in mice. American Journal of Physiology-Heart and Circulatory Physiology, 311, R906–R916.Google Scholar
  10. Chen, L., Fan, J., Li, Y., Shi, X., Ju, D., Yan, Q., Yan, X., Han, L., & Zhu, H. (2014). Modified jiu wei qiang huo decoction improves dysfunctional metabolomics in influenza a pneumonia-infected mice. Biomed Chromatogr, 28, 468–474.CrossRefGoogle Scholar
  11. Chung, D. H., Golden, J. E., Adcock, R. S., Schroeder, C. E., Chu, Y. K., Sotsky, J. B., et al. (2016). Discovery of a broad-spectrum antiviral compound that inhibits pyrimidine biosynthesis and establishes a type 1 interferon-independent antiviral state. Antimicrob Agents Chemother, 60, 4552–4562.CrossRefGoogle Scholar
  12. Cui, L., Zheng, D., Lee, Y. H., Chan, T. K., Kumar, Y., Ho, W. E., Chen, J. Z., Tannenbaum, S. R., & Ong, C. N. (2016). Metabolomics investigation reveals metabolite mediators associated with acute lung injury and repair in a murine model of influenza pneumonia. Science Reports, 6, 26076.CrossRefGoogle Scholar
  13. Dimmock, N. J., Dove, B. K., Meng, B., Scott, P. D., Taylor, I., Cheung, L., et al. (2012). Comparison of the protection of ferrets against pandemic 2009 influenza a virus (h1n1) by 244 di influenza virus and oseltamivir. Antiviral Res, 96, 376–385.CrossRefGoogle Scholar
  14. Fiehn, O., Robertson, D., Griffin, J., van der Werf, M., Nikolau, B., Morrison, N., et al. (2007). The metabolomics standards initiative (msi). Metabolomics, 3, 175–178.CrossRefGoogle Scholar
  15. Francis, T. Jr., & Stuart-Harris, C. H. (1938). Studies on the nasal histology of epidemic influenza virus infection in the -ferret. I. The development and repair of the nasal lesion. Journal of Experimental Medicine, 68, 789–801.CrossRefGoogle Scholar
  16. Fu, Y., Gaelings, L., Soderholm, S., Belanov, S., Nandania, J., Nyman, T. A., et al. (2016). Jnj872 inhibits influenza a virus replication without altering cellular antiviral responses. Antiviral Research, 133, 23–31.CrossRefGoogle Scholar
  17. Ghosh, S. K., Perrine, S. P., Williams, R. M., & Faller, D. V. (2012). Histone deacetylase inhibitors are potent inducers of gene expression in latent ebv and sensitize lymphoma cells to nucleoside antiviral agents. Blood, 119, 1008.CrossRefGoogle Scholar
  18. Govorkova, E. A., Ilyushina, N. A., Boltz, D. A., Douglas, A., Yilmaz, N., & Webster, R. G. (2007). Efficacy of oseltamivir therapy in ferrets inoculated with different clades of h5n1 influenza virus. Antimicrobial Agents and Chemotherapy, 51, 1414–1424.CrossRefGoogle Scholar
  19. Karpe, A. V., Beale, D. J., Godhani, N. B., Morrison, P. D., Harding, I. H., & Palombo, E. A. (2015a). Untargeted metabolic profiling of winery-derived biomass waste degradation by Penicillium chrysogenum. Journal of Agricultural and Food Chemistry, 63, 10696–10704.CrossRefGoogle Scholar
  20. Karpe, A. V., Beale, D. J., Harding, I. H., & Palombo, E. A. (2015b). Optimization of degradation of winery-derived biomass waste by ascomycetes. Journal of Chemical Technology & Biotechnology, 90, 1793–1801.CrossRefGoogle Scholar
  21. Kobzik, L. (2017). Searching for a lifeline: Transcriptome profiling studies of influenza susceptibility and resistance. Journal of Innate Immunity, 9, 232–242.CrossRefGoogle Scholar
  22. Li, J., Zhang, D., Zhu, X., He, Z., Liu, S., Li, M., Pang, J., & Lin, Y. (2011). Studies on synthesis and structure-activity relationship (SAR) of derivatives of a new natural product from marine fungi as inhibitors of influenza virus neuraminidase. Marine Drugs, 9, 1887–1901.CrossRefGoogle Scholar
  23. Lietzen, N., Ohman, T., Rintahaka, J., Julkunen, I., Aittokallio, T., Matikainen, S., & Nyman, T. A. (2011). Quantitative subcellular proteome and secretome profiling of influenza a virus-infected human primary macrophages. PLoS Pathogen, 7, e1001340.CrossRefGoogle Scholar
  24. Lin, S., Liu, N., Yang, Z., Song, W., Wang, P., Chen, H., et al. (2010). GC/MS-based metabolomics reveals fatty acid biosynthesis and cholesterol metabolism in cell lines infected with influenza a virus. Talanta, 83, 262–268.CrossRefGoogle Scholar
  25. Metabolomic Society (2006) Metabolomics standards initiative (msi), CIMR: In vivo context.Google Scholar
  26. Monto, A. S., & Maassab, H. F. (1981). Ether treatment of type b influenza virus antigen for the hemagglutination inhibition test. Journal of Clinical Microbiology, 13, 54–57.Google Scholar
  27. Oh, D. Y., Barr, I. G., & Hurt, A. C. (2015). A novel video tracking method to evaluate the effect of influenza infection and antiviral treatment on ferret activity. PLoS ONE, 10, e0118780.CrossRefGoogle Scholar
  28. Oh, D. Y., Barr, I. G., Mosse, J. A., & Laurie, K. L. (2008). Mdck-siat1 cells show improved isolation rates for recent human influenza viruses compared to conventional mdck cells. Journal of Clinical Microbiology, 46, 2189–2194.CrossRefGoogle Scholar
  29. Perrine, S. P., Hermine, O., Small, T., Suarez, F., Reilly, R., Boulad, F., et al. (2007). A phase 1/2 trial of arginine butyrate and ganciclovir in patients with epstein-barr virus-associated lymphoid malignancies. Blood, 109, 2571.CrossRefGoogle Scholar
  30. Rabinowitz, J. D., Purdy, J. G., Vastag, L., Shenk, T., & Koyuncu, E. (2011). Metabolomics in drug target discovery. Cold Spring Harbor Symposia on Quantitative Biology, 76, 235–246.CrossRefGoogle Scholar
  31. Ritter, J. B., Wahl, A. S., Freund, S., Genzel, Y., & Reichl, U. (2010). Metabolic effects of influenza virus infection in cultured animal cells: Intra- and extracellular metabolite profiling. BMC Systems Biology, 4, 61.CrossRefGoogle Scholar
  32. Snowden, S., Dahlen, S. E., & Wheelock, C. E. (2012). Application of metabolomics approaches to the study of respiratory diseases. Bioanalysis, 4, 2265–2290.CrossRefGoogle Scholar
  33. Söderholm, S., Fu, Y., Gaelings, L., Belanov, S., Yetukuri, L., Berlinkov, M., et al. (2016). Multi-omics studies towards novel modulators of influenza a virus–host interaction. Viruses, 8, 269.CrossRefGoogle Scholar
  34. Spector, I. C., Feitelson, M. A., & Arzumanyan, A. (2018) Use of short chain fatty acids for the treatment and prevention of diseases and disorders. Philadelphia, PA: Temple University.Google Scholar
  35. Stencel-Baerenwald, J. E., Reiss, K., Reiter, D. M., Stehle, T., & Dermody, T. S. (2014). The sweet spot: Defining virus–sialic acid interactions. Nature Reviews Microbiology, 12, 739.CrossRefGoogle Scholar
  36. WHO (2016) Influenza (seasonal) fact sheet. Geneva: WHOGoogle Scholar
  37. Wold, S., Sjöström, M., & Eriksson, L. (2001). Pls-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130.CrossRefGoogle Scholar
  38. Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). Metaboanalyst 3.0: Making metabolomics more meaningful. Nucleic Acids Research, 43, W251–W257.CrossRefGoogle Scholar
  39. Xia, J., & Wishart, D. S. (2010). Metpa: A web-based metabolomics tool for pathway analysis and visualization. Bioinformatics, 26, 2342–2344.CrossRefGoogle Scholar
  40. Xia, J., & Wishart, D. S. (2016). Using metaboanalyst 3.0 for comprehensive metabolomics data analysis. Current Protocols in Bioinformatics, 55, 14.10.1–14.10.91.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Commonwealth Scientific & Industrial Research Organization (CSIRO), Land & WaterBrisbaneAustralia
  2. 2.WHO Collaborating Centre for Reference and Research on Influenza, VIDRL, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
  3. 3.School of Health and Life SciencesFederation UniversityChurchillAustralia
  4. 4.Analytical Science and TechnologySeqirusParkvilleAustralia
  5. 5.Faculty of Science, Engineering and TechnologySwinburne University of TechnologyHawthornAustralia
  6. 6.Department of Microbiology and ImmunologyUniversity of MelbourneParkvilleAustralia

Personalised recommendations