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In plaque-mass spectrometry imaging of a bloom-forming alga during viral infection reveals a metabolic shift towards odd-chain fatty acid lipids

Abstract

Tapping into the metabolic crosstalk between a host and its virus can reveal unique strategies employed during infection. Viral infection is a dynamic process that generates an evolving metabolic landscape. Gaining a continuous view into the infection process is highly challenging and is limited by current metabolomics approaches, which typically measure the average of the entire population at various stages of infection. Here, we took an innovative approach to study the metabolic basis of host–virus interactions between the bloom-forming alga Emiliania huxleyi and its specific virus. We combined a classical method in virology, the plaque assay, with advanced mass spectrometry imaging (MSI), an approach we termed ‘in plaque-MSI’. Taking advantage of the spatial characteristics of the plaque, we mapped the metabolic landscape induced during infection in a high spatiotemporal resolution, unfolding the infection process in a continuous manner. Further unsupervised spatially aware clustering, combined with known lipid biomarkers, revealed a systematic metabolic shift during infection towards lipids containing the odd-chain fatty acid pentadecanoic acid (C15:0). Applying ‘in plaque-MSI’ may facilitate the discovery of bioactive compounds that mediate the chemical arms race of host–virus interactions in diverse model systems.

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Fig. 1: Overview of the workflow of ‘in plaque-MSI’ analysis.
Fig. 2: Targeted MSI analysis of known lipid biomarkers reveals the metabolic landscape produced during viral infection.
Fig. 3: Unsupervised spatially aware clustering allows the identification of unknown lipids.
Fig. 4: Induction of C15:0-based lipids during viral infection.
Fig. 5: Schematic model of the metabolic shift towards C15:0-based lipids in infected E.huxleyi cells.

Data availability

Data supporting the findings of this study are available within the paper (and Supplementary Information files). Raw data generated or analysed in this study have been deposited to the EMBL-EBI MetaboLights repository75 with the identifiers MTBLS767 (including LC–MS of plaque samples, LC–MS/MS of specific lipids and GC–MS) and MTBLS769 (including MALDI-MSI and Flow-probe-MSI). The data can be accessed with the following links: http://www.ebi.ac.uk/metabolights/MTBLS767 and http://www.ebi.ac.uk/metabolights/MTBLS769.

References

  1. 1.

    Olive, A. J. & Sassetti, C. M. Metabolic crosstalk between host and pathogen: sensing, adapting and competing. Nat. Rev. Microbiol. 14, 221–234 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    Magnúsdóttir, S. & Thiele, I. Modeling metabolism of the human gutmicrobiome. Curr. Opin. Biotechnol. 51, 90–96 (2018).

  3. 3.

    Aldridge, B. B. & Rhee, K. Y. Microbial metabolomics: innovation, application, insight. Curr. Opin. Microbiol. 19, 90–96 (2014).

    CAS  Article  Google Scholar 

  4. 4.

    Ankrah, N. Y. D. et al. Phage infection of an environmentally relevant marine bacterium alters host metabolism and lysate composition. ISME J. 8, 1089–1100 (2014).

    CAS  Article  Google Scholar 

  5. 5.

    De Smet, J. et al. High coverage metabolomics analysis reveals phage-specific alterations to Pseudomonas aeruginosa physiology during infection. ISME J. 10, 1823–1835 (2016).

    Article  Google Scholar 

  6. 6.

    Link, H., Fuhrer, T., Gerosa, L., Zamboni, N. & Sauer, U. Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat. Methods 12, 1091–1097 (2015).

    CAS  Article  Google Scholar 

  7. 7.

    Roe, B., Kensicki, E., Mohney, R. & Hall, W. W. Metabolomic profile of hepatitis C virus-infected hepatocytes. PLoS ONE 6, e23641 (2011).

    CAS  Article  Google Scholar 

  8. 8.

    Malitsky, S. et al. Viral infection of the marine alga Emiliania huxleyi triggers lipidome remodeling and induces the production of highly saturated triacylglycerol. New Phytol. 210, 88–96 (2016).

    CAS  Article  Google Scholar 

  9. 9.

    Frada, M. J. et al. Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi. PLoS Pathog. 13, e1006775 (2017).

    Article  Google Scholar 

  10. 10.

    Snijder, B. et al. Population context determines cell-to-cell variability in endocytosis and virus infection. Nature 461, 520–523 (2009).

    CAS  Article  Google Scholar 

  11. 11.

    Rosenwasser, S., Ziv, C., Creveld, S.G.V. & Vardi, A. Virocell metabolism: metabolic innovations during host–virus interactions in the ocean. Trends Microbiol. 24, 821–832 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Petras, D., Jarmusch, A. K. & Dorrestein, P. C. From single cells to our planet—recent advances in using mass spectrometry for spatially resolved metabolomics. Curr. Opin. Chem. Biol. 36, 24–31 (2017).

    CAS  Article  Google Scholar 

  13. 13.

    Dong, Y., Li, B. & Aharoni, A. More than pictures: when MS imaging meets histology. Trends Plant. Sci. 21, 686–698 (2016).

    CAS  Article  Google Scholar 

  14. 14.

    Ryffel, F. et al. Metabolic footprint of epiphytic bacteria on Arabidopsis thaliana leaves. ISME J. 10, 632–643 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    Watrous, J. D. et al. Microbial metabolic exchange in 3D. ISME J. 7, 770–780 (2013).

    CAS  Article  Google Scholar 

  16. 16.

    Lasch, P. et al. Identification of highly pathogenic microorganisms by matrix-assisted laser desorption ionization-time of flight mass spectrometry: results of an interlaboratory ring trial. J. Clin. Microbiol. 53, 2632–2640 (2015).

    CAS  Article  Google Scholar 

  17. 17.

    Simó, R. Production of atmospheric sulfur by oceanic plankton: biogeochemical, ecological and evolutionary links. Trends Ecol. Evol. 16, 287–294 (2001).

    Article  Google Scholar 

  18. 18.

    Lehahn, Y. et al. Decoupling physical from biological processes to assess the impact of viruses on a mesoscale algal bloom. Curr. Biol. 24, 2041–2046 (2014).

    CAS  Article  Google Scholar 

  19. 19.

    Rosenwasser, S. et al. Rewiring host lipid metabolism by large viruses determines the fate of Emiliania huxleyi, a bloom-forming alga in the ocean. Plant Cell 26, 2689–2707 (2014).

    CAS  Article  Google Scholar 

  20. 20.

    Fulton, J. M. et al. Novel molecular determinants of viral susceptibility and resistance in the lipidome of Emiliania huxleyi. Environ. Microbiol. 16, 1137–1149 (2014).

    CAS  Article  Google Scholar 

  21. 21.

    Hunter, J. E., Frada, M. J., Fredricks, H. F., Vardi, A. & Van Mooy, B. A. S. Targeted and untargeted lipidomics of Emiliania huxleyi viral infection and life cycle phases highlights molecular biomarkers of infection, susceptibility, and ploidy. Front. Mar. Sci. 2, 81 (2015).

    Article  Google Scholar 

  22. 22.

    Wilson, W. H. et al. Complete genome sequence and lytic phase transcription profile of a Coccolithovirus. Science 309, 1090–1092 (2005).

    CAS  Article  Google Scholar 

  23. 23.

    Vardi, A. et al. Viral glycosphingolipids induce lytic infection and cell death in marine phytoplankton. Science 326, 861–865 (2009).

    CAS  Article  Google Scholar 

  24. 24.

    Ziv, C. et al. Viral serine palmitoyltransferase induces metabolic switch in sphingolipid biosynthesis and is required for infection of a marine alga. Proc. Natl Acad. Sci. USA 113, E1907–E1916 (2016).

    CAS  Article  Google Scholar 

  25. 25.

    Sheyn, U. et al. Expression profiling of host and virus during a coccolithophore bloom provides insights into the role of viral infection in promoting carbon export. ISME J. 12, 704–713 (2018).

    CAS  Article  Google Scholar 

  26. 26.

    Bidle, K. D., Haramaty, L., Barcelos e Ramos, J. & Falkowski, P. Viral activation and recruitment of metacaspases in the unicellular coccolithophore, Emiliania huxleyi. Proc. Natl Acad. Sci. USA 104, 6049–6054 (2007).

    CAS  Article  Google Scholar 

  27. 27.

    Sheyn, U., Rosenwasser, S., Ben-Dor, S., Porat, Z. & Vardi, A. Modulation of host ROS metabolism is essential for viral infection of a bloom-forming coccolithophore in the ocean. ISME J. 10, 1742–1754 (2016).

    CAS  Article  Google Scholar 

  28. 28.

    Laber, C. P. et al. Coccolithovirus stimulation of carbon export in the North Atlantic. Nat. Microbiol. 3, 537–547 (2018).

    CAS  Article  Google Scholar 

  29. 29.

    Ellis, E. L. & Delbrück, M. The growth of bacteriophage. J. Gen. Physiol. 22, 365–384 (1939).

    CAS  Article  Google Scholar 

  30. 30.

    Cooper, P. D. The plaque assay of animal viruses. Adv. Virus Res. 8, 319–378 (1961).

    CAS  Article  Google Scholar 

  31. 31.

    Yin, J. Evolution of bacteriophage T7 in a growing plaque. J. Bacteriol. 175, 1272–1277 (1993).

    CAS  Article  Google Scholar 

  32. 32.

    Llewellyn, C. A. et al. The response of carotenoids and chlorophylls during virus infection of Emiliania huxleyi (Prymnesiophyceae). J. Exp. Mar. Bio. Ecol. 344, 101–112 (2007).

    CAS  Article  Google Scholar 

  33. 33.

    Hsu, C.-C. et al. Real-time metabolomics on living microorganisms using ambient electrospray ionization flow-probe. Anal. Chem. 85, 7014–7018 (2013).

    CAS  Article  Google Scholar 

  34. 34.

    Deininger, S. O., Ebert, M. P., Fütterer, A., Gerhard, M. & Röcken, C. MALDI imaging combined with hierarchical clustering as a new tool for the interpretation of complex human cancers. J. Proteome Res. 7, 5230–5236 (2008).

    CAS  Article  Google Scholar 

  35. 35.

    Alexandrov, T., Chernyavsky, I., Becker, M., von Eggeling, F. & Nikolenko, S. Analysis and interpretation of imaging mass spectrometry data by clustering mass-to-charge images according to their spatial similarity. Anal. Chem. 85, 11189–11195 (2013).

    CAS  Article  Google Scholar 

  36. 36.

    Sud, M. et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35, D527–D532 (2007).

    CAS  Article  Google Scholar 

  37. 37.

    Alcolombri, U. et al. Identification of the algal dimethyl sulfide-releasing enzyme: a missing link in the marine sulfur cycle. Science 348, 1466–1469 (2015).

    CAS  Article  Google Scholar 

  38. 38.

    Schatz, D. et al. Communication via extracellular vesicles enhances viral infection of a cosmopolitan alga. Nat. Microbiol. 2, 1485–1492 (2017).

    CAS  Article  Google Scholar 

  39. 39.

    Vardi, A. et al. Host–virus dynamics and subcellular controls of cell fate in a natural coccolithophore population. Proc. Natl Acad. Sci. USA 109, 19327–19332 (2012).

    CAS  Article  Google Scholar 

  40. 40.

    Gotoh, N. et al. Metabolism of odd-numbered fatty acids and even-numbered fatty acids in mouse. J. Oleo. Sci. 57, 293–299 (2008).

    CAS  Article  Google Scholar 

  41. 41.

    Long, A. M. & Short, S. M. Seasonal determinations of algal virus decay rates reveal overwintering in a temperate freshwater pond. ISME J. 10, 1602–1612 (2016).

    CAS  Article  Google Scholar 

  42. 42.

    Evans, C., Pond, D. W. & Wilson, W. H. Changes in Emiliania huxleyi fatty acid profiles during infection with E. huxleyi virus 86: physiological and ecological implications. Aquat. Microb. Ecol. 55, 219–228 (2009).

    Article  Google Scholar 

  43. 43.

    Sperl, W. et al. Odd-numbered long-chain fatty acids in propionic acidaemia. Eur. J. Pediatr. 159, 54–58 (2000).

    CAS  Article  Google Scholar 

  44. 44.

    Řezanka, T., Vítová, M., Nováková, A. & Sigler, K. Separation and identification of odd chain triacylglycerols of the protozoan Khawkinea quartana and the mold Mortierella alpina using LC–MS. Lipids 50, 811–820 (2015).

    Article  Google Scholar 

  45. 45.

    Böer, M., Graeve, M. & Kattner, G. Impact of feeding and starvation on the lipid metabolism of the Arctic pteropod Clione limacina. J. Exp. Mar. Bio. Ecol. 328, 98–112 (2006).

    Article  Google Scholar 

  46. 46.

    Narayanan, S., Tamura, P. J., Roth, M. R., Prasad, P. V. V. & Welti, R. Wheat leaf lipids during heat stress: I. High day and night temperatures result in major lipid alterations. Plant Cell Environ. 39, 787–803 (2016).

    CAS  Article  Google Scholar 

  47. 47.

    Ingram, L. O., Chevalier, L. S., Gabbay, E. J. & Winters, K. Priopionate-induced synthesis of odd-chain-length fatty acids by Escherichia coli. J. Bacteriol. 131, 1023–1025 (1977).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Oizumi, J., Giudici, T. A., Ng, W. G., Shaw, K. N. F. & Donnell, G. N. Propionate metabolism by cultured skin fibroblasts from normal individuals and patients with methylmalonicaciduria and propionicacidemia. Biochem. Med. 26, 28–40 (1981).

    CAS  Article  Google Scholar 

  49. 49.

    Wendel, U. Abnormality of odd-numbered long-chain fatty acids in erythrocyte membrane lipids from patients with disorders of propionate metabolism. Pediatr. Res. 25, 147–150 (1989).

    CAS  Article  Google Scholar 

  50. 50.

    Kondo, N. et al. Identification of the phytosphingosine metabolic pathway leading to odd-numbered fatty acids. Nat. Commun. 5, 5338 (2014).

    CAS  Article  Google Scholar 

  51. 51.

    Keller, M. D., Selvin, R. C., Claus, W. & Guillard, R. R. L. Media for the culture of oceanic ultraphytoplankton. J. Phycol. 23, 633–638 (2007).

    Article  Google Scholar 

  52. 52.

    Goyet, C. & Poisson, A. New determination of carbonic acid dissociation constants in seawater as a function of temperature and salinity. Deep Sea Res. Part A Oceanogr. Res. Pap. 36, 1635–1654 (1989).

    CAS  Article  Google Scholar 

  53. 53.

    Schroeder, D. C., Oke, J., Malin, G. & Wilson, W. H. Coccolithovirus (Phycodnaviridae): characterisation of a new large dsDNA algal virus that infects Emiliana huxleyi. Arch. Virol. 147, 1685–1698 (2002).

    CAS  Article  Google Scholar 

  54. 54.

    Barak-Gavish, N. et al. Bacterial virulence against an oceanic bloom-forming phytoplankter is mediated by algal DMSP. Sci. Adv. 4, eaau5716 (2018).

    Article  Google Scholar 

  55. 55.

    Watrous, J. D. & Dorrestein, P. C. Imaging mass spectrometry in microbiology. Nat. Rev. Microbiol. 9, 683–694 (2011).

    CAS  Article  Google Scholar 

  56. 56.

    Hoffmann, T. & Dorrestein, P. C. Homogeneous matrix deposition on dried agar for MALDI imaging mass spectrometry of microbial cultures. J. Am. Soc. Mass Spectrom. 26, 1959–1962 (2015).

    CAS  Article  Google Scholar 

  57. 57.

    Yang, Y.-L., Xu, Y., Straight, P. & Dorrestein, P. C. Translating metabolic exchange with imaging mass spectrometry. Nat. Chem. Biol. 5, 885–887 (2009).

    CAS  Article  Google Scholar 

  58. 58.

    Liu, W.-T. et al. Imaging mass spectrometry of intraspecies metabolic exchange revealed the cannibalistic factors of Bacillus subtilis. Proc. Natl Acad. Sci. USA 107, 16286–16290 (2010).

    CAS  Article  Google Scholar 

  59. 59.

    Yin, J. Spatially resolved evolution of viruses. Ann. N. Y. Acad. Sci. 745, 399–408 (1994).

    CAS  Article  Google Scholar 

  60. 60.

    Watrous, J. et al. Mass spectral molecular networking of living microbial colonies. Proc. Natl Acad. Sci. USA 109, E1743–E1752 (2012).

    CAS  Article  Google Scholar 

  61. 61.

    Adusumilli, R. & Mallick, P. Data conversion with ProteoWizard msConvert. Methods Mol. Biol. 1550, 339–368 (2017).

    CAS  Article  Google Scholar 

  62. 62.

    Schramm, T. et al. imzML—a common data format for the flexible exchange and processing of mass spectrometry imaging data. J. Proteomics 75, 5106–5110 (2012).

    CAS  Article  Google Scholar 

  63. 63.

    Race, A. M., Styles, I. B. & Bunch, J. Inclusive sharing of mass spectrometry imaging data requires a converter for all. J. Proteomics 75, 5111–5112 (2012).

    CAS  Article  Google Scholar 

  64. 64.

    Gibb, S. & Strimmer, K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28, 2270–2271 (2012).

    CAS  Article  Google Scholar 

  65. 65.

    Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. cluster: cluster analysis basics and extensions. R package version 2.0.5 (CRAN, 2016); https://cran.r-project.org

  66. 66.

    Neuwirth, E. RColorBrewer: ColorBrewer Palettes. R Package Version 1.1-2 (CRAN, 2014); https://cran.r-project.org

  67. 67.

    Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).

    Article  Google Scholar 

  68. 68.

    Wehrens, R. Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences (Springer, Berlin Heidelberg, 2011).

  69. 69.

    Böcker, S., Letzel, M. C., Lipták, Z. & Pervukhin, A. SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics 25, 218–224 (2009).

    Article  Google Scholar 

  70. 70.

    Robichaud, G., Garrard, K. P., Barry, J. A. & Muddiman, D. C. MSiReader: an open-source interface to view and analyze high resolving power MS imaging files on MATLAB platform. J. Am. Soc. Mass Spectrom. 24, 718–721 (2013).

    CAS  Article  Google Scholar 

  71. 71.

    Bokhart, M. T., Nazari, M., Garrard, K. P. & Muddiman, D. C. MSiReaderv1.0: evolving open-source mass spectrometry imaging software for targeted and untargeted analyses. J. Am. Soc. Mass Spectrom. 29, 8–16 (2018).

    CAS  Article  Google Scholar 

  72. 72.

    Hummel, J. et al. Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipids. Front. Plant Sci. 2, 1–17 (2011).

    Article  Google Scholar 

  73. 73.

    Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).

    CAS  Article  Google Scholar 

  74. 74.

    Guijas, C. et al. METLIN: a technology platform for identifying knowns and unknowns. Anal. Chem. 90, 3156–3164 (2018).

    CAS  Article  Google Scholar 

  75. 75.

    Haug, K. et al. MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41, D781–D786 (2013).

    CAS  Article  Google Scholar 

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Acknowledgements

We thank C. Kuhlisch from the Vardi lab for her assistance with GC–MS analyses and fruitful discussions, S. Graff van Creveld from the Vardi lab for her assistance in designing the figures for this manuscript and A. Mizrachi from the Vardi lab for her assistance with image analysis and processing. We also thank A. Brandis from the Targeted Metabolomics Unit at the Life Sciences Core Facilities, Weizmann Institute of Science, for his assistance in FAME derivatization, R. Rotkopf from the Bioinformatics Unit, Department of Biological Services, Weizmann Institute of Science, for his assistance with the statistical analysis, S. S. Lee from the Scientific Center for Optical and Electron Microscopy (ScopeM), ETH Zürich, for his assistance with epifluorescence microscopy and T. Luzzatto-Knaan from the Department of Marine Biology, University of Haifa, for her useful comments on the manuscript. This research was supported by the European Research Council CoG (VIROCELLSPHERE grant no. 681715) awarded to A.V. and by EMBO Short Term Fellowship (ASTF 601–2015) awarded to G.S.

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G.S. and A.V. conceptualized the project and conceived and designed the experiments. G.S. and A.V. wrote the manuscript. G.S. performed all experiments. N.S. developed the computational analysis of MS data. C.Z. conducted lipid extractions and the LC–MS experiments. R.A.M. and E.J.N.H. conducted the Flow-probe-MS experiments. Y.D. conducted the MALDI-MS experiments. I.R. conducted the GC–MS experiments. D.S. isolated the vesicles and virions for lipidomics analysis. All authors provided useful feedback on the experimental design and comments on the manuscript.

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Correspondence to Assaf Vardi.

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Supplementary Information

Supplementary Figures 1–17 and Supplementary Tables 1–13.

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Supplementary Data

Supplementary Data 1–3: flow-probe-MS and MS/MS analysis of mass features found in four Flowprobe MSI clusters, LC–MS and MS/MS analysis of mass features found in four Flowprobe MSI clusters, LC–MS and MS/MS analysis of additional odd lipids.

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Schleyer, G., Shahaf, N., Ziv, C. et al. In plaque-mass spectrometry imaging of a bloom-forming alga during viral infection reveals a metabolic shift towards odd-chain fatty acid lipids. Nat Microbiol 4, 527–538 (2019). https://doi.org/10.1038/s41564-018-0336-y

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