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Nat Microbiol. 2019 Mar;4(3):527-538. doi: 10.1038/s41564-018-0336-y. Epub 2019 Feb 4.

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

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Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel.
Department of Postharvest Science of Fresh Produce, Institute of Postharvest and Food Sciences, The Volcani Center, Bet Dagan, Israel.
Institute of Microbiology, ETH Zurich, Zurich, Switzerland.
Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel.
Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel.


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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