Proteomic traits vary across taxa in a coastal Antarctic phytoplankton bloom

Abstract Production and use of proteins is under strong selection in microbes, but it is unclear how proteome-level traits relate to ecological strategies. We identified and quantified proteomic traits of eukaryotic microbes and bacteria through an Antarctic phytoplankton bloom using in situ metapro...

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Bibliographic Details
Published in:The ISME Journal
Main Authors: McCain, J. Scott P., Allen, Andrew E., Bertrand, Erin M.
Other Authors: Simons Foundation, Killam Trusts, National Science Foundation
Format: Article in Journal/Newspaper
Language:English
Published: Springer Science and Business Media LLC 2021
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Online Access:http://dx.doi.org/10.1038/s41396-021-01084-9
https://www.nature.com/articles/s41396-021-01084-9.pdf
https://www.nature.com/articles/s41396-021-01084-9
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Summary:Abstract Production and use of proteins is under strong selection in microbes, but it is unclear how proteome-level traits relate to ecological strategies. We identified and quantified proteomic traits of eukaryotic microbes and bacteria through an Antarctic phytoplankton bloom using in situ metaproteomics. Different taxa, rather than different environmental conditions, formed distinct clusters based on their ribosomal and photosynthetic proteomic proportions, and we propose that these characteristics relate to ecological differences. We defined and used a proteomic proxy for regulatory cost, which showed that SAR11 had the lowest regulatory cost of any taxa we observed at our summertime Southern Ocean study site. Haptophytes had lower regulatory cost than diatoms, which may underpin haptophyte-to-diatom bloom progression in the Ross Sea. We were able to make these proteomic trait inferences by assessing various sources of bias in metaproteomics, providing practical recommendations for researchers in the field. We have quantified several proteomic traits (ribosomal and photosynthetic proteomic proportions, regulatory cost) in eukaryotic and bacterial taxa, which can then be incorporated into trait-based models of microbial communities that reflect resource allocation strategies.