bioRxivMicrobiologyPreprint
Sub-daily Bermuda Atlantic Time Series virus sampling reveals taxonomy, host, and functional differences at the population, but not community level
Alfonso CarrilloiD, Emily Hageman, Lauren ChittickiD, Anna I. MackeyiD, Kimberley S NdlovuiD, Funing TianiD, Naomi E. GilbertiD, Daniel MuratoreiD, Dean R. VikiD, Gary R. LeCleiriD, Christine L SuniD, Hee-Young JangiD, Ricardo R. PavaniD, Joshua S. WeitziD, Steven W. WilhelmiD, Matthew B. SullivaniD
Abstract
Ocean microbes contribute to biogeochemical cycles and ecosystem function, but they do so under top-down pressure imposed by viruses. While viruses are increasingly understood spatially and beginning to be incorporated into predictive modeling, high-frequency ocean virus dynamics remain understudied due to methodological challenges. Here we sampled stratified Bermuda Atlantic Time Series (BATS) waters for 112 hours at sub-daily 4-(surface) or 12-(deep chlorophyll maximum) hour intervals, purified viral particles from these samples, sequenced their metagenomes, and used the resulting data to characterize high-frequency virus community dynamics. Aggregated community diversity metrics changed with depth, but were not statistically significant temporally at a fixed location. However, finer-scale population-level analyses revealed both depth and temporal change, including physicochemical depth-driven differences and, in surface waters, thousands of viral populations that exhibited statistically significant diel rhythms. Statistical analyses revealed three main archetypes of temporal dynamics that themselves differed in abundance patterns, host predictions, viral taxonomy, and gene functions. Among these, highlights include viruses resembling an archetype with a night peaking pattern in activity that include an over-representation of viruses that putatively infect Prochlorococcus, a phototrophic cyanobacteria. Together, these efforts provide baseline community-and population-scale short-time-frame observations relevant to future climate state modeling.
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