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Diatom primary productivity is tightly coupled with carbon export through the ballasted nature of the silica-based cell wall, linking the oceanic silicon and carbon cycles. However, despite low productivity, iron (Fe)-limited regimes are considered ‘hot spots’ of diatom silica burial with enhanced carbon export efficiency, raising questions about the mechanisms driving the biogeochemistry of these regions. Marine viruses are classically recognized as catalysts of remineralization through host lysis, short-circuiting the trophic transfer of carbon and facilitating the retention of dissolved organic matter and associated elements in the surface ocean. Here we used metatranscriptomic analysis of diatoms and associated viruses, along with a suite of physiological and geochemical metrics, to study the interaction between diatoms and viruses in Fe-limited regimes of the northeast Pacific. We found low cell-associated diatom virus diversity and abundance in a chronically Fe-limited region of the subarctic northeast Pacific. In a coastal upwelling region of the California Current, transient iron limitation also substantially reduced viral replication. These observations were recapitulated in Fe-limited cultures of the bloom-forming, centric diatom, Chaetoceros tenuissimus, which exhibited delayed virus-mediated mortality in addition to reduced viral replication. We suggest Fe-limited diatoms escape viral lysis and subsequent remineralization in the surface ocean, providing an additional mechanism contributing to enhanced carbon export efficiency and silica burial in Fe-limited oceanic regimes. Diatoms are less susceptible to viral infection in iron-limited oceans, according to metatranscriptomic analyses of diatoms and viruses in nutrient-replete and limited regions.

Diatoms are among the most globally distributed and ecologically successful organisms in the modern ocean, contributing upwards of 40% of total marine primary productivity1,2. By converting dissolved silicon into biogenic silica, and photosynthetically fixing carbon dioxide into particulate organic carbon, diatoms effectively couple the silicon (Si) and carbon cycles and ballast substantial vertical flux of carbon out of the euphotic zone into the mesopelagic and deep ocean3–5. Viruses are key players in ocean biogeochemical cycles6,7, yet little is known about how viral infection specifically impacts diatom populations. Here, we show that Si limitation facilitates virus infection and mortality in diatoms in the highly productive coastal waters of the California Current Ecosystem. Using metatranscriptomic analysis of cell-associated diatom viruses and targeted quantification of extracellular viruses, we found a link between Si stress and the early, active and lytic stages of viral infection. This relationship was also observed in cultures of the bloom-forming diatom Chaetoceros tenuissimus, where Si stress accelerated virus-induced mortality. Together, these findings contextualize viruses within the ecophysiological framework of Si availability and diatom-mediated biogeochemical cycling. Using metatranscriptomics of diatom-associated viruses and quantification of extracellular viruses in coastal water samples, the authors link silicon limitation to increased virus-induced mortality of diatoms, which could have implications for marine biogeochemical cycling.

Sorting the suffixes of a string into lexicographical order is a fundamental task in a number of contexts, most notably lossless compression (Burrows--Wheeler transformation) and text indexing (suffix arrays). Most approaches to suffix sorting produce a sorted array of suffixes directly, continually moving suffixes into their final place in the array until the ordering is complete. In this article, we describe a novel and resource-efficient (time and memory) approach to suffix sorting, which works in a complementary way—by assigning each suffix its rank in the final ordering, before converting to a sorted array, if necessary, once all suffixes are ranked. We layer several powerful extensions on this basic idea and show experimentally that our approach is superior to other leading algorithms in a variety of real-world contexts.