Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in the ocean. Their growth, demise, and interactions with other microorganisms drive the fluxes of biogeochemical elements through marine ecosystems. Mixotrophic protists are capable of both photosynthesis and ingestion of prey and are dominant components of open-ocean planktonic communities. Yet the role of mixotrophs in elemental cycling is obscured by their capacity to act as primary producers or heterotrophic consumers depending on factors that remain largely uncharacterized. Here, we develop and apply a machine learning model that predicts the in situ trophic mode of aquatic protists based on their patterns of gene expression. This approach leverages a public collection of protist transcriptomes as a training set to identify a subset of gene families whose transcriptional profiles predict trophic mode. We applied our model to nearly 100 metatranscriptomes obtained during two oceanographic cruises in the North Pacific and found community-level and population-specific evidence that abundant open-ocean mixotrophic populations shift their predominant mode of nutrient and carbon acquisition in response to natural gradients in nutrient supply and sea surface temperature. Metatranscriptomic data from ship-board incubation experiments revealed that abundant mixotrophic prymnesiophytes from the oligotrophic North Pacific subtropical gyre rapidly remodeled their transcriptome to enhance photosynthesis when supplied with limiting nutrients. Coupling this approach with experiments designed to reveal the mechanisms driving mixotroph physiology provides an avenue toward understanding the ecology of mixotrophy in the natural environment.

Iron (Fe) is an important growth factor for diatoms and its availability is further restricted by changes in the carbonate chemistry of seawater. We investigated the physiological attributes and transcriptional profiles of the diatom Thalassiosira pseudonana grown on a day: night cycle under different CO2/pH and iron concentrations, that in combination generated available iron (Fe’) concentrations of 1160, 233, 58 and 12 pM. We found the light-dark conditions to be the main driver of transcriptional patterns, followed by Fe’ concentration and CO2 availability, respectively. At the highest Fe’ (1160 pM), 55% of the transcribed genes were differentially expressed between day and night, whereas at the lowest Fe’ (12 pM), only 28% of the transcribed genes displayed comparable patterns. While Fe limitation disrupts the diel expression patterns for genes in most central metabolism pathways, the diel expression of light- signaling molecules and glycolytic genes was relatively robust in response to reduced Fe’. Moreover, we identified a non-canonical splicing of transcripts encoding triose-phosphate isomerase, a key-enzyme of glycolysis, generating transcript isoforms that would encode proteins with and without an active site. Transcripts that encoded an active enzyme maintained a diel expression at low Fe’, while transcripts that encoded the non-active enzyme lost the diel expression. This work illustrates the interplay between nutrient limitation and transcriptional regulation over the diel cycle. Considering that future ocean conditions will reduce the availability of Fe in many parts of the oceans, our work identifies some of the regulatory mechanisms that may shape future ecological communities.

Intricate networks of single-celled eukaryotes (protists) dominate carbon flow in the ocean. Their growth, demise, and interactions with other microorganisms drive the fluxes of biogeochemical elements through marine ecosystems. Mixotrophic protists are capable of both photosynthesis and ingestion of prey and are dominant components of open-ocean planktonic communities. Yet, the role of mixotrophs in elemental cycling is obscured by their capacity to act as primary producers or heterotrophic consumers depending on factors that remain largely uncharacterized. Here we introduce a machine learning model that can predict the in situ nutritional mode of aquatic protists based on their patterns of gene expression. This approach leverages a public collection of protist transcriptomes as a training set to identify a subset of gene families whose transcriptional profiles predict trophic status. We applied our model to nearly 100 metatranscriptomes obtained during two oceanographic cruises in the North Pacific and found community-level and population-specific evidence that abundant open-ocean mixotrophic populations shift their predominant mode of nutrient and carbon acquisition in response to natural gradients in nutrient supply and sea surface temperature. In addition, metatranscriptomic data from ship-board incubation experiments revealed that abundant mixotrophic prymnesiophytes from the oligotrophic North Pacific subtropical gyre rapidly remodelled their transcriptome to enhance photosynthesis when supplied with limiting nutrients. Coupling the technique introduced here with experiments designed to reveal the mechanisms driving mixotroph physiology is a promising approach for understanding the ecology of mixotrophic populations in the natural environment.Significance statementMixotrophy is a ubiquitous nutritional strategy in marine ecosystems. Although our understanding of the distribution and abundance of mixotrophic plankton has improved significantly, the functional roles of mixotrophs are difficult to pinpoint, as mixotroph nutritional strategies are flexible and form a continuum between heterotrophy and phototrophy. We employ a machine learning-driven metatranscriptomic technique to assess the nutritional strategies of abundant planktonic populations in situ and demonstrate that mixotrophic populations play varying functional roles along physico-chemical gradients in the North Pacific Ocean, revealing a degree of physiological plasticity unique to aquatic mixotrophs. Our results highlight mechanisms that may dictate the flow of biogeochemical elements and the ecology of the North Pacific Ocean, one of the largest biogeographical provinces on Earth.

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Phytoplankton and bacteria form the base of marine ecosystems and their interactions drive global biogeochemical cycles. The effects of bacteria and bacteria-produced compounds on diatoms range from synergistic to pathogenic and can affect the physiology and transcriptional patterns of the interacting diatom. Here, we investigate physiological and transcriptional changes in the marine diatom Thalassiosira pseudonana induced by extracellular metabolites of a known antagonistic bacterium Croceibacter atlanticus. Mono-cultures of C. atlanticus released compounds that inhibited diatom cell division and elicited a distinctive morphology of enlarged cells with increased chloroplast content and enlarged nuclei, similar to what was previously observed when the diatom was co-cultured with live bacteria. The extracellular C. atlanticus metabolites induced transcriptional changes in diatom pathways that include recognition and signaling pathways, cell cycle regulation, carbohydrate and amino acid production, as well as cell wall stability. Phenotypic analysis showed a disruption in the diatom cell cycle progression and an increase in both intra- and extracellular carbohydrates in diatom cultures after bacterial exudate treatment. The transcriptional changes and corresponding phenotypes suggest that extracellular bacterial metabolites, produced independently of direct bacterial-diatom interaction, may modulate diatom metabolism in ways that support bacterial growth.