The dynamic trophic architecture of open-ocean protist communities revealed through machine-guided metatranscriptomics

Abstract

AbstractIntricate 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 thein situnutritional 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 populationsin situand 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.