Biology subjects

Casey, J. R.

Publications and source records attributed to Casey, J. R..

3 recordsLinked to original sources

bioRxiv2024

Spatially structured competition and cooperation alters algal carbon flow to bacteria

Microbial communities regulate the transformations of carbon in aquatic systems through metabolic interactions and food-web dynamics that can alter the balance of photosynthesis and respiration. Direct competition for resources is thought to drive microbial community assembly in algal systems, but other interaction modes that may shape communities are more challenging to isolate. Through untargeted metabolomics and metabolic modeling, we predicted the degree of resource competition between bacterial pairs when growing on model diatom Phaeodactylum tricornutum-derived substrates. In a subsequent sequential media experiment, we found that pairwise interactions were consistently more cooperative than predicted based on resource competition alone, indicating an unexpected role for cooperation in algal carbon processing. To link this directly to algal carbon fate, we chose a representative cooperative and competitive influencer isolate and a model recipient and applied single-cell isotope tracing in a custom porous microplate cultivation system. In the presence of live algae, the recipient drew down more algal carbon in the presence of the cooperative influencer compared to the competitive influencer, supporting the sequential experiment results. We also found that total carbon assimilation into bacterial biomass, integrated over influencer and recipient, was significantly higher for the cooperative interaction. Our findings support the notion that non-competitive interactions are critical for predicting algal carbon fate. Significance StatementMicrobial interactions have widely been studied in the context of host resources but testing and measuring direct interactions in a lab has been particularly challenging. By combining untargeted metabolomics, sequential/(co-)culture, and metabolic modeling, we demonstrate that the presence of an unexpected interaction mode in a live system and show how it impacts the flow of host-derived resources. This top-down approach can help identify novel bacterial interactions that play a crucial role in microbial community-host ecosystems, which may have an impact in holobiont phenotypes including alga, fungal, or plant systems.

microbiology↗

bioRxiv2021

Diel population dynamics and mortality of Prochlorococcus in the North Pacific Subtropical Gyre

Photosynthesis fuels primary production at the base of marine food webs. Yet, in many surface ocean ecosystems, diel-driven primary production is tightly coupled to daily loss. This tight coupling raises the question: which top-down drivers predominate in maintaining persistently stable picocyanobacterial populations over longer time scales? Motivated by high-frequency surface water measurements taken in the North Pacific Subtropical Gyre (NPSG), we developed multitrophic models to investigate bottom-up and top-down mechanisms underlying the balanced control of Prochlorococcus populations. We find that incorporating photosynthetic growth with viral- and predator-induced mortality is sufficient to recapitulate daily oscillations of Prochlorococcus abundances with baseline community abundances. In doing so, we infer that grazers function as the primary top-down factor despite high standing viral particle densities while identifying the potential for light-dependent viral traits and non-canonical loss factors to shape the structure and function of marine microbial communities.

ecology↗

bioRxiv2020

Community-scale Synchronization and Temporal Partitioning of Gene Expression, Metabolism, and Lipid Biosynthesis in Oligotrophic Ocean Surface Waters

Sunlight drives daily rhythms of photosynthesis, growth, and division of photoautotrophs throughout the surface oceans. However, the cascading impacts of oscillatory light input on diverse microbial communities and community-scale metabolism remains unclear. Here we use an unsupervised machine learning approach to show that a small number of diel archetypes can explain pervasive periodic dynamics amongst more than 65,000 distinct time series, including transcriptional activity, macromolecules, lipids, and metabolites from the North Pacific Subtropical Gyre. Overall, we find evidence for synchronous timing of carbon-cycle gene expression that underlie daily oscillations in the concentrations of particulate organic carbon. In contrast, we find evidence of asynchronous timing in gene transcription related to nitrogen metabolism and related metabolic processes consistent with temporal niche partitioning amongst microorganisms in the bacterial and eukaryotic domains.

ecology↗