Biology subjects

Coenen, A. R.

Publications and source records attributed to Coenen, A. R..

4 recordsLinked to original sources

bioRxiv2025

Emergent higher-order interactions enable coexistence in phage-bacteria community dynamics

Diverse phage-bacteria communities coexist at high densities in environmental, agricultural, and human-associated microbiomes. Phage-bacteria coexistence is often attributed to coevolutionary processes mediated by complex, pairwise infection networks. Here, using in vitro experiments and mathematical models, we explore how higher-order interactions function as a complementary, ecological feedback mechanism to stabilize phage-bacteria communities. To do so, we examine an environmentally-derived, synthetic phage-bacteria community comprised of five marine heterotrophic bacteria (Cellulophaga baltica and Pseudoalteromonas strains) and five associated phage. We used Bayesian inference to reconstruct free phage production in one-step growth experiments and then forecasted pairwise phage-bacteria community dynamics over multiple infection cycles. In contrast to model predictions of rapid bacterial population collapse, each bacterial strain persisted in the community. We hypothesized and then experimentally validated the relevance of infection attenuation at relatively high viral densities. We extended models into a community context, corroborating complex coexistence of all phage and bacteria. Life history traits inferred in community fits often differed from those inferred in a pairwise context, implicating higher-order interactions as an additional, ecological stabilization mechanism. Follow-up experiments confirm that phage traits (including burst size) can shift when infecting single vs. multiple strains. More broadly, these findings suggest that complex community coexistence of phage and bacteria may be more common than anticipated when including feedback mechanisms outside of the growth-dominated regimes of fitted pairwise models that do not reflect the full scope of ecologically relevant contexts.

ecology↗

bioRxiv2025

Seasonal Enhancement of the Viral Shunt Catalyzes a Subsurface Oxygen Maximum in the Sargasso Sea

Subsurface oxygen maxima (SOMs) occur directly beneath the mixed layer of stratified water columns across oligotrophic open ocean basins. SOMs occur seasonally and are hypothesized to result from elevated microbial net primary productivity (NPP). Here, we set out to identify mechanistic drivers of the SOM near the Bermuda Atlantic Time Series (BATS) site in the Sargasso Sea in October 2019. Coupled time-series analysis of metatranscriptomics, flow cytometry, and family-specific cyanophage quantification revealed elevated Prochlorococcus abundances, cyanophage abundances, and cyanophage-specific transcriptional activity in the SOM. These findings spurred us to analyze historical oxygen saturation profiles at BATS - identifying a repeated, seasonal cycle in SOM emergence associated with elevated virus-like particles and Prochlorococcus numbers. Returning to the 2019 study site, we found that transcriptional markers for increased dissolved organic matter uptake by copiotrophic bacteria were enriched in the SOM, consistent with enhanced catabolic activity due to the viral shunt. In addition, Prochlorococcus exhibited enrichment in ammonium transport transcripts at the SOM, consistent with increased responsiveness to remineralization activity by heterotrophs. Altogether, these findings suggest that enhanced viral lysis leads to locally elevated nutrient recycling and oxygen production, further reinforcing hypotheses that viruses may play a critical role in the emergence of SOMs in the oligotrophic ocean.

ecology↗

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↗

bioRxiv2017

Correlations, interactions, and predictability in virus-microbe networks

1Microbes are present in high abundances in the environment and in human-associated microbiomes, often exceeding one million per milliliter. Viruses of microbes are present in even higher abundances and are important in shaping microbial populations, communities, and ecosystems. Given the relative specificity of viral infection, it is essential to identify the functional linkages between viruses and their microbial hosts, particularly given dynamic changes in virus and host abundances. Multiple approaches have been proposed to infer infection networks from time-series of in situ communities, among which correlation-based approaches have emerged as the de facto standard. In this work, we evaluate the accuracy of correlation-based inference methods using an in silico approach. In doing so, we compare predicted networks to actual networks to assess the self-consistency of correlation-based inference. At odds with assumptions underlying its widespread use, we find that correlation is a poor predictor of interactions in the context of viral infection and lysis of microbial hosts. The failure to predict interactions holds for methods which leverage product-moment, time-lagged, and relative-abundance based correlations. In closing, we discuss alternative inference methods, particularly model-based methods, as a means to infer interactions in complex microbial communities with viruses.\n\n2 ImportanceInferring interactions from population time-series is an active and ongoing area of research. It is relevant across many biological systems - in particular in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference - using correlations to predict interactions - is widespread. However, it is well known that \"correlation does not imply causation\". Despite this, many studies apply correlation-based inference methods to experimental time-series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions within in silica virus-microbe communities, raising questions on their relevance when applied in situ.

ecology↗