A thermal trade-off between viral production and degradation drives virus-phytoplankton population dynamics.

Marine viruses interact with microbial hosts in dynamic environments shaped by variation in abiotic factors, including temperature. However, the impacts of temperature on viral infection of phytoplankton are not well understood. Here we coupled mathematical modelling with experiments to explore the effect of temperature on virus-phytoplankton interactions. Our model shows the negative consequences of high temperatures on infection and suggests a temperature-dependent threshold between viral production and degradation. Modelling long-term dynamics in environments with different average temperatures revealed the potential for long-term host-virus coexistence, epidemic free or habitat loss states. We generalised our model to variation in global sea surface temperatures corresponding to present and future seas and show that climate change may differentially influence virus-host dynamics depending on the virus-host pair. Temperature-dependent changes in the infectivity of virus particles may lead to shifts in virus-host habitats in warmer oceans, analogous to projected changes in the habitats of macro-, microorganisms and pathogens.

Unveiling membrane thermoregulation strategies in marine picocyanobacteria.

The wide latitudinal distribution of marine Synechococcus cyanobacteria partly relies on the differentiation of lineages adapted to distinct thermal environments. Membranes are highly thermosensitive cell components, and the ability to modulate their fluidity can be critical for the fitness of an ecotype in a particular thermal niche. We compared the thermophysiology of Synechococcus strains representative of major temperature ecotypes in the field. We measured growth, photosynthetic capacities and membrane lipidome variations. We carried out a metagenomic analysis of stations of the Tara Oceans expedition to describe the latitudinal distribution of the lipid desaturase genes in the oceans. All strains maintained efficient photosynthetic capacities over their different temperature growth ranges. Subpolar and cold temperate strains showed enhanced capacities for lipid monodesaturation at low temperature thanks to an additional, poorly regiospecific Δ9-desaturase. By contrast, tropical and warm temperate strains displayed moderate monodesaturation capacities but high proportions of double unsaturations in response to cold, thanks to regiospecific Δ12-desaturases. The desaturase genes displayed specific distributions directly related to latitudinal variations in ocean surface temperature. This study highlights the critical importance of membrane fluidity modulation by desaturases in the adaptive strategies of Synechococcus cyanobacteria during the colonization of novel thermal niches.

Accounting for cellular-level variation in lysis: implications for virus-host dynamics.

Viral impacts on microbial populations depend on interaction phenotypes-including viral traits spanning the adsorption rate, latent period, and burst size. The latent period is a key viral trait in lytic infections. Defined as the time from viral adsorption to viral progeny release, the latent period of bacteriophage is conventionally inferred via one-step growth curves in which the accumulation of free virus is measured over time in a population of infected cells. Developed more than 80 years ago, one-step growth curves do not account for cellular-level variability in the timing of lysis, potentially biasing inference of viral traits. Here, we use nonlinear dynamical models to understand how individual-level variation of the latent period impacts virus-host dynamics. Our modeling approach shows that inference of the latent period via one-step growth curves is systematically biased-generating estimates of shorter latent periods than the underlying population-level mean. The bias arises because variability in lysis timing at the cellular level leads to a fraction of early burst events, which are interpreted, artefactually, as an earlier mean time of viral release. We develop a computational framework to estimate latent period variability from joint measurements of host and free virus populations. Our computational framework recovers both the mean and variance of the latent period within simulated infections including realistic measurement noise. This work suggests that reframing the latent period as a distribution to account for variability in the population will improve the study of viral traits and their role in shaping microbial populations.IMPORTANCEQuantifying viral traits-including the adsorption rate, burst size, and latent period-is critical to characterize viral infection dynamics and develop predictive models of viral impacts across scales from cells to ecosystems. Here, we revisit the gold standard of viral trait estimation-the one-step growth curve-to assess the extent to which assumptions at the core of viral infection dynamics lead to ongoing and systematic biases in inferences of viral traits. We show that latent period estimates obtained via one-step growth curves systematically underestimate the mean latent period and, in turn, overestimate the rate of viral killing at population scales. By explicitly incorporating trait variability into a dynamical inference framework that leverages both virus and host time series, we provide a practical route to improve estimates of the mean and variance of viral traits across diverse virus-microbe systems.

Emergence and maintenance of stable coexistence during a long-term multicellular evolution experiment.

The evolution of multicellular life spurred evolutionary radiations, fundamentally changing many of Earth's ecosystems. Yet little is known about how early steps in the evolution of multicellularity affect eco-evolutionary dynamics. Through long-term experimental evolution, we observed niche partitioning and the adaptive divergence of two specialized lineages from a single multicellular ancestor. Over 715 daily transfers, snowflake yeast were subjected to selection for rapid growth, followed by selection favouring larger group size. Small and large cluster-forming lineages evolved from a monomorphic ancestor, coexisting for over ~4,300 generations, specializing on divergent aspects of a trade-off between growth rate and survival. Through modelling and experimentation, we demonstrate that coexistence is maintained by a trade-off between organismal size and competitiveness for dissolved oxygen. Taken together, this work shows how the evolution of a new level of biological individuality can rapidly drive adaptive diversification and the expansion of a nascent multicellular niche, one of the most historically impactful emergent properties of this evolutionary transition.

Disentangling top-down drivers of mortality underlying diel population dynamics 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 in this environment function as the predominant top-down factor despite high standing viral particle densities. The model-data fits also reveal the ecological relevance of light-dependent viral traits and non-canonical factors to cellular loss. Finally, we leverage sensitivity analyses to demonstrate how variation in life history traits across distinct oceanic contexts, including variation in viral adsorption and grazer clearance rates, can transform the quantitative and even qualitative importance of top-down controls in shaping Prochlorococcus population dynamics.

Emergence and maintenance of stable coexistence during a long-term multicellular evolution experiment.

The evolution of multicellular life spurred evolutionary radiations, fundamentally changing many of Earth’s ecosystems. Yet little is known about how early steps in the evolution of multicellularity transform eco-evolutionary dynamics, e.g., via niche expansion processes that may facilitate coexistence. Using long-term experimental evolution in the snowflake yeast model system, we show that the evolution of multicellularity drove niche partitioning and the adaptive divergence of two distinct, specialized lineages from a single multicellular ancestor. Over 715 daily transfers, snowflake yeast were subject to selection for rapid growth in rich media, followed by selection favoring larger group size. Both small and large cluster-forming lineages evolved from a monomorphic ancestor, coexisting for over ~4,300 generations. These small and large sized snowflake yeast lineages specialized on divergent aspects of a trade-off between growth rate and survival, mirroring predictions from ecological theory. Through modeling and experimentation, we demonstrate that coexistence is maintained by a trade-off between organismal size and competitiveness for dissolved oxygen. Taken together, this work shows how the evolution of a new level of biological individuality can rapidly drive adaptive diversification and the expansion of a nascent multicellular niche, one of the most historically-impactful emergent properties of this evolutionary transition.

Global Phylogeography of Marine Synechococcus in Coastal Areas Reveals Strong Community Shifts.

Marine Synechococcus comprise a numerically and ecologically prominent phytoplankton group, playing a major role in both carbon cycling and trophic networks in all oceanic regions except in the polar oceans. Despite their high abundance in coastal areas, our knowledge of Synechococcus communities in these environments is based on only a few local studies. Here, we use the global metagenome data set of the Ocean Sampling Day (June 21st, 2014) to get a snapshot of the taxonomic composition of coastal Synechococcus communities worldwide, by recruitment on a reference database of 141 picocyanobacterial genomes, representative of the whole Prochlorococcus, Synechococcus, and Cyanobium diversity. This allowed us to unravel drastic community shifts over small to medium scale gradients of environmental factors, in particular along European coasts. The combined analysis of the phylogeography of natural populations and the thermophysiological characterization of eight strains, representative of the four major Synechococcus lineages (clades I to IV), also brought novel insights about the differential niche partitioning of clades I and IV, which most often co-dominate the Synechococcus community in cold and temperate coastal areas. Altogether, this study reveals several important characteristics and specificities of the coastal communities of Synechococcus worldwide. IMPORTANCE Synechococcus is the second most abundant phytoplanktonic organism on Earth, and its wide genetic diversity allowed it to colonize all the oceans except for polar waters, with different clades colonizing distinct oceanic niches. In recent years, the use of global metagenomics data sets has greatly improved our knowledge of "who is where" by describing the distribution of Synechococcus clades or ecotypes in the open ocean. However, little is known about the global distribution of Synechococcus ecotypes in coastal areas, where Synechococcus is often the dominant phytoplanktonic organism. Here, we leverage the global Ocean Sampling Day metagenomics data set to describe Synechococcus community composition in coastal areas worldwide, revealing striking community shifts, in particular along the coasts of Europe. As temperature appears as an important driver of the community composition, we also characterize the thermal preferenda of 8 Synechococcus strains, bringing new insights into the adaptation to temperature of the dominant Synechococcus clades.

Modeling serological testing to inform relaxation of social distancing for COVID-19 control.

Serological testing remains a passive component of the public health response to the COVID-19 pandemic. Using a transmission model, we examine how serological testing could have enabled seropositive individuals to increase their relative levels of social interaction while offsetting transmission risks. We simulate widespread serological testing in New York City, South Florida, and Washington Puget Sound and assume seropositive individuals partially restore their social contacts. Compared to no intervention, our model suggests that widespread serological testing starting in late 2020 would have averted approximately 3300 deaths in New York City, 1400 deaths in South Florida and 11,000 deaths in Washington State by June 2021. In all sites, serological testing blunted subsequent waves of transmission. Findings demonstrate the potential benefit of widespread serological testing, had it been implemented in the pre-vaccine era, and remain relevant now amid the potential for emergence of new variants.

Linking Light-Dependent Life History Traits with Population Dynamics for Prochlorococcus and Cyanophage.

Prochlorococcus cyanobacteria grow in diurnal rhythms driven by diel cycles. Their ecology depends on light, nutrients, and top-down mortality processes, including lysis by viruses. Cyanophage, viruses that infect cyanobacteria, are also impacted by light. For example, the extracellular viability and intracellular infection kinetics of some cyanophage vary between light and dark conditions. Nonetheless, it remains unclear whether light-dependent viral life history traits scale up to influence population-level dynamics. Here, we examined the impact of diel forcing on both cellular- and population-scale dynamics in multiple Prochlorococcus-phage systems. To do so, we developed a light-driven population model, including both cellular growth and viral infection dynamics. We then tested the model against measurements of experimental infection dynamics with diel forcing to examine the extent to which population level changes in both viral and host abundances could be explained by light-dependent life history traits. Model-data integration reveals that light-dependent adsorption can improve fits to population dynamics for some virus-host pairs. However, light-dependent variation alone does not fully explain realized host and virus population dynamics. Instead, we show evidence consistent with lysis saturation at relatively high virus-to-cell ratios. Altogether, our study represents a quantitative approach to integrate mechanistic models to reconcile Prochlorococcus-virus dynamics spanning cellular-to-population scales.IMPORTANCE The cyanobacterium Prochlorococcus is an essential member of global ocean ecosystems. Light rhythms drive Prochlorococcus photosynthesis, ecology, and interactions with potentially lethal viruses. At present, the impact of light on Prochlorococcus-virus interactions is not well understood. Here, we analyzed Prochlorococcus and virus population dynamics with a light-driven population model and compared our results with experimental data. Our approach revealed that light profoundly drives both cellular- and population-level dynamics for some host-virus systems. However, we also found that additional mechanisms, including lysis saturation, are required to explain observed host-virus dynamics at the population scale. This study provides the basis for future work to understand the intertwined fates of Prochlorococcus and associated viruses in the surface ocean.

Modeling serological testing to inform relaxation of social distancing for COVID-19 control.

Serological testing remains a passive component of the current public health response to the COVID-19 pandemic. Using a transmission model, we examined how serology can be implemented to allow seropositive individuals to increase levels of social interaction while offsetting transmission risks. We simulated the use of widespread serological testing in three metropolitan areas with different initial outbreak timing and severity characteristics: New York City, South Florida, and Washington Puget Sound. In our model, we use realistic serological assay characteristics, in which tested seropositive individuals partially restore their social contacts and act as immunological 'shields'. Compared to a scenario with no intervention, beginning a mass serological testing program on November 1, 2020 was predicted to avert 15,000 deaths (28% reduction, 95% CrI: 0.4%-30.2%) in New York City, 3,000 (31.1% reduction, 95% CrI: 26.4%-33.3%) in South Florida and 10,000 (60.3% reduction, 95% CrI: 50.2%-60.7%) in Washington State by June 2021. In all three sites, widespread serological testing substantially blunted new waves of transmission. Serological testing has the potential to mitigate the impacts of the COVID-19 pandemic while also allowing a substantial number of individuals to safely return to social interactions and economic activity.

Intervention Serology and Interaction Substitution: Modeling the Role of 'Shield Immunity' in Reducing COVID-19 Epidemic Spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 690,000 confirmed cases and more than 33,000 confirmed deaths globally as of March 30, 2020 [1-4]. At present two central public health control strategies have emerged: mitigation and suppression (e.g, [5]). Both strategies focus on reducing new infections by reducing interactions (and both raise questions of sustainability and long-term tactics). Complementary to those approaches, here we develop and analyze an epidemiological intervention model that leverages serological tests [6, 7] to identify and deploy recovered individuals as focal points for sustaining safer interactions via interaction substitution, i.e., to develop what we term 'shield immunity' at the population scale. Recovered individuals, in the present context, represent those who have developed protective, antibodies to SARS-CoV-2 and are no longer shedding virus [8]. The objective of a shield immunity strategy is to help sustain the interactions necessary for the functioning of essential goods and services (including but not limited to tending to the elderly [9], hospital care, schools, and food supply) while decreasing the probability of transmission during such essential interactions. We show that a shield immunity approach may significantly reduce the length and reduce the overall burden of an outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well recognized roles in estimating prevalence [10, 11] and in the potential development of plasma-based therapies [12-15].

Modeling shield immunity to reduce COVID-19 epidemic spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 20201-4. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies5. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests6,7 to identify and deploy recovered individuals8 as focal points for sustaining safer interactions via interaction substitution, developing what we term 'shield immunity' at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services9 while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence10,11 and in the potential development of plasma-based therapies12-15.

Picoeukaryotes of the Micromonas genus: sentinels of a warming ocean.

Photosynthetic picoeukaryotesx in the genus Micromonas show among the widest latitudinal distributions on Earth, experiencing large thermal gradients from poles to tropics. Micromonas comprises at least four different species often found in sympatry. While such ubiquity might suggest a wide thermal niche, the temperature response of the different strains is still unexplored, leaving many questions as for their ecological success over such diverse ecosystems. Using combined experiments and theory, we characterize the thermal response of eleven Micromonas strains belonging to four species. We demonstrate that the variety of specific responses to temperature in the Micromonas genus makes this environmental factor an ideal marker to describe its global distribution and diversity. We then propose a diversity model for the genus Micromonas, which proves to be representative of the whole phytoplankton diversity. This prominent primary producer is therefore a sentinel organism of phytoplankton diversity at the global scale. We use the diversity within Micromonas to anticipate the potential impact of global warming on oceanic phytoplankton. We develop a dynamic, adaptive model and run forecast simulations, exploring a range of adaptation time scales, to probe the likely responses to climate change. Results stress how biodiversity erosion depends on the ability of organisms to adapt rapidly to temperature increase.

How do microalgae perceive light in a high-rate pond? Towards more realistic Lagrangian experiments.

Hydrodynamics in a high-rate production reactor for microalgae cultivation affects the light history perceived by cells. The interplay between cell movement and medium turbidity leads to a complex light pattern, whose forcing effects on photosynthesis and photoacclimation dynamics are non-trivial. Hydrodynamics of high density algal ponds mixed by a paddle wheel has been studied recently, although the focus has never been on describing its impact on photosynthetic growth efficiency. In this multidisciplinary downscaling study, we first reconstructed single cell trajectories in an open raceway using an original hydrodynamical model offering a powerful discretization of the Navier-Stokes equations tailored to systems with free surfaces. The trajectory of a particular cell was selected and the associated high-frequency light pattern was computed. This light pattern was then experimentally reproduced in an Arduino-driven computer controlled cultivation system with a low density Dunaliella salina culture. The effect on growth and pigment content was recorded for various frequencies of the light pattern, by setting different paddle wheel velocities. Results show that the frequency of this realistic signal plays a decisive role in the dynamics of photosynthesis, thus revealing an unexpected photosynthetic response compared to that recorded under the on/off signals usually used in the literature. Indeed, the light received by a single cell contains signals from low to high frequencies that nonlinearly interact with the photosynthesis process and differentially stimulate the various time scales associated with photoacclimation and energy dissipation. This study highlights the need for experiments with more realistic light stimuli to better understand microalgal growth at high cell densities. An experimental protocol is also proposed, with simple, yet more realistic, step functions for light fluctuations.

Temperature is a key factor in Micromonas-virus interactions.

The genus Micromonas comprises phytoplankton that show among the widest latitudinal distributions on Earth, and members of this genus are recurrently infected by prasinoviruses in contrasted thermal ecosystems. In this study, we assessed how temperature influences the interplay between the main genetic clades of this prominent microalga and their viruses. The growth of three Micromonas strains (Mic-A, Mic-B, Mic-C) and the stability of their respective lytic viruses (MicV-A, MicV-B, MicV-C) were measured over a thermal range of 4-32.5 °C. Similar growth temperature optima (Topt) were predicted for all three hosts but Mic-B exhibited a broader thermal tolerance than Mic-A and Mic-C, suggesting distinct thermoacclimation strategies. Similarly, the MicV-C virus displayed a remarkable thermal stability compared with MicV-A and MicV-B. Despite these divergences, infection dynamics showed that temperatures below Topt lengthened lytic cycle kinetics and reduced viral yield and, notably, that infection at temperatures above Topt did not usually result in cell lysis. Two mechanisms operated depending on the temperature and the biological system. Hosts either prevented the production of viral progeny or maintained their ability to produce virions with no apparent cell lysis, pointing to a possible switch in the viral life strategy. Hence, temperature changes critically affect the outcome of Micromonas infection and have implications for ocean biogeochemistry and evolution.