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

Abstract

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.

Keywords: cyanobacteria; cyanophage; diurnal rhythm; light-dark cycle; modeling; virus.

FIG 1

Cyanophage infection in the light or the dark. (a) Viral life history trait definitions: viral adsorption (encounter and adsorption on a noninfected host, in ml h⁻¹), latent period (time between adsorption and lysis of the host cell, in hours), and burst size (new phages produced per one lysed host cell). (b) Host-cyanophage pairs used in the study. (c) Infection under light or dark (data from reference ; see Materials and Methods). Cyanophage P-HM2 and P-SSP7 were used to infect their host cells under continuous light or in the dark. For all the host-phage pairs, the phage/host ratio is 0.1. Extracellular phage concentrations were measured as phage DNA by quantitative PCR and normalized to the value at time zero.

FIG 2

Modeling Prochlorococcus MED4 strain as function of light without viruses during the exponential phase. (a) Fit of the host dynamic (equation 1). Solid lines represent the median of 5,000 model simulations, and shaded areas are the 95% quantiles. Black dots are data (from reference 45) for two replicates, and gray shaded area represents the dark condition. (b) Model growth parameter distributions of the host model (equations 1 and 4). Parameter distribution estimated using an MCMC algorithm: photosynthesis-irradiance (PI)-curve slope of the linear phase α, optimal growth light L_opt, maximal growth rate μ_max, minimum amount of light necessary to divide K_L, and natural mortality ω. (c) Model growth functions that drive the host dynamic: growth is expressed as the net growth rate (μ_opt − ω) as a function of irradiance (equation 4; left) and as a function of time (equation 2; right).

FIG 3

Description of the model. (a) Schematic representation of the model. The host population is divided into 3 classes: susceptible (S), exposed (E), and infected (I) types. The virus particle density is denoted by V. Black arrows are biological processes. (b) Definitions of the hypotheses. Each hypothesis describes a possible relation between light and the infection parameters. When parameter ϕ, β, or λ is light dependent, it is a piecewise function, i.e., it takes one value in light and one value in dark.

FIG 4

Light-driven models fit to host and virus population abundance data. Model fits under H0 and hypotheses H2_ϕλ and H1_ϕ for an inoculation time of 14.5 h after the beginning of the experiment. Phage P-HM2 infecting strain MED4 (left) and P-SSP7 infecting MED4 (right). Solid lines represent the median values of 5,000 model simulations, with shaded areas the 95% quantiles. Data are represented by the black dots for two replicates. Vertical shaded gray lines represent dark conditions.

FIG 5

Viral dynamics under light-dark cycle for different viral inoculation times. Model fits under H0 and best hypotheses $\widetilde{H0}$ or $\widetilde{H{1}_{\varphi }}$ for different viral inoculation times. Phage P-HM2 infecting strain MED4 (left) and P-SSP7 infecting MED4 (right). Solid lines represent the median values of 5,000 model simulations, with shaded areas the 95% quantiles. Data are represented by the black dots for two replicates. Vertical shaded gray lines represent dark conditions.

FIG 6

Model infection parameter distributions: P-HM2 (left) and P-SSP7 (right) infecting strain MED4. Distributions are calculated with 5,000 parameter sets. Distributions are colored according to the model hypothesis: H0 (gray), $\widetilde{H{1}_{\varphi }}$ (shades of blue), and $\widetilde{H0}$ (pink). For the P-HM2/MED4 hypothesis $\widetilde{H{1}_{\varphi }}$, ϕ is light dependent, while λ and β are not (Constant; light blue). The light-dependent ϕ can take two values: one during light (Light; medium blue) and one during dark (Dark; dark blue).