Decision Making by Temperate Phages

Golding, Ido; Coleman, Seth; Nguyen, Thu V.P.; Yao, Tianyou

doi:10.1016/b978-0-12-809633-8.20969-2

Cited by 16 publications

(19 citation statements)

References 12 publications

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“…Consecutive panels show an optimal strategy dynamics resulting from the optimal response functions to arbitrium quorum sensing concentration represented in Fig. 3a given a time horizon (12,18,24,36 and 48 hours, respectively) We see a common strategy of pure lysis followed by a stochastic strategy in all cases. The time of the switch from obligate lysis to a stochastic strategy can be deduced by the time at which the production of lysogens start.…”

Section: Effect Of Resource Level On Optimal Strategymentioning

confidence: 98%

“…Hence, when phage were relatively more abundant than bacterial hosts, phage infections tended to lead to lysogeny, rather than lysis. Recent work using single-cell imaging methods revealed that the probability of lysogeny increases with increasing cellular multiplicity of infection (from 20% given a single phage to 80% given five coinfecting phage) [28,12]. These shifts in cellular fate reflect a combination of interactions between phage-specific gene regulatory circuits and the ecological context (which drives the multiplicity of infection).…”

Section: Introductionmentioning

confidence: 99%

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Time-scales modulate optimal lysis-lysogeny decision switches and near-term phage fitness

Shivam

Lucía-Sanz

et al. 2021

Preprint

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Temperate phage can initiate lysis or lysogeny after infecting a bacterial host. The genetic switch between lysis and lysogeny is mediated by phage regulatory genes as well as host and environmental factors. Recently, a new class of decision switches was identified in phage of the SPbeta group, mediated by the extracellular release of small, phage-encoded peptides termed arbitrium. Arbitrium peptides can be taken up by bacteria prior to infection, modulating the decision switch in the event of a subsequent phage infection. Increasing concentration of arbitrium increases the chance that a phage infection will lead to lysogeny, rather than lysis. Although prior work has centered on the molecular mechanisms of arbitrium-induced switching, here we focus on how selective pressures impact the benefits of plasticity in switching responses. In this work, we examine the possible advantages of near-term adaptation of communication-based decision switches used by the SPbeta-like group. We combine a nonlinear population model with a control theoretic approach to evaluate the relationship between a putative phage reaction norm (i.e., the probability of lysogeny as a function of arbitrium) and the near-term time horizon. We show the adaptive potential of communication-based lysis-lysogeny responses and find that optimal switching between lysis to lysogeny increases near-term fitness compared to fixed responses. We further find that plastic responses are robust to the inclusion of cellular-level stochasticity. These findings provide a principled basis to explore the long-term evolution of phage-encoded decision systems mediated by extracellular decision-signaling molecules, and the feedback between phage reaction norms and ecological context.

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Section: Effect Of Resource Level On Optimal Strategymentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Time-scales modulate optimal lysis-lysogeny decision switches and near-term phage fitness

Shivam

Lucía-Sanz

et al. 2021

Preprint

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“…The COI model assumed that two phage infections occurring within the commitment time were necessary for lysogeny. This assumption was based on the observation that most temperate phages seem to encode a repressor system that is functionally similar to lambda's cro/cI (33,34). This includes phages in marine environments, such as temperate phages infecting SAR11, suggesting that lessons learned from lambda can be extended to the marine environment (7,52,53).…”

Section: Marine Gutmentioning

confidence: 99%

“…The response of lysogeny to coinfection seems to represent a widespread strategy among temperate phage populations. Most temperate phages encode a repressor system functionally similar to lambda's cro/cI (34). For example, phage P22, which displays only 13% genomic similarity with lambda, encodes the same repressor system (35).…”

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confidence: 99%

Quantification of Lysogeny Caused by Phage Coinfections in Microbial Communities from Biophysical Principles

Luque

Silveira

2020

mSystems

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Temperate phages can associate with their bacterial host to form a lysogen, often modifying the phenotype of the host. Lysogens are dominant in the microbially dense environment of the mammalian gut. This observation contrasts with the long-standing hypothesis of lysogeny being favored at low microbial densities, such as in oligotrophic marine environments. Here, we hypothesized that phage coinfections—a well-understood molecular mechanism of lysogenization—increase at high microbial abundances. To test this hypothesis, we developed a biophysical model of coinfection for marine and gut microbiomes. The model stochastically sampled ranges of phage and bacterial concentrations, adsorption rates, lysogenic commitment times, and community diversity from each environment. In 90% of the sampled marine communities, less than 10% of the bacteria were predicted to be lysogenized via coinfection. In contrast, 25% of the sampled gut communities displayed more than 25% of lysogenization. The probability of lysogenization in the gut was a consequence of the higher densities and higher adsorption rates. These results suggest that, on average, coinfections can form two trillion lysogens in the human gut every day. In marine microbiomes, which were characterized by lower densities and phage adsorption rates, lysogeny via coinfection was still possible for communities with long lysogenic commitment times. Our study indicates that different physical factors causing coinfections can reconcile the traditional view of lysogeny at poor host growth (long commitment times) and the recent Piggyback-the-Winner framework proposing that lysogeny is favored in rich environments (high densities and adsorption rates). IMPORTANCE The association of temperate phages and bacterial hosts during lysogeny manipulates microbial dynamics from the oceans to the human gut. Lysogeny is well studied in laboratory models, but its environmental drivers remain unclear. Here, we quantified the probability of lysogenization caused by phage coinfections, a well-known trigger of lysogeny, in marine and gut microbial environments. Coinfections were quantified by developing a biophysical model that incorporated the traits of viral and bacterial communities. Lysogenization via coinfection was more frequent in highly productive environments like the gut, due to higher microbial densities and higher phage adsorption rates. At low cell densities, lysogenization occurred in bacteria with long duplication times. These results bridge the molecular understanding of lysogeny with the ecology of complex microbial communities.

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“…In fact, it is not even clear to what extent HIV latency is an evolved property of the pathogen. Phage encodes several genes that coordinate its lysis-lysogeny decision, latency maintenance, and the eventual reversal to the lytic cycle (Golding et al, 2019). While the exact mechanisms of latency establishment in HIV remain to be elucidated, so far no viral factors strongly controlling this process have been found (Siliciano and Greene, 2011;Romani and Allahbakhshi, 2017).…”

Section: The Evolution Of Viral Latencymentioning

confidence: 99%

Microbial Evolution at Multiple Scales

Doekes¹

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Consider a population of parent entities, each of which is characterised by some characteristic ( = 1, 2, … , ). This characteristic might be a continuous phenotypic value of the entities, such as a bacterium's investment in public good production or a pathogen's virulence, an indicator variable that is 1 if the entity displays a certain behaviour (e.g., altruism) and 0 if it does not, or a measure of allelic dosage. Let = 1 ∑ =1 denote the mean value of in the population.2 If public-good producers and cheaters are initially mixed at a 1:1 ratio and the inoculum size is large, the initial frequency of public-good producing cells is very similar for all cultures, i.e., Φ ≈ 0.5 for all cultures . The between-culture variation is then small, and the within-culture variation is large (within-culture variation is maximal if Φ = 0.5). If the inoculum size is small, by chance populations are more often initialised with either a large or small frequency of public-good producers. This increases the between-culture variation and decreases within-culture variation.12 Chapter 1. IntroductionSo far, we have focused on the effects of spatial structure on microbial evolution. Next, we will turn our attention to another form of multilevel structure.3 0 is defined as the number of new infections caused by an infected individual in an otherwise susceptible host population. Multilevel pathogen evolution1 13 the set-point viral load: the relatively stable concentration of viral particles in the blood during chronic infection (Fraser et al., 2007). 14Chapter 1. Introduction a A model of within-host dynamics is nested through functional relationships into a between-host epidemiological model Time Strain frequency Replication rate Replication rate Transmission rate Time to deathtransmission death b c Figure 1.3. Nested dynamical model of pathogen evolution. (a) A mechanistic or population genetic model is used to describe within-host dynamics of different pathogen strains. (b)The within-host model is nested in a between-host model by defining functional relationships between the within-host pathogen population and epidemiological characteristics. In the example shown here, the replication rate of the dominant pathogen strain affects the transmission rate and life expectancy of the host. These curves are based on observations of the relationship between the set-point viral load of an HIV-infection and infectiousness and time until the onset of AIDS (Fraser et al., 2007(Fraser et al., , 2014. (c) The epidemiological model describes the population dynamics of hosts and between-host transmission.

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Decision Making by Temperate Phages

Cited by 16 publications

References 12 publications

Time-scales modulate optimal lysis-lysogeny decision switches and near-term phage fitness

Time-scales modulate optimal lysis-lysogeny decision switches and near-term phage fitness

Quantification of Lysogeny Caused by Phage Coinfections in Microbial Communities from Biophysical Principles

Microbial Evolution at Multiple Scales

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