Special Issue: Microbial Endurance

Review

Why Be Temperate: Lessons from l 1,

Sylvain Gandon *

Many have evolved the ability to induce latent of their

Trends

hosts. The bacteriophage l is a classical model for exploring the regulation and

Bacteriophage l is the archetypal tem-

the evolution of latency. Here, I review recent experimental studies on phage perate phage. The study of the genetic

switch between lysis and lysogeny in l

l that identify specific conditions promoting the evolution of lysogenic life

led to a deep understanding of the

cycles. In addition, I present specific adaptations of phage l that allow this

molecular mechanisms underlying

to react plastically to variations in the environment and to reactivate its lytic gene regulation in other organisms.

life cycle. All of these different examples are discussed in the light of evolution-

An understanding of the regulation of

ary epidemiology theory to disentangle the different evolutionary forces acting

viral latency in l offers unique oppor-

on temperate phages. Understanding phage l adaptations yield important tunities to explore experimentally and

theoretically the adaptive nature of

insights into the evolution of latency in other microbes, including several life-

fixed and plastic latency in different

threatening human pathogens. environments.

Understanding the evolution of lyso-

Phage Life Cycles geny in l can help to build up a com-

prehensive framework to study the

Some pathogens exploit their host for a very limited amount of time. In humans, for instance,

evolution of latency in other infectious

most influenza virus infections last for less than 10 days [1]. The duration of the is

, including many life-threaten-

generally limited by host immunity but it can also be reduced by the death of the infected host. In

ing human pathogens.

contrast, other pathogens can remain in their host for a very long time in a latent state. Many

human have adopted this alternative life history strategy. For instance, infections by

viruses lead to short acute and usually localized infections of the skin, yielding

durable infection of nearby sensory [2]. These latent infections can later reactivate to

cause recurrent and transmission. Hence, latency can guarantee lifetime persistence of

the infection and constitutes a major therapeutic challenge in several human pathogens [3–6].

Yet, the adoption of a latent life cycle is a dramatic feature that requires specific

adaptations. When should pathogens adopt such latent life history strategies? When the

pathogens are in a latent state, when should they reactivate and attempt to reach another host?

These fundamental questions are relevant for a broad range of pathogens but are often difficult

to tackle experimentally. , however, provide a powerful model system to explore

these questions. Bacteriophages are viruses that infect bacteria and they are one of the most

abundant organisms in the biosphere [7–9]. They can adopt very different exploitation strategies

of their host, ranging from lytic to lysogenic life cycles (Figure 1, Key Figure). Lytic life cycles are

1

characterized by bacterial adsorption, replication of the phage genome, synthesis of new viral CEFE UMR 5175, CNRS – Université

de Montpellier, Université Paul-Valéry

particles, and bacterial lysis. Lysogeny, however, is characterized by the integration of the virus

Montpellier, EPHE, 1919, route de

as a in the bacterial genome and the indefinite persistence of the infection. Lysogeny

Mende, 34293 Montpellier Cedex 5,

was discovered in bacteriophages by Bail and Bordet 90 years ago [10–12]. The dichotomy France

between these two alternative lifestyles provides a fantastic opportunity to study the evolution of

latency in microbes. Bacteriophage l [13] is a temperate coliphage that has been involved in

*Correspondence:

major molecular biology advances [14]. In particular, it has been used as a classical model

[email protected]

system to unveil the regulation of lysogeny [15]. (S. Gandon).

356 Trends in , May 2016, Vol. 24, No. 5 http://dx.doi.org/10.1016/j.tim.2016.02.008

© 2016 Elsevier Ltd. All rights reserved.

Key Figure

Schematic Representation of (A) a Lytic and (B) a Lysogenic Bacterioph- age Life Cycle

(A) Free viruses Lyc life cycle

(B) Free viruses

1 – φ Lysis α φ Lysogeny Lysogenic life cycle

Prophage

Figure 1. In both cases the infection starts with the adsorption of a free virus particle to a susceptible bacterium.. In virulent

phages, such as T-even coliphages (lytic life cycle), the infection leads rapidly to lysis and to the release of free virus particles.

In temperate phages, such as bacteriophage l (lysogenic life cycle), the infection can either lead to the integration of the

prophage in the bacterial genome (with probability f) or to lysis (with probability 1– f). The prophage can either be

transmitted vertically with the replication of the lysogenic bacteria or reactivate the lysis (at a rate /) and release free virus

particles.

The regulation of lysogenisation is mainly under the control of the repressor gene cI. The

repressor protein turns off the expression of other genes of the phage but activates its own

. This prevents the expression of the lytic life cycle and allows the lysogenic bacteria

to divide and to transmit the prophage vertically. Other phage genes are also involved in this

regulation. The gene cro is an antagonist of cI, and its expression is associated with the

expression of other phage genes leading to the launch of a productive infection and the lysis

of the host. The balance between the expression of cI and cro is at the heart of the genetic switch

that allows l to control the fate (lysogeny or lysis) of its host [15]. Interestingly, genetically

identical cells infected with the same number of viruses can give rise to distinct outcomes

(Figure 1). Detailed models of within-host dynamics of the accumulation of gene products of cI

and cro have shown that this binomial variability can be explained by spontaneous biochemical

noise during the development of the infection [16,17]. Yet, this fact does not imply that fate is

exclusively stochastic. First, several mutations are known to alter the propensity for lysogenisa-

tion. For instance, the cI857 mutant has a cI repressor that decreases the probability of lysogeny,

f, and increases the induction rate, /. Second, several environmental factors are known to affect

lysogenisation and induction.

Trends in Microbiology, May 2016, Vol. 24, No. 5 357

Earlier studies of l were mainly focused on the molecular details of phage infections. Here, I want

to discuss the biology of l with an evolutionary perspective to question the adaptive nature of

specific features of its life cycle. First, I discuss the adaptive nature of fixed latency in different

environmental conditions. I review two recent experimental studies that explore the effects of (i)

epidemiological dynamics, and (ii) spatial structure on the evolution of viral latency. Second, I

discuss the ability of l to adopt plastic exploitation strategies in response to fluctuations of its

environment. I use theoretical models to explore different drivers of the evolution of pathogen

plasticity: (i) variations in the environment inside the host, and (ii) variations in the environment

outside the host. These theoretical results are discussed in the light of plastic transmission

strategies observed in l. Finally, I discuss the relevance of these studies for the evolution of both

fixed and plastic transmission strategies of other bacteriophages and other pathogens.

Latency as a Fixed Strategy

An understanding of the regulation of lysogeny in l provides invaluable information on the

evolution of latency in pathogens. When should pathogens adopt such latent life-history

strategies? To answer this question, Berngruber et al. [18,19] studied the competition between

two l variants with distinct life history strategies. The wild-type temperate l has a high

4

lysogenisation rate (f 0.5) and a low induction rate (/ 10 ). In contrast, the virulent

lcI857 is a thermosensitive mutant with a low lysogenisation rate (f < 0.1) and a high induction

3

rate (/ >10 ). Monitoring the frequency of these variants provides a measure of selection on

those life-history traits. In other words, it provides a way to determine when latency is favored

and when it is counterselected.

Evolution during Epidemics

During the early stage of a phage epidemic the density of susceptible bacteria is very large, and

free viruses have many opportunities for horizontal transmission. This promotes the spread of

phage variants with more aggressive host exploitation strategies that rapidly get access to a

larger number of hosts through the production of free virus particles. In contrast, at the end of an

epidemic, the availability of susceptible cells is reduced and the probability of finding a new host

to infect is very low. In this case, more prudent host exploitation strategies become adaptive

because they secure the durable exploitation of a host and invest in vertical transmission [20,21].

This argument is akin to the effect of host availability on optimal lysis time in lytic phages [22,23].

But the dynamic nature of the density of susceptible hosts requires models that keep track of

epidemiology and selection to understand and predict the competition between different life-

history strategies [24].

Berngruber et al. [20] formalized the effects of epidemiology on l evolution throughout epi-

demics and tested these predictions with experimental epidemics in chemostats. Each chemo-

stat was seeded with a 1:1 ratio of the temperate wild-type l and the virulent lcI857. These

experiments supported three theoretical predictions (Figure 2). First, the frequency of the virulent

mutant increases rapidly during the early phase of the epidemics and decreases later on.

Second, the evolutionary outcome is governed by the epidemiology. When the experiment is

initiated with a small number of infected cells the availability of a large number of susceptible cells

favors the virulent lcI857 in the early stage of the epidemic. In contrast, when the experiment

starts with a larger fraction of infected cells, the initial benefit of the virulent lcI857 is reduced.

Finally, the experiments confirm that the frequency of the virulence mutant is always higher in the

free virus compartment. The validation of these three qualitative predictions demonstrates

the predictive power of models integrating epidemiology and evolution, and accounting

for the specificities of l life cycle.

One key element of l biology is the ability of lysogenic bacteria to prevent the exploitation of

the host by superinfecting viruses. In other words, superinfection inhibition ensures that the

358 Trends in Microbiology, May 2016, Vol. 24, No. 5 (A) (B) Free virus Virulent/nonvirulent rao Virulent/nonvirulent rao 1 10 100 1 10 100 1000

0102030 0102030

Time (hours) Time (hours)

Figure 2. Virulence Evolution During an Epidemic in Bacteriophage l. (A) Change in the virulent/avirulent ratio in the

provirus stage. (B) Change in the virulent/avirulent ratio in the free virus stage. The initial value of the virulent/avirulent (lcI857/

l) ratio in the provirus was 1:1, and competition was started from two different initial prevalence values: 1% (red) and 100%

(blue). The lines indicate the mean over four replicate chemostats, and the envelopes show the 95% confidence intervals of

the log-transformed data. Modified from Berngruber et al. [18].

prophage controls the fate of its cell. Note, however, that lambdoid phages have evolved the

ability to escape superinfection inhibition, and several immunity groups are circulating. A

phage from a different immunity group is able to escape the repression induced by the

resident phage. This dramatically alters the prediction regarding the evolution of lysogeny

because the coexistence between different immunity groups selects for more virulent

strategies [25].

Evolution in Spatially Structured Environments

The previous section focused on the transient evolutionary dynamics taking place in a well-

mixed environment during an epidemic. Epidemics, however, often spread in spatially struc-

tured environments. Theory predicts that spatial structure generally selects for lower pathogen

virulence [26–28]. Berngruber et al. [19] extended previous models to account for localized

migration of the bacteria and localized transmission of the phage. In addition, this model allows

us to track the contribution of horizontal and vertical transmission of the two phage genotypes.

This helped to identify conditions promoting the evolution of the temperate phage. To test

these theoretical predictions, Berngruber et al. [19] manipulated the amount of mixing among

bacteria and phages and recovered two main theoretical predictions. First, low mixing

promotes the spread of the temperate phage. Indeed, in a spatially structured environment

the virulent phage rapidly runs out of susceptible cells (Figure 3). Second, the relative

contributions of horizontal and vertical transmissions are modulated by spatial structure.

When mixing is high, most of the fitness of the virulent mutant depends on horizontal

transmission. When mixing is low, vertical transmission is key because it allows perpetuation

of the infection. In other words, spatial structure changes the availability of susceptible cells

and empty sites in the local environment of an infection. The magnitude of this effect varies with

the genotype of the parasite and this is what selects for latent (i.e., more prudent) exploitation

strategies [19].

Trends in Microbiology, May 2016, Vol. 24, No. 5 359 1

0.500 λ

0.100

0.050 Fitness of virulent

0.010

0.005

No Low Intermediate High

Mixing

Figure 3. Mixing Selects for Virulence in Bacteriophage l. A 1:1 mixture of temperate l and its virulent mutant lcI857

was used to seed spatial epidemics. Spatial structure was disturbed to different degrees (no mixing, low mixing,

intermediate mixing, and high mixing) and transferred onto a fresh biofilm of susceptible hosts for 3 consecutive days.

Disturbance of the epidemic structure has a strong effect on the competitive fitness of the virulent lcI857. The undisturbed

environment strongly selects against virulence whereas virulence is beneficial in a well-mixed environment. As expected,

intermediate levels of mixing lead to intermediate fitness of lcI857. Modified from Berngruber et al. [19].

Latency as a Plastic Strategy

In the above section, latency is assumed to be a fixed strategy. Bacteriophage l, however, is

also known to exhibit plastic life-history strategies. For instance, the efficacy of lysogenisation

and the induction rate of the prophage can vary with the environment. These conditional

strategies are well studied on a molecular level but the adaptive nature of this plasticity is often

overlooked.

Escaping a Stressful Environment

One of the best studied examples of plasticity is perhaps the effect of stress on the induction

rate, /, of bacteriophage l. Whenever a lysogen undergoes DNA damage due to UV irradiation

or other chemical stress, the protein RecA is activated. RecA is involved in the inactivation

(cleavage) of the LexA repressor and in the launch of the SOS response, activating different

mechanisms of DNA repair. Interestingly, RecA is also involved in the inactivation (cleavage) of

the cI repressor and thus in the launch of the of l. Is this conditional induction rate

adaptive? One way to answer this question is to show that such plasticity may be adaptive under

some theoretical scenario. In Box 1 I developed a simple model where the infected host can be in

two different states. The host can be in a ‘normal’ state in which host cells replicate and have low

mortality, but with a rate s host cells may enter a ‘stressful’ state where both fecundity and

survival are reduced. The model shows that the virus infecting a stressed cell should shift

towards a more intensive exploitation strategy. In other words, this simple model provides an

adaptive explanation for the conditional induction rate of l because the activation of RecA

signals that the host is in a critical state.

Lysogenisation or Not in a Variable Environment

The probability to lysogenize, f, is another life history trait of l modulated by variations in the

environment. In particular, this probability increases when the number of virus particles

360 Trends in Microbiology, May 2016, Vol. 24, No. 5

Box 1. Leaving a Sinking Ship

Let us assume a simple epidemiological model where the host can fall into two different states: (i) a ‘normal’ host with high

fecundity, l, and low mortality, d, and (ii) a ‘stressed’ host with low fecundity, l*, and high mortality, d*. We can model the

dynamics of phage l under these assumptions which yields the following epidemiological model (the dot notation refers

to differentiation with respect to time):

_

L ¼ lL þ faSVðd þ a þ sÞL

_

L ¼ lL þ sLðd þ aÞL

_

V ¼ BðaL þ aLÞ þ Bð1fÞaSVmV

where L and L* are the densities of normal and stressed hosts respectively, V is the density of free viruses, and S is the

density of susceptible hosts. The parameters l, f, d, a, m, a, s and B refer to the fecundity of lysogens, the lysogenisation

rate, the natural death rate of lysogens, the adsorbtion rate of the virus, the death rate of free virus, efficiency of the

induction rate of lysogens, the rate at which a bacterium enters the stressed state, and the burst size of a productive

infection (see Figure 1 in main text). This simple model can be used to explore the effect of host stress on the evolution of

the parasite exploitation strategy. To study the evolution of conditional lysis strategies, we focus on the ability of a mutant

m m

pathogen (with a strategy a and a ) to invade a resident pathogen population (with a strategy a and a*) at equilibrium. In

^

other words, we assume that the density S of susceptible hosts is fixed by the resident strategy of the pathogen. The

m

dynamics of the mutant can be fully described by the matrix F which accounts for how many mutants are created in the

m

two different types of host and in the environment (as free-living viruses) and the matrix V which refers to transition

between these three compartments: 0 1

^

l 0 aSf Fm ¼ @ A

0 l 0

^

00 0 aBSð1fÞ 1

m

d þ a þ s 0 0

m @ m A

V ¼ s d þ a 0

m m

a B a B m

The per-generation growth rate of the mutant is given by the dominant eigenvalue Rm of the next generation matrix [49]: 0 1

tm tm tm

L ! L L ! L V ! L

Fm:V m1 ¼ @ tm tm 0 A

L ! L L ! L

tm tm tm

L ! V L ! V V ! V

m

where t is the per-generation transition between compartment X and Y. For the sake of simplicity, we focus on the

X ! Y

special case where l* = 0. We derive the gradient of selection on conditional lysis:

@Rm ^

/ aBSfðdðd þ aÞ þ dsÞ mðd þ aÞl

@am |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}

benefit of lysis cost of lysis

@Rm ^

/ s fd > m aBS 0

@a

In other words, lysis can evolve towards intermediate rates in normal hosts to balance the benefit associated with access

to new hosts and the cost of losing the current host. In stressed hosts, however, lysis is maximized because there is no

benefit associated with delaying lysis in a sterilized and moribund host.

attempting to infect a host cell is high [29,30]. Some of the molecular details governing this

regulation have been largely uncovered and modeled [16,17,31]. Yet, the adaptive nature of this

regulation remains unclear. Why should pathogens coinfecting the same host adopt a latent

strategy? Classical virulence evolution theory, in contrast, often predicts that coinfections should

yield more aggressive exploitation strategies [21]. Such conditional lysogenisation strategy,

however, may be adaptive in some environments. Avlund et al. [32] argue that it may evolve as a

response to the stochasticity of the environment. Indeed, conditional in plants and

other organisms has often been viewed as a bet-hedging strategy that evolved in random

environments [33,34]. Yet, I want to argue here that conditional latency may also be adaptive in

fully deterministic but temporally variable environments. Indeed, the fact that several viruses are

attempting to enter the same cell indicates that very few hosts are available and, in this case, it

may be advantageous to take care of the host. Lysogenisation may thus be a way to stay in the

host and avoid the perils of the free-living stage of the virus life cycle. Box 2 is an attempt to

formalize the evolution of plastic transmission strategies in a variable environment. Theory shows

that plasticity may evolve when it allows transmission to coincide with a transitory increase in the

availability of susceptible cells. But the model does not specify which cues can be used by the

virus to trigger a life-history switch. The difficulty for the virus is to use local information obtained

Trends in Microbiology, May 2016, Vol. 24, No. 5 361

Box 2. Plastic Transmission Strategies in Variable Environments Outstanding Questions

uð Þ fl

Fluctuation in the quality of the environment is modeled with t which describes the periodic in ux of susceptible cells in The details of the genetic switch in l

the population. The epidemiological model introduced in Box 1 becomes: have been unveiled. Is it also possible

_ to disentangle the molecular mecha-

S ¼ uðtÞaSVdS

_ nisms underlying the regulation of

L ¼ lL þ faSVðd þ aÞL

_ latency in other pathogens?

V ¼ BaL þ Bð1fÞaSVmV

Under the assumption that the dynamics of the free virus compartment is much faster than the other processes, we can

What is the amount of natural variation in

assume that the density of free viruses is always at this equilibrium:

the propensity to develop fixed or plastic

^ BaL latency among pathogens? Is it possible

V ¼

fi

mB ð1fÞaS to relate this variation to speci c features

of their local environment?

For the sake of simplicity, we focus on the case where f = 1. The dynamics of a mutant virus may thus be described by:

 

_ faBSam How do stochastic and deterministic

Lm ¼ l þ ðd þ amÞ Lm

variations in the environment interact

|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}m

with the evolution of latency?

rm

If the system oscillates periodically, the mutant will invade if its average growth rate over one period T (noted with a tilde) is

Is it possible to demonstrate experi-

positive:

mentally the adaptive nature of plastic

Z  

T f ~a~ f latency in the laboratory?

~ 1 ~ aBS m aB ~

rm ¼ rmdt ¼ l þ d þ a~mÞ þ cov S; a~mÞ > 0

T 0 m m

Do different pathogen species share

The covariance in the above condition captures what may select for plasticity [43]. A strategy building up a covariance similar environmental cues that trigger

between the timing of lysis and the high density of susceptible hosts is going to be selected because it synchronizes the relapses?

release of free virus with the availability of susceptible hosts. Multiplicity of infection may be used by the virus to build up

this covariation because it carries valuable information on the epidemiological state of the population. Note that, in Is it possible to develop new drugs that

‘ ’

contrast to Avlund et al. [32], phages do not play dice in the above model because the dynamics is fully deterministic. prevent latency and facilitate within-

fl

They do not evolve plastic transmission strategies because of stochasticity but because of the periodic uctuation of the host eradication?

environment.

within an infected host to infer the epidemiological state of the population (i.e., outside the

infected host). The number of coinfecting viruses carries valuable information on the epide-

miological state of the population, and this is why it may be a good cue to evolve conditional

lysogenisation. But other cues may also be used to modulate lysogenisation. In particular,

host starvation is known to increase the probability of lysogenisation [29]. Starvation is a very

good indicator of the environmental state of the population because it signals that very little

bacterial growth is feasible. In a variable environment, where starvation oscillates, it is adaptive

for the virus to hide in infected cells (lysogenisation) during a famine to avoid the risks

associated with the free-living stage. The above argument does not account for the potential

benefits on host fitness associated with the integration of the phage in the host genome. For

instance, Chen et al. [35] showed that lysogenisation with l may carry benefits for the

Escherichia coli bacteria because it lowers the growth rate of lysogens in energy-poor

environments. The potential symbiotic nature of lysogenisation in some environments clearly

disserves further investigation [36].

Concluding Remarks

The discovery and the characterization of bacteriophage l has led to major discoveries in

molecular biology [14]. In particular, the regulation of the lysis–lysogeny decision remains a

very fruitful topic that stimulated a broad range of research studies [26,37]. The molecular

details of the genetic switch of l have been scrutinized for decades and led to a deep

understanding of gene regulation that shed light on many other biological regulatory pro-

cesses. The lysis–lysogeny decision has clear-cut implications on the virus life cycle and offers

unique opportunities to study the ecology and evolution of virus latency. Here, I reviewed a

few recent studies and discussed the adaptive nature of both constitutive and conditional

latency. Competition between different l strains (with different life-history strategies) has

allowed the study of which environmental factors promote the evolution of fixed lysogenisation

362 Trends in Microbiology, May 2016, Vol. 24, No. 5

strategies. To explore the adaptive nature of conditional lysogenisation I developed new

models of life-history evolution in fixed and variable environments. Evolutionary epidemiology

theory provides a way to illustrate how l developed various adaptations to cope with variable

environmental conditions. I do not claim that every feature of the biology of l is well adapted.

As any organism, l evolves under a set of evolutionary constraints. Evolutionary epidemiol-

ogy, however, provides a fruitful framework for trying to make sense of some peculiar features

of the l life cycle. As such, it may also help to understand the evolution of latency in other

phages and other pathogens.

From l to Other Bacteriophages

It is important to remember that while lysogenisation is the classical way to generate latent

infections, alternative strategies are used by lytic phages. For instance, lysis inhibition is the

ability to delay the timing of lysis in T4 phages, and this inhibition is triggered by multiplicity of

infection [38]. It is tempting to think that the same forces have led to the evolution of the

regulation of lysogenisation in l and lysis inhibition in T4. In both cases, multiplicity of

infection carries information about the epidemiological state of the population that may be

used by the virus to better time the induction of a productive infection. Similarly, pseudo-

lysogeny provides an alternative life-history strategy that enables the virus to delay lysis in

starved hosts [39]. Pseudolysogeny is distinct from lysogeny because the phage genome

does not integrate into the host chromosome. The effect of starvation, however, recalls the

increased lysogenisation of l in starved cells. In both cases, conditional latency is likely to be

adaptive because it allows the virus to avoid the risks associated with the free-living stage of

the life cycle.

From Bacteriophages to Other Pathogens

It is also tempting to use our understanding of the evolution of latency in l to study the evolution

of both constitutive and plastic latency in other pathogens. We explored the effect of epidemiol-

ogy and spatial structure on l evolution, but other environmental factors may trigger higher

investment in latency. In malaria, for instance, it is clear that the seasonality of the environment

has dramatic effects on the availability of mosquito vectors and thus on epidemiological

dynamics. Plasmodium vivax is one of the main causative agents of malaria in humans, and

this pathogen has the ability to remain dormant for several months before relapsing and causing

new infections. The rate at which P. vivax relapses is variable among different pathogen isolates

and has been found to depend on latitude [40,41]. In particular, the relapsing rate is much lower

(i.e., latency is more pronounced) at high latitude where seasonality implies that transmission is

hindered by the lack of mosquito vectors in winter. This pattern is consistent with classical

theoretical predictions on the evolution of latency [33,34,42–44].

The analysis of the l life cycle indicates that different types of cue may trigger the relapse of

latent infections. First, relapses may be induced by cues indicating that the environment inside

the host has changed and is unlikely to sustain long-lasting infections (Box 1). Many infections

by herpes simplex viruses and have been shown to reactivate when the

host is immune-depressed or physiologically stressed [2]. For instance, asymptomatic reacti-

vation of varicella zoster virus has been observed in astronauts after a mission in space [45]. Of

course, a drop in the efficacy of host immunity may provide a proximal cause for the replication

of the virus leading to a relapse. Yet, the ultimate cause of the relapse may be related to the

evolved ability of the virus to react actively to variations in the physiological state of its host.

Second, relapses may also be induced by cues indicating that the environment outside the host

has changed and is becoming suitable (Box 2). Higher multiplicity of infection carries informa-

tion regarding the epidemiological state of the population. Interestingly, malaria parasites,

similar to bacteriophages, have plastic transmission strategies with regard to the multiplicity of

infection [46]. Again, it is unclear whether this pattern is adaptive or not, but given the

Trends in Microbiology, May 2016, Vol. 24, No. 5 363

tremendous amount of plasticity in Plasmodium parasites [43,47,48] this evolutionary per-

spective is worth investigating.

Bacteriophages live in complex environments and they have evolved elaborate strategies to

cope with the variability of their habitat. In particular, bacteriophage l has the ability to adopt

constitutive and conditional latency. The study of evolutionary forces leading to these fascinating

adaptations give insight into the diversity of other pathogens’ life-history strategies (see Out-

standing Questions). Because the dormant stages of many human pathogens is challenging the

efficacy of classical therapeutic interventions [3–6], a better understanding of the ultimate causes

of these pathogen life histories may thus inspire new strategies to control and eradicate those

infectious diseases.

Acknowledgments

This work benefited a lot from discussions with Thomas Berngruber, Marc Choisy, Troy Day, Bryan Grenfell, Quentin

Legros, Bruce Levin, Sébastien Lion, Ana Rivero, and Joshua Weitz.

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