example of red queen hypothesis

What Is the Red Queen Hypothesis?

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Evolution is the changing in species over time. However, with the way ecosystems work on Earth, many species have a close and important relationship with each other to ensure their survival. These symbiotic relationships, such as the predator-prey relationship, keep the biosphere running correctly and keep species from going extinct. This means as one species evolves, it will affect the other species in some way. This coevolution of the species is like an evolutionary arms race that insists that the other species in the relationship must also evolve to survive.

The “Red Queen” hypothesis in evolution is related to the coevolution of species. It states that species must continuously adapt and evolve to pass on genes to the next generation and also to keep from going extinct when other species within a symbiotic relationship are evolving. First proposed in 1973 by Leigh Van Valen, this part of the hypothesis is especially important in a predator-prey relationship or a parasitic relationship.

Predator and Prey

Food sources are arguably one of the most important types of relationships in regards to survival of a species. For instance, if a prey species evolves to become faster over a period of time, the predator needs to adapt and evolve to keep using the prey as a reliable food source. Otherwise, the now faster prey will escape, and the predator will lose a food source and potentially go extinct. However, if the predator becomes faster itself, or evolves in another way like becoming stealthier or a better hunter, then the relationship can continue, and the predators will survive. According to the Red Queen hypothesis, this back and forth coevolution of the species is a constant change with smaller adaptations accumulating over long periods of time.

Sexual Selection

Another part of the Red Queen hypothesis has to do with sexual selection. It relates to the first part of the hypothesis as a mechanism to speed up evolution with the desirable traits. Species that are capable of choosing a mate rather than undergoing asexual reproduction or not having the ability to select a partner can identify characteristics in that partner that are desirable and will produce the more fit offspring for the environment. Hopefully, this mixing of desirable traits will lead to the offspring being chosen through natural selection and the species will continue. This is a particularly helpful mechanism for one species in a symbiotic relationship if the other species cannot undergo sexual selection.

Host and Parasite

An example of this type of interaction would be a host and parasite relationship. Individuals wanting to mate in an area with an abundance of parasitic relationships may be on the lookout for a mate that seems to be immune to the parasite. Since most parasites are asexual or not able to undergo sexual selection, then the species that can choose an immune mate has an evolutionary advantage. The goal would be to produce offspring that have the trait that makes them immune to the parasite. This would make the offspring more fit for the environment and more likely to live long enough to reproduce themselves and pass down the genes.

This hypothesis does not mean that the parasite in this example would not be able to coevolve. There are more ways to accumulate adaptations than just sexual selection of partners. DNA mutations can also produce a change in the gene pool only by chance. All organisms regardless of their reproduction style can have mutations happen at any time. This allows all species, even parasites, to coevolve as the other species in their symbiotic relationships also evolve.

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Red Queen hypothesis

The idea that, in order for a species to maintain a particular niche in an ecosystem and its fitness relative to other species, that species must be constantly undergoing adaptive evolution because the organisms with which it is  coevolving  are themselves undergoing adaptive evolution. When species evolve in accordance with the Red Queen hypothesis, it often results in an evolutionary  arms race .

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Red Queen Hypothesis

The Red Queen Hypothesis, named after the Red Queen in the book Alice in Wonderland, brings together two evolutionary theories.

This article is a part of the guide:

• Law Of Segregation
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The basis for the entire theory is down to ‘the evolutionary arms race’, where prey and predator constantly evolve together to reach some sort of uneasy balance.

An example of the Red Queen Hypothesis might be one of the plants that evolve toxins to kill off predators such as caterpillars .

If the plant, under predation selection pressure, evolved a new type of toxin to which the caterpillar had no immunity, most of the caterpillars would die off and the tree would flourish. This victory would be short lived, as only the caterpillars immune to the toxin, even if only a tiny percentage, would breed rapidly, and once again the tree would be under attack.

Sexual Reproduction and Genetics

For the Red Queen Hypothesis to happen, some sort of genetic mixing of genes must happen, such as sexual reproduction, as this throws up enough random fluctuations and mutations to allow new traits to appear. Most random mutations will disappear, as they confer no advantage or may even be detrimental.

Occasionally, they will give an advantage and will quickly spread through a population, as individuals with this genotype will have a competitive advantage. The industrial melanism shown by the peppered moth is an excellent example of this process in action. Without this mutation there would be a chance that the species may have become extinct.

If its population had shrunk, through predation or disease, to a small size, the species would have been open to environmental factors wiping it out.

Genetic fluctuations rely on probability and numbers. A large population is much more able, by chance, to throw up random mutations, whilst a small population is less likely.

Other Selection Pressures

Predator/prey relationships are not the only factors in the Red Queen Hypothesis.

If many species are competing for the same resources, mutations are sometimes needed to prevent a species from being out-competed. This is possibly one of the reasons why sexual reproduction occurs in higher species. If no random mixing occurred, then a bacteria or parasite may quickly evolve into a lethal form which would wipe out a species.

Sexual reproduction means that in a large population, there would be enough individuals with resistance to breed, pass the trait on and continue the species.

In a strange way, this benefits both host and parasite because, if a parasite or bacteria was so effective that it killed the host species, then it too is guaranteed extinction.

This process of sexual selection may explain why the vast majority of genes in vertebrates are dormant and do nothing (often called ‘junk DNA’) as they are preserving possible mutations that might suddenly be needed in the future if the environment or parasite pressure changes.

Sickle Cell Anemia

Often, these genes can even be detrimental. In humanity, sickle cell anemia is a gene that causes the red blood cells of an individual to become sickle shaped and less able to carry oxygen.

If both of an individual’s parents pass on the gene, the individual will have the full blown disease and, without medical intervention, probably die. Logically, it would be expected that this trait should die out of a population due to natural selection.

However, another factor has to be thrown in to the mix, malaria, where a parasite enters the blood stream through a mosquito bite. This parasite cannot live in blood affected by the sickle cell disease.

All of a sudden, the demographic changes; individuals with no copies of the gene are likely to suffer malaria, those with two copies anemia.

Only the individuals with one copy of the gene will be unaffected, as they do not have enough cells affected to cause the anemic effects, but enough to deter the malaria parasite, the cost being that some of their offspring will suffer anemia or malaria susceptibility.

In a final support to the Red Queen Hypothesis , this mutation has occurred independently, at least four times in human history, with the same gene involved, indicating that this gene must have been preserved for some reason and parasitic pressure caused it to be manifested.

Species, whilst improving and evolving to be more successful must face some sort of pressure from parasites or predators in order to evolve, with sexual reproduction being one of the main factors. Whole ecosystems and food chains are kept in check by this evolutionary ‘arms race’ as described by the Red Queen Hypothesis.

Sexual reproduction also acts as a safeguard against extinction. If a natural disaster or epidemic almost wipes out a species, a large population is genetically diverse enough to allow some individuals to survive and the species can once again prosper.

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Martyn Shuttleworth (Jan 1, 2008). Red Queen Hypothesis. Retrieved Sep 17, 2024 from Explorable.com: https://explorable.com/red-queen-hypothesis

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• v.14(5); 2018 May

Getting somewhere with the Red Queen: chasing a biologically modern definition of the hypothesis

Luke c. strotz.

1 Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS 66045, USA

2 Biodiversity Institute, University of Kansas, Lawrence, KS 66045, USA

Marianna Simões

Matthew g. girard, laura breitkreuz, julien kimmig, bruce s. lieberman, associated data.

This article has no additional data.

The Red Queen hypothesis (RQH) is both familiar and murky, with a scope and range that has broadened beyond its original focus. Although originally developed in the palaeontological arena, it now encompasses many evolutionary theories that champion biotic interactions as significant mechanisms for evolutionary change. As such it de-emphasizes the important role of abiotic drivers in evolution, even though such a role is frequently posited to be pivotal. Concomitant with this shift in focus, several studies challenged the validity of the RQH and downplayed its propriety. Herein, we examine in detail the assumptions that underpin the RQH in the hopes of furthering conceptual understanding and promoting appropriate application of the hypothesis. We identify issues and inconsistencies with the assumptions of the RQH, and propose a redefinition where the Red Queen's reign is restricted to certain types of biotic interactions and evolutionary patterns occurring at the population level.

1. ‘Down the rabbit hole’ 1 : introduction

The Red Queen hypothesis (RQH) was first proposed by Van Valen [ 1 ] to explain a pattern he argued was manifest in the fossil record involving component members of several major taxonomic groups: survivorship curves that were linear when plotted against geologic time. The RQH contains several additional elements Van Valen [ 1 ] derived from this pattern. First, in any taxonomic group that occupies the same adaptive landscape, the probability of survival is independent of age throughout its existence. Then, Van Valen [ 1 ] took this interpretation one step further and concluded that all members of such groups had an equal probability of extinction. This aspect of the RQH he termed the ‘Law of Constant Extinction’ which was held to be applicable across different organizational (e.g. population, community), and taxonomic (e.g. species, genera, families) levels. Finally, Van Valen [ 1 ] suggested that the RQH involved omnipresent competitive interactions among taxonomic groups; these were continually changing, but they were not getting relatively better in a competitive sense through time such that there was a zero-sum expectation ( figure 1 ). Instead, they were metaphorically running in place and not getting anywhere: like the eponymous Red Queen from Lewis Carroll's ‘Through the Looking-Glass, and What Alice Found There’.

Evolutionary change under Red Queen hypothesis-type dynamics versus Court Jester hypothesis-type dynamics. The blue line represents the abiotic environment. Species A (green) represents a potential prey organism. Species B (purple) represents its potential predator. Species C (black) also preys on species A. Species D (orange) is a descendant taxon of species B. In both scenarios (Red Queen and Court Jester), species B goes extinct (represented by the dotted line). Under the Red Queen scenario ( sensu Van Valen [ 1 ]), the extinction of species B is due to species A shifting from adaptive zone 1 to adaptive zone 2 as it moves towards a fitness optimum and ultimately exceeds the relevant traits of species B (traits relevant to the capacity of species B to capture and consume species A). The new adaptive zone that species A now occupies contains a different predator species (species C) whose fitness is affected by the arrival of species A, as per Van Valen's [ 1 ] zero-sum assumption. In this scenario, abiotic parameters remain unchanged and evolutionary change can still occur. In the Court Jester scenario, the extinction of species B is due to environmental changes that result in suboptimal conditions for species B. Populations within the species become isolated from one another, population sizes decrease, and almost all of the component populations die off, such that species B goes extinct; however, in one case an isolated population of species B diverges, survives and becomes a new species (species D). No changes in the adaptive zone of species A occur in this scenario, nor did they the cause the extinction of species B. Both hypotheses have different evolutionary and spatial scales. The RQH operates across individual populations on small spatial and short temporal scales, leading to differential survival of populations within communities. By contrast, the Court Jester hypothesis is tied to large-scale shifts in the physical environment, which would affect multifarious populations from species in different clades, with each population potentially responding individualistically to the perturbation [ 2 ].

Many aspects of Van Valen's findings have stimulated extensive debate and discussion. For instance, his purported link between extinction probability and species age has been disputed [ 3 – 7 ]. The RQH has also been the subject of reviews (e.g. [ 8 – 10 ]) and a variety of modelling-based studies (e.g. [ 11 – 15 ]). Some of these endorsed aspects of the core predictions of the RQH, others challenged them. Moreover, additional definitions of the RQH have been proposed [ 10 ] that differ from Van Valen [ 1 ]. Scientists have also attempted to use the RQH to explain phenomena beyond its original purview, for example, the dynamics of some host–parasite systems [ 16 ] and the role that these coevolutionary relationships may play in the maintenance of sexual reproduction [ 8 , 17 – 19 ].

Even as the scope of the RQH has broadened, at its core the RQH retains a key element: the primary drivers of macroevolution are held to be biotic interactions, in particular, the effects that the origin of a new trait in one group (populations, species, etc.) has on each group it interacts with [ 9 ]. This feature is not exclusive to the RQH. Further, there is an emphasis in the RQH on groups interacting with equal effect [ 1 ]. This is potentially problematic, as real ecological dynamics are usually far more complicated [ 20 ] and groups rarely have equal effects on each other [ 21 ].

Also, notably many studies [ 2 , 3 , 20 – 24 ] have disputed the validity of numerous aspects of Van Valen's [ 1 ] original RQH. Thus, it is perhaps surprising that the RQH continues to receive support and evolve as a concept [ 9 , 25 – 28 ]. In the light of these theoretical and conceptual peregrinations, we re-examine the RQH, discuss original and subsequent expositions and put forward a single definition, informed by Van Valen's [ 1 ] original exposition, that also accounts for subsequent treatments. The aim is to provide clarity to what has at times been a murky topic in evolutionary biology.

2. ‘It's no use going back to yesterday, because I was a different person then’: the RQH evolves

Stenseth & Maynard Smith [ 12 ] suggested rejecting the RQH's zero-sum expectation and proposed that RQH dynamics may only apply in ecosystems where evolutionary rates are greater than zero, where evolution is mediated by biotic interactions, and where the physical environment remains unchanged. The purpose of this was not to refute the RQH, but to provide the RQH with an alternate null hypothesis where environmental change is the impediment to evolutionary stasis, and evolutionary advances by one species need not necessarily result in a net negative effect of the same magnitude across other species. While this new hypothesis was not the RQH sensu Van Valen [ 1 ], it did open up the application of a RQH-like framework beyond its original domain and began a trend of placing different evolutionary phenomena under the banner of the RQH that did not entirely align with the RQH sensu Van Valen [ 1 ]. For instance, it is suggested that the RQH provides a mechanism for the evolution and maintenance of sex by explaining the value of recombination due to the negative frequency-dependent selection associated with parasitism [ 8 , 17 , 18 ], with sexual lineages better at evading parasites via genetic recombination, thereby forcing continual coevolution on the part of the parasite to maintain a constant level of virulence [ 16 ]. Similar types of continual coevolutionary patterns have also been proposed for predator–prey relationships [ 19 ], with predators maintaining sexual reproduction to preserve a constant level of predation success as variable prey populations within species shift in abundance between those with a greater capacity for obtaining nutrients and those better able to defend against predation.

In an attempt to clarify and categorize the growing body of RQH-influenced ideas, an important synthesis was provided by Brockhurst et al . [ 10 ], who argued that there were three distinct classes of concepts that aligned with the RQH based on the patterns they displayed and the processes involved. The work of Brockhurst et al . [ 10 ] also continued the trend of the RQH evolving beyond the original purview of Van Valen [ 1 ]. Brockhurst et al .'s [ 10 ] classes were designated the Escalatory, Fluctuating and Chase. The Escalatory-RQH occurs when species interactions lead to directional selection and both interacting species move towards a fitness optimum as each struggles to ‘exceed’ the relevant trait of the other species [ 10 ]. Such a dynamic is posited in the case of evolutionary arms races. The Fluctuating-RQH is associated with an oscillating mode of selection, where two antagonist species oscillate backwards and forwards between fitness optima, with one interactor always lagging behind the other [ 10 ]. This involves continual, yet non-directional, evolutionary motion for both antagonists, analogous to constrained stasis or a random walk in species morphology that produces no net change over the long term [ 29 , 30 ]. Finally, the Chase-RQH supposes that across the range of two interacting or co-evolving species, respective populations may be responding in different ways to the biotic milieu they experience [ 10 ]. As populations of the chased antagonist seek to escape co-occurring populations of the chaser through the evolution of novelty, diversity within populations becomes reduced but divergence between populations increases as they spread across a multidimensional phenotypic space. All three classes outlined by Brockhurst et al . [ 10 ] invoke biotic interactions among two groups as the drivers of evolutionary change. The Escalatory-RQH approximates the RQH sensu Van Valen [ 1 ]: a key difference is the latter focuses on higher taxa. The Chase-RQH, however, diverges from the RQH sensu Van Valen [ 1 ] because it involves several interacting component populations of different species, each evolving in varying directions due to distinct selective pressures. The Fluctuating-RQH also potentially diverges from the RQH sensu Van Valen [ 1 ] if the emphasis is placed on changes in specific fitness or phenotypic states, because species are hopping back and forth between distinct states rather than continually running in place. Alternatively, it is possible that if the change in question is migration across the evolutionary landscape, or changes in the dynamics of species interactions, then the relevant species are indeed running in place and the fluctuating-RQH can be considered equivalent to the RQH sensu Van Valen [ 1 ].

3. ‘Off with her head!’: problematic aspects of the RQH

Acceptance of the RQH has not been universal, and a number of authors have either implicitly or explicitly opposed Van Valen's [ 1 ] conclusions. For instance, the competitive species interactions invoked by the RQH have been shown as unlikely to result in persistent evolutionary change [ 31 ]. Questions have also been raised as to whether the taxonomic survivorship curves presented by Van Valen [ 1 ] are truly linear [ 5 , 7 , 32 – 35 ]. While evidence from planktic microfossils has been used to support log-linearity of species-level survivorship curves [ 11 , 36 – 39 ], results for planktic foraminifera have, by contrast, demonstrated a positive relationship between extinction risk and species age [ 6 , 40 , 41 ]. Mass extinction has been singled out as one significant phenomenon that causes groups to deviate from constant extinction over time (e.g. [ 35 , 42 ]). Van Valen [ 1 ] himself noted that mass extinctions in specific clades (e.g. ammonites) were exceptions to the ‘Law of Constant Extinction’ as they represent times of exceptional elevation of extinction rates. Intriguingly though, if mass extinctions truly eliminate large numbers of species effectively at random then, under certain circumstances of prior diversification, they could result in situations where the probability of extinction of species is independent of its duration [ 3 ].

Conceptual criticisms of the RQH have also focused on whether, even assuming taxonomic survivorship curves are linear, the ‘new evolutionary law’ Van Valen [ 1 ] erected was valid [ 3 , 4 , 43 , 44 ]. For example, McCune [ 3 ] concluded that, while the probability of extinction of taxa within a clade may be constant with respect to the duration of those taxa, this does not mean that the rate or the probability of extinction is constant per unit time. She thus argued that the RQH is only one of many potential phenomena that might explain linear taxonomic survivorship curves.

The RQH also depends upon substantive phyletic speciation and associated pseudoextinction [ 4 , 12 ]. This creates a paradox for the RQH because, if phyletic speciation is a primary evolutionary mode, this means that the rate of extinction will be directly correlated with the rate of evolution. Yet the RQH posits that species extinction should be independent of duration. As Vrba [ 4 ] recognized, the rate of phyletic speciation cannot be independent of itself.

4. ‘I'm not crazy. My reality is just different than yours’: abiotic alternatives to the RQH

Another significant criticism of the RQH stems from the limited role it imputes to abiotic factors as important drivers of evolutionary change [ 2 , 4 , 24 ]. Evidence from a variety of sources [ 45 – 51 ] uphold abiotic factors as important drivers of evolution and speciation. This has led to proposed alternatives to the RQH which focus on the physical environment as the main driver of evolution. The most prominent of these is the ‘Court Jester’ hypothesis [ 23 ] ( figure 1 ), with the name chosen to highlight the capricious effects environmental changes can have on evolution. This is in contradiction to the more predictable effects that might be associated with the RQH. The Court Jester attempts to unite under one concept the plethora of previously proposed ideas that support abiotic factors as main drivers of evolutionary change (e.g. ‘turnover-pulse hypothesis’ [ 4 ]; ‘stationary model’ [ 12 ] and ‘coordinated stasis’ [ 52 ]).

Proponents of abiotic change as the chief driver of evolution have been particularly critical of the assertion that competition between groups at taxonomic ranks higher than the species, where the RQH sensu Van Valen [ 1 ] is focused, could result in these groups diverging or going extinct [ 2 , 4 , 24 ]. If and when the RQH does operate, it should be at the level of individual populations at small spatial and short temporal scales. Entities at higher hierarchical levels ( sensu [ 2 , 4 ]), such as clades which consist of many species, should not be expected to respond as a unit ([ 45 ]; though see [ 53 ] for a divergent opinion). Indeed, there is scant evidence that the RQH operates at scales involving entire continents or millions of years [ 2 , 4 , 23 , 24 ]. Instead, individual populations of species living in different communities and climates would interact in different ways with numerous populations of other species across the totality of their ranges, and respond individualistically to any perturbations [ 2 , 4 , 23 , 24 ]. While refocusing the RQH onto the species, and especially the population level, would address these concerns [ 9 ], it is not the same as the RQH sensu Van Valen [ 1 ].

5. ‘Who in the world am I? Ah, that's the great puzzle’: what is the proper domain of the RQH?

As the RQH has been increasingly applied beyond Van Valen's [ 1 ] original focus, it has become increasingly difficult to evaluate its legitimacy. It is first and foremost necessary to ascertain whether the RQH sensu Van Valen [ 1 ] has been generally upheld. If it has been, then the RQH provides an explanation for how biotic interactions could drive phenotypic change, even if only under certain circumstances. However, if it is not generally upheld by most studies, then the question becomes which parts of the RQH should be retained in evolutionary theory and how should the RQH be viewed in the future?

It has been proposed that rejecting the RQH is only possible by demonstrating that evolutionary and ecological changes of organisms (presumably at the species or population level) are primarily due to abiotic change, while at the same time also considering the effect of biotic interactions or abiotic change that is biotically driven [ 9 ]. We diverge from this proposal as this set of conditions is probably unachievable, because a period of constancy of any potential abiotic factors is virtually absent from the geological record [ 14 ]. Another challenge to evaluating the RQH is that biotic and abiotic factors can interact to drive macroevolution [ 54 , 55 ], making it hard to differentiate primary biotically driven evolution from secondary biotically driven evolution instigated by abiotic forcing. Because of these challenges, here we focus on an alternative method to assess the validity of the RQH sensu Van Valen [ 1 ]. As would be the case for any hypothesis, if any (or several) of the core assumptions of the RQH are found to be spurious, then the hypothesis itself would be difficult to uphold, and usage should only be done with significant caution and caveats.

Upon consideration of the evidence for and against the RQH sensu Van Valen [ 1 ] as a valid macroevolutionary concept, potential problems with the hypothesis emerge. Of greatest concern are the evidence-based [ 5 – 7 , 32 – 35 ] and concept-based [ 3 , 4 ] refutations of the foundational assumption of the RQH that taxonomic survivorship curves are linear. This undercuts the very notion of Van Valen's [ 1 ] ‘Law of Constant Extinction’. These refutations are supported by an even larger body of the literature in palaeontology and community ecology, demonstrating that extinction is associated with a range of parameters that are not purely stochastic (e.g. geographical range size is a demonstrated key predictor of extinction [ 55 ]). Van Valen, despite erecting his ‘law’, acknowledged ‘the probability of extinction is not constant over geological time’ ([ 1 ], p. 18) and that constant extinction only prevails if extinctions associated with major perturbations are ignored. Mass extinctions, of course, are a significant feature of the history of life. If the assumption of constant extinction rates is invalid, and extinction probabilities are not age independent, this impugns the RQH as an explanation of macroevolutionary patterns. Evolutionary advances by one species need not produce a net negative effect of the same magnitude across all other coexisting species, because no mechanism is required to maintain equal extinction probabilities. The apparent difficulty of reducing macroevolutionary dynamics to a zero-sum process [ 12 ] finds meaning when the ‘Law of Constant Extinction’ is rejected [ 20 , 21 ].

There are additional issues that, when considered singularly, would not be enough to refute the foundational assumptions of the RQH but, when considered collectively, call into question their cogency. First, Van Valen [ 1 ] used data from groups that have subsequently been identified as paraphyletic, such that they lack evolutionary significance [ 56 ]. Second, only five of the 56 clades Van Valen [ 1 ] analysed were treated at the species level, with the remaining majority comprising either genera or families. The notion that competition, selection or anagenesis could involve higher taxa is inconsistent with basic evolutionary principles. For example, genera are arbitrary units that are not necessarily monophyletic or equivalent across clades [ 56 ] and it is unlikely evolutionary processes can be applied to them.

A further difficulty with upholding the RQH involves the phenomenon of pseudoextinction. Although Van Valen [ 1 ] noted that pseudoextinction is an infrequent process in the fossil record (at higher taxonomic levels), frequent pseudoextinction is perforce necessary in any system where the RQH is the explanatory mechanism for evolutionary change [ 4 , 12 , 57 ]. Like constant extinction, frequent pseudoextinction has been refuted in the literature [ 58 ], and does not make sense in light of modern phylogenetic understanding. Analyses of species origination for planktic foraminifera, a group where taxon durations can be accurately estimated at the species level, have demonstrated pseudoextinction to be a rare occurrence (less than 10%) at the macroevolutionary scale [ 58 , 59 ], and once putative ‘archetypal’ examples of anagenesis with concomitant pseudoextinction have subsequently been shown to involve cladogenesis [ 60 ].

Ultimately, given the challenges to the evidence and core assumptions underlying Van Valen's [ 1 ] RQH, it seems hard to advocate that his microevolutionary mechanism of intrinsic biotic conflicts is what drives the macroevolutionary trends observed in the fossil record. There may still be a place for a RQH-like framework but, if so, it operates at the level of populations within ecosystems [ 2 , 4 , 24 ] and is not the RQH sensu Van Valen [ 1 ].

6. ‘Everything's got a moral, if only you can find it’: a contemporary definition of the RQH

The paucity of support for the RQH sensu Van Valen [ 1 ] does not mean that we propose no conditions exist where biotic interactions could be a significant mechanism for evolutionary change. It also does not take away from the fact that Van Valen's [ 1 ] original RQH was highly valuable, stimulated a variety of important research and greatly furthered conceptual understanding. Antagonistic interactions are potential examples where a qualified version of the RQH could conceivably apply, such as populations of hosts and their parasites or predators and their prey [ 19 , 61 ].

We propose that were one to take Van Valen's RQH and modify it based on developments in evolutionary theory, palaeontology and phylogenetics made after 1973, then what results is Brockhurst et al .'s [ 10 ] Chase-RQH. Although Brockhurst et al . [ 10 ] did not go so far as to nominate the Chase-RQH (or any of their other classes of Red Queen) as a replacement of the RQH sensu Van Valen [ 1 ], we do so here ( figure 2 ). It is not Van Valen's [ 1 ] RQH because it does not focus on constant extinction rates within higher taxonomic groups, nor does it claim that the probability of extinction of any taxonomic group is independent of its duration, but it does capture coevolutionary relationships where two interacting, antagonistic populations are continually changing yet neither is ‘improving’ relative to the other. It also parallels Thompson's [ 62 ] strongly supported view of coevolution, which emphasized the geographical mosaic of the phenomenon. Further, it agrees with Eldredge's [ 2 ], Vrba's [ 4 ], Barnosky's [ 23 ], Benton's [ 24 ] and Liow et al .'s [ 9 ] contention that the RQH be refocused at the species/population level and that Red Queen phenomena occur at the level of populations within ecosystems, not species or higher taxa across vast tracts of geographical space. Brockhurst et al .'s [ 10 ] Chase-RQH also resolves the problems with Van Valen's [ 1 ] RQH that pertain to pseudoextinction. In fact, the Chase-RQH can lead to cladogenesis, as individual populations may deviate from the species as a whole ( figure 2 ), such that a descendant may evolve while its ancestor persists. This divergence among populations subject to different selection regimes places Chase-RQH within the tenets of modern microevolutionary theory. The Chase-RQH also aligns with models linking host–parasite interactions to the evolution and maintenance of sex. Most importantly, the Chase-RQH establishes a link between microevolutionary processes and macroevolutionary patterns that is internally consistent with current thinking in evolutionary biology.

A contemporary definition of RQH based upon Brockhurst et al .'s [ 10 ] Chase Red Queen and informed by Vrba's [ 4 ] turnover pulse hypothesis and Barnosky's [ 23 ] Court Jester hypothesis. Two interacting co-evolving species (species A and B) have seemingly fixed relative fitness over time at the macroscale. A detailed examination (first box) shows at the microscale that actually species A is a chased antagonist pursued by species B across an adaptive zone, with a lag in the response of species B relative to the shifts in mean fitness of species A. These shifts in mean fitness for species A and B represent disparate reactions by individual populations of each species as they respond individualistically to biotic or abiotic perturbations (second box). As populations of the chaser seek to keep pace with co-occurring populations of the chased antagonist, individual populations deviate from the species mean trait values and may even go extinct (e.g. species B, pop 4). Because the mean relative fitness of the two species is constant over time, the relative fitness of co-occurring populations of species A versus species B must be maintained over time, but the relative fitness of non-co-occurring populations of species A versus species B need not remain static. Significant abiotic events can potentially alter this balance, with individual populations of either species becoming isolated and forming a new species, or the extinction of key populations leading to the overall extinction of either species (but not necessarily both).

Acknowledgements

We thank Mabel Alvarado, Rebecca Dorward, Jennifer C. Giron Duque, Kaylee Herzog, Kayla Kolis and Ryan Ridder for their contributions to the preparation of this manuscript. We would also like to thank Paul Craze, the handling editor and three anonymous reviewers whose comments helped to improve our paper.

1 In the tradition of Leigh Van Valen and inspired by his choice of Alice's encounter with the Red Queen as an apt allegory for biotically driven evolutionary dynamics, we use relevant quotes from Lewis Caroll's ‘Alice's Adventures in Wonderland’ or ‘Through the Looking-Glass, and What Alice Found There’ as subheadings for each of our sections.

Data accessibility

Competing interests.

We declare we have no competing interests

This research was supported by NSF ADBC-1206757 and DBI-1602067.

EvolutionBiology.com

Red queen principle.

Through the Looking Glass – Lewis Carroll

The Red Queen Hypothesis in biology states that species continually need to change to keep up with the competition. If a species would stop changing, it would lose the competition with the other species that do continue to change. If you take for example the relationship between a parasite and its host. Both the parasite and the host are involved in an arms race with each other. There is pressure on the host to evolve to become resistant to the parasite and there is pressure on the parasite to evolve ways to cope with the resistance of the host. Both species need to change genetically to keep up with the changes in the other species.

The Red Queen Principle is an important theory because it is used in explaining sexual reproduction , the importance of genetic diversity and the speed of evolution . From the Red Queen Principe follows that species are never “finished”, extinction probability does not increase with existence age of the species and the speed of genetic change over time is important for evolution and survival of species. Species with a quicker generation time will have the ability to evolve faster , giving them an advantage in an arms race. This calls for a mechanism to increase speed of evolution. Sexual reproduction is one way to increase evolution speed in a species, because it allows for new mutations to spread fast in the population and new combinations of alleles to occur faster. It is thought that species with long generation time have to have sexual reproduction to be able to stay in the race with species with a short generation time.

The Red Queen Hypothesis was formulated in 1973 by Leigh Van Valen.

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40 The Red Queen

What does lewis carroll’s red queen have to do with sex.

In this scene from Through the Looking-Glass and What Alice Found There by Lewis Carroll, Alice and the Queen run with all their effort – yet make no progress.  Such is the claim that sex allows organisms to avoid extinction by keeping up in a very odd sort of race.

Alice never could quite make it out, in thinking it over afterwards, how it was that they began: all she remembers is that they were running hand in hand, and the Queen went so fast that it was all she could do to keep up with her: and still the Queen kept crying “Faster! Faster!”, but Alice felt she could not go faster, though she had not breath left to say so. 

The most curious part of the thing was, that the trees and the other things round them never changed their places at all: however fast they went, they never changed their places at all: however fast they went they never seemed to pass anything.  “I wonder if all the things move along with us?” thought poor puzzled Alice.  And the Queen seemed to guess her thoughts, for she cried “Faster! Don’t try to talk!”

Not that Alice had any idea of doing that.  She felt as if she would never be able to talk again, she was getting so much out of breath: and still the Queen cried “Faster! Faster!”, and dragged her along.  “Are we nearly there?” Alice managed to pant out at last.

“Nearly there!” the Queen repeated.  “Why, we passed it ten minutes ago! Faster!” And they ran on for a time in silence, with the wind whistling in Alice’s ears, and almost blowing her hair off her head, she fancied

“Now! Now!” cried the Queen. “Faster! Faster!” And they went so fast that at last they seemed to skim through the air, hardly touching the ground with their feet, till suddenly, just as Alice was getting quite exhausted, they stopped, and she found herself sitting on the ground, breathless and giddy. 

The Queen propped her up against a tree, and said kindly, “You may rest a little, now.”

Alice looked round her in great surprise.  “Why, I do believe we’ve been under this tree the whole time!  Everything’s just as it was!”

“Of course it is, “ said the Queen.  “What would you have it?”

“Well, in our country, “ said Alice, still panting a little, “you’d generally get to somewhere else – if you ran very fast for a long time as we’ve been doing.”

“A slow sort of country!” said the Queen.  “Now here, you see, it takes all the running you can do, to keep in the same place.  If you want to get somewhere else, you must run at least twice as fast as that!”

“I’d rather not try, please!” said Alice….

      Through the Looking-Glass and What Alice Found There by Lewis Carroll

Is sex part of an arms race?

In one human generation, HIV (the virus that causes AIDS) will reproduce over a million times. Given how natural selection works—via heritable variation and differential reproduction—human beings don’t stand a chance against this virus. How can we possibly adapt to such a fast-moving target? For that matter, how can any longer-lived organism compete with a quickly reproducing and quickly evolving enemy? Many of these enemies, or pathogens , such as viruses and bacteria, are also numerous and difficult to detect—invisible to the naked eye, they can enter a host’s body silently and reproduce with a fervor while their victims remain blissfully unaware. Given these challenges, how can any host organism defend itself against its would-be attackers? According to one hypothesis, outwitting pathogens is the whole point of sex.

The Red Queen

We are in the midst of an evolutionary arms race , in which host and parasitic pathogen must constantly adapt. Parasites must adapt to the host’s natural defenses, and host populations are under pressure to keep up with their ever-changing parasites.  This reciprocal evolution between two types of organisms (in this case, host and parasite) is a type of coevolution. According to the Red Queen Hypothesis , sex exists as a mechanism for keeping up with rapidly coevolving pathogens. By generating genetic diversity, sex makes host organisms a moving target.  Like Alice and the Red Queen in Lewis Carroll’s novel (Box 3), both host and parasite are running a race in which neither makes any observable progress. Yet, if the host organisms didn’t change dramatically with each new generation (if they didn’t have sex), they might go extinct.

Parasites adapt to exploit the most common type of host.  Therefore, a host that can produce offspring that have novel defenses against parasites would have an advantage over an organism producing clones–simply by making offspring that are different.

Introductory Biology: Evolutionary and Ecological Perspectives Copyright © by Various Authors - See Each Chapter Attribution is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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7.5 Testing the Red Queen Hypothesis

The Red Queen hypothesis—that sex allows organisms to keep up in a race against coevolving pathogens—can be tested by analyzing three key predictions of this hypothesis:

• Sex is most beneficial where there is a high risk of infection.
• Pathogens are more likely to attack common phenotypes in a population (e.g. clones), as opposed to the less-common counterparts (such as the diverse set of organisms that resulted from sexual reproduction).
• In sexually reproducing populations, individuals choose mates that maximize diversity of their offspring.

Note that all of these predictions implicitly rely on the heritability of being healthy (in this case, the ability to combat pathogens); specifically, parents must be able to pass along to their offspring genes for avoiding pathogens. Testing these predictions has resulted in several lines of evidence supporting the Red Queen Hypothesis.

Prediction 1: Sex is most beneficial where there is a high risk of infection

An excellent system for testing this prediction involves a flatworm parasite in the genus Microphallus, a duck, and a small mud snail ( Potamopyrgus antipodarum ; Figure 7.7). This species of snail is able to reproduce sexually or asexually. The extent of sexual reproduction in a population of snails can be quantified by counting the number of males—asexual snails are all female.

The flatworm’s life cycle begins inside of the snail, where the worm emerges from its egg. Infected snails are consumed by ducks. Once in the duck’s intestine, adult worms have sex and produce eggs. Flatworm eggs are released, with duck feces, into the water, where they are ingested by snails and the cycle continues (Figure 7.8). Snails are harmed by this flatworm, largely because a symptom of infection is sterilization (the flatworm’s scientific name, Microphallus , translates to “small penis”).

Observations of this system in two New Zealand lakes (Alexandrina and Kaniere) revealed that snails are more likely to be sexual (measured by frequency of males) in shallow waters, where ducks feed, than in deeper waters, where ducks do not feed (Figure 7.9).

These results suggest that coevolutionary pressure is greater on the snails in the shallows, presumably because presence of feeding ducks makes the flatworms more common (remember that part of the flatworms’ lives is spent in duck feces). Higher infection rates in the shallows indicate that, in support of Prediction 1, sex is most beneficial where there is a high risk of infection.

Prediction 2:  Pathogens are more likely to attack common phenotypes in a population, as opposed to the less-common counterparts

In the Mexican desert there are isolated pools inhabited by a species of minnow. Within these pools, populations of asexually reproducing individuals exist alongside sexually reproducing individuals. Fish in these ponds exhibit “black spot disease,” which is caused by a parasitic flatworm. Investigators have observed the frequency of sexual and asexual fish and the number of black spots in each type of fish in these ponds. Clonal fish are likely to have the most common phenotype in these ponds (as they are genetically identical to each other), while the sexually reproducing fish will have a wide variety of infrequent phenotypes. As the Red Queen Hypothesis predicts, the common type of fish (usually one of the clonal species) had the highest number of parasitic spots. In ponds where there was a genetically diverse, sexually reproducing population, the sexual fish had fewer spots.

Additional evidence comes from the evening primrose (Figure 7.10), a flowering plant that—like the minnows, snails, and water fleas discussed above—exists in sexual and asexual forms. Evening primrose can be damaged by mildew from a pathogenic fungus. The plants produce an enzyme protein called chitinase to defend themselves against this fungus. A recent comparison indicated that the sexually reproducing primrose plants had greater variety in the gene that codes for chitinase than did the asexual plants. In addition, the overall amount of chitinase expressed was higher in the sexual plants than in the asexuals. Finally, the researchers found that the plants that were more resistant to mildew damage had higher fitness (they produced more fruit, and thus more offspring) in the presence of that pathogen. In evening primrose, certain rare mutations in a key gene render an individual less susceptible to a pathogen, supporting the prediction that parasites are more likely to attack the most common phenotype in a population, and providing additional evidence for The Red Queen.

Know Your Pathogens

A pathogen is something that infects and causes a fitness cost in another organism.  Pathogens come in a wide variety; some of them are not even considered living!

Prions – Prions are non-living infectious agents that are misfolded proteins.

Viruses – Whether you consider viruses alive or not depends on your definition of life.  Viruses are protein-encased DNA or RNA entities that hijack a cell’s replication machinery to reproduce.  Viral infections include influenza, HIV, HPV, and herpes.

Fungal pathogens – Fungi are responsible for a variety of infections including mildew, thrush, athlete’s foot and smut.

Bacteria – Bacteria are prokaryotic organisms that occur everywhere. There are more bacteria in and on you than there are cells in your body. Fortunately, the vast majority of bacteria are benign. However, some bacteria cause problems such as urinary-tract infections, some kinds of pneumonia, ear infections, pertussis (whooping cough), chlamydia, gonorrhea, and syphilis.

Protists – Protists are single-celled eukaryotes that cause diseases such as malaria and amoebic dysentery.

Animals –Common animal pathogens include lice, many types of worms, and parasitic wasps.

Prediction 3: In sexually reproducing populations, individuals choose mates that maximize diversity in their offspring

If there is a fitness advantage to diversity, parents can best maximize their offsprings’ potential (and have more grand-offspring) with careful mate choice. There are numerous examples of organisms preferring mates that increase offspring diversity, and shunning mates that might do the opposite. Even many hermaphrodites, with both male and female sex organs, seek other hermaphrodites for copulation…even if they are capable of self-fertilization.

An excellent model for studying mate choice is Atlantic Salmon, an important commercial fish that lives its life in the ocean and returns to freshwaters to mate (or spawn ). Sofia Consuegra and Carlos Garcia de Leaniz compared offspring diversity of salmon that were mated in a commercial fish hatchery (and unable to choose their mates) against that of salmon allowed to choose mates in the wild. The hatchery-spawned fish exhibited lower diversity than did the wild-spawned fish. Furthermore, hatchery-spawned fish displayed a greater number of roundworm parasites ( Anisakis ) then did their wild-spawned counterparts (figure 7.11).  These results support the prediction that individuals will choose mates that maximize diversity in their offspring. Also, this work lends support to the Red Queen Hypothesis by illustrating a potential benefit to Atlantic Salmon—namely, parasite avoidance.

  Check Yourself

• By Michal Maňas - Maňas M. (2014). "Photo of the day (35): Potamopyrgus antipodarum". Blog about gastropods. http://gastropods.wordpress.com https://gastropods.files.wordpress.com/2014/10/potamopyrgus-antipodarum.png, CC BY 4.0, https://commons.wikimedia.org/w/index.php?curid=36715581 ↵

Introduction to the Evolution & Biology of Sex Copyright © by Katherine Furniss and Sarah Hammarlund is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Red Queen Hypothesis: What It Means in Business & How to Avoid It

Table of Contents

Survival — it's something we all strive for, and businesses are no different. Companies must adapt to keep up with the competition or perish in the business world- especially the IT industry. But is survival the only way to stay afloat? When do you know you've done enough? The term ‘red queen effect’ has become synonymous with success and survival in the IT industry as businesses must keep up with the competition or risk falling behind.

So, how does the red queen hypothesis affect companies, and how can you avoid it? This article will explain the red queen effect, how it relates to businesses, and, most importantly, how to avoid falling into its trap.

What Is the Red Queen Hypothesis?

The red queen hypothesis is a term coined from Lewis Carroll’s novel, Through the Looking-Glass , emphasizing the need to evolve to remain competitive and survive. In the book, the Red Queen tells Alice, “It takes all the running you can do to keep in the same place.” Alice quickly realizes that if she stops running, she will fall behind. The Queen tells Alice that this is the way of life in her kingdom, unlike Alice's slow sort of country; she must continue running to stay in the same spot. 

This is the same concept as the red queen effect in business — companies must constantly change and improve their products or services to stay competitive.

Red Queen Hypothesis Example

One of the best red queen effect examples to illustrate this phenomenon is to refer to Warren Buffet's comments on investments in the textile industry versus the candy and newspaper business. He notes that Berkshire Hathaway Inc. could make significant capital expenditures to reduce variable costs and increase economic benefits, but that wasn’t enough. The variable cost savings would be undercut by competitors through reduced prices, investing in a losing proposition. 

This is a common red queen cause and effect among many companies: focusing on staying afloat in a poor industry and, as a result, wasting capital for a poor return on it, causing ripple effects across the organization.

The red queen effect is the need to continually adapt and evolve to maintain relevance in an ever-changing environment. Companies must constantly innovate and find new ways to stay ahead of the competition to ensure their survival and success.

How to Avoid the Red Queen Effect

It may sound like the red queen effect isn't such a bad thing — after all, any business should be looking to innovate and stay ahead. But it can become a trap if you're not careful. Instead of competing on a level playing field, you can find yourself lost in a never-ending cycle of adaptation and innovation, which can lead to burnout and frustration. Here are some tips to avoid your business falling into the trap of the red queen effect.

Strategize on Market Opportunities 

The first step to avoiding the red queen effect is to take a step back and look at the industry context. What are your competitors doing? Are there industry conditions or trends you can capitalize on? Look at the market position of your industry and try to identify areas of potential for industry growth. This will give you a better understanding of where your business can fit in and what market opportunities are out there.

In tech, industry trends move fast and can be hard to keep up with. Slowing down and taking your time when considering market opportunities is essential — you don't want to rush into something you're not ready for.

Differentiate From Competition

Once you’ve identified a market opportunity or industry trend, it's time to set yourself apart from the competition. Consider making yourself stand out from your competition by creating a distinct identity. Find ways to position your business to give you an advantage over your competitors. This could be anything from offering a new product or service to setting industry-leading customer service and support standards.

Ask yourself what makes your business unique and how to differentiate yourself in the marketplace. Doing something different, and being proactive, can help you avoid the trap of the red queen effect by giving you an edge over your competition rather than getting stuck in a cycle of reactive adaptation and innovation.

Don't Sit Still for Too Long

The key to avoiding the red queen effect is to keep moving — but not too fast. While you want to get ahead of the competition, you don't want to rush into something and become overwhelmed. If you stay in the same place for too long, you risk falling behind. For instance, if you see an opportunity to launch a new product, don't wait too long, or the market may become saturated. You must strike while the iron is hot and act quickly but do so thoughtfully.

At the same time, don't let yourself become too fixated on the competition — it can be tempting to focus on what the other players in your industry are doing, but this could prevent you from identifying opportunities elsewhere. Be aware of the competition and market trends, but don't let them completely dictate your strategy.

Prioritize Mental Health

Your team is the key to success, so ensuring everyone is happy and healthy is essential. Working long hours and constantly striving for more can take its toll, so taking regular breaks and ensuring your team has the necessary resources is essential. There are several ways to do this, including:

• Offering flexible working hours and remote work options
• Delegating work more effectively
• Providing a support system that allows your team to reach out if they're feeling overwhelmed

It’s also important to foster collaboration and growth within your broader organization. Let team members learn and develop their skills, which will help them stay motivated and engaged. Finally, ensure everyone is on the same page by regularly communicating your goals and objectives.

Be Innovative 

Innovation is vital to avoiding the red queen effect. Striving for success through innovation and forward-thinking will help you stay ahead of the competition and ensure your business remains relevant. Foster an environment of creativity and brainstorming where you can test and iterate on new ideas. Look for new and exciting ways to solve problems within your industry, and don’t be afraid to take risks.

Turn Ideas Into Action

An idea is only as good as its execution, so once you have identified a new opportunity or trend, take action instead of letting it sit on the shelf. For instance, if you have developed a new product or service, set aside the time and resources to bring it to life. Have the courage to take risks and not be afraid to try innovative ideas — you never know what could happen. Moreover, don’t be afraid to ask for help from industry experts or seek out mentors and advisors who can provide guidance.

Revelo Can Help Your Business Thrive 

Your business can do more than just survive — it can thrive. By understanding and avoiding the red queen effect, you can remain ahead of the competition and continue to see a return on capital rather than poor returns. The key is to stay aware of the competition while innovating, taking risks, and acting on ideas. However, to make this possible, you must first have the right team to help you drive your business forward.

If your business is looking to remain ahead of the competition with a team that can drive innovation and productivity, Revelo can help . Revelo is a talent marketplace that connects you with the top IT professionals in the industry who are ready to help your business succeed. Contact Revelo today to learn how we can help you build an unstoppable team .

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Red Queen Hypothesis, The

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Red Queen’s Race ; The Red Queen Effect

The Red Queen hypothesis is an evolutionary hypothesis that states that all living beings must constantly adjust, evolve, and reproduce while attempting to survive ever-evolving predators.

Introduction

This hypothesis was first proposed by Leigh Van Valen in 1973. The term “Red Queen” is a reference to a statement made by the Red Queen to Alice, characters in the popular 1871 novel Through the Looking-Glass , written by Lewis Carol.

The Red Queen Hypothesis and it’s Relevance

The statement that sparked this hypothesis is “Now, here , you see, it takes all the running you can do, to keep in the same place” (Carroll 1871 ). Van Valen’s reference is essentially a metaphor for an evolutionary arms race. Predators that undergo a beneficial adaption may spark a change in selection pressure when it comes to a group of prey. This, in turn, would continue in a positive feedback loop, which gives rise to a form of antagonistic coevolution....

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Carroll, L. (1991[1871]). 2: The garden of live flowers. In Through the looking-glass (The millennium fulcrum Edition 1.7 ed.).

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Gat, A. (2009). So why do people fight? Evolutionary theory and the causes of war. European Journal of International Relations, 15 (4), 571–599.

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Vermeij, G. J. (1987). Evolution and escalation. An ecological history of life (pp. 369–370). Princeton: Princeton University Press.

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Evolutionary insights into host–pathogen interactions from mammalian sequence data

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• Comparative genomics

Infections are possibly the major selective pressure acting on humans, and host–pathogen interactions contribute to shaping the genetic diversity of both organisms.

Comparisons among species provide a snapshot of selective events that have been unfolding over long timescales. These approaches use extant genetic diversity and phylogenetic relationships among species to identify positively selected sites.

Positive selection often acts on a limited number of sites in a protein that is otherwise selectively constrained; one example is the localized signal of selection at Niemann–Pick C1 protein (NPC1), the receptor for the Ebola virus.

As epitomized by the evolutionary history of tripartite motif-containing 5 ( TRIM5 ), past infection events may leave a signature that affects the ability of extant species to fight emerging pathogens.

Protein regions at the host–pathogen interface are expected to be targeted by the strongest selective pressure (this is the case for dipeptidyl peptidase 4 (DPP4) and angiotensin-converting enzyme 2 (ACE2), which act as receptors for coronaviruses).

Other mammals host a wide range of viruses that are highly pathogenic for humans. Sequencing the genomes of these pathogens will be instrumental in refining our understanding of the process of host–pathogen interaction.

Pathogen-driven natural selection is not limited to the immune system: genes that encode incidental pathogen receptors and components of the contact system and coagulation cascade can also be targeted.

Infections are one of the major selective pressures acting on humans, and host-pathogen interactions contribute to shaping the genetic diversity of both organisms. Evolutionary genomic studies take advantage of experiments that natural selection has been performing over millennia. In particular, inter-species comparative genomic analyses can highlight the genetic determinants of infection susceptibility or severity. Recent examples show how evolution-guided approaches can provide new insights into host–pathogen interactions, ultimately clarifying the basis of host range and explaining the emergence of different diseases. We describe the latest developments in comparative immunology and evolutionary genetics, showing their relevance for understanding the molecular determinants of infection susceptibility in mammals.

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Host–parasite co-evolution and its genomic signature

The microbiome extends host evolutionary potential

Host genotype and genetic diversity shape the evolution of a novel bacterial infection

Infections are thought to represent the major selective pressure for humans 1 and, possibly, for all living organisms. Encounters of hosts and pathogens result in so-called 'arms races', whereby hosts are under pressure to evolve resistance to pathogens while pathogens strive to develop countermeasures to evade host surveillance and to achieve a successful infection. Thus, when resistance and counter-resistance are at least partially genetically determined, cyclical adaptation and counter-adaptation occur, and a genetic conflict is fuelled ( Box 1 ). This is generally referred to as a 'Red Queen' scenario, a definition proposed by Leigh Van Valen 2 after the character in Lewis Carroll's novel Through the Looking-Glass who says: “It takes all the running you can do, to keep in the same place”. At its core, the Red Queen hypothesis highlights the relevance of biotic versus abiotic interactions as drivers of perpetual evolutionary change (see Ref. 3 for a recent review). Although the hypothesis is perfectly conjured up by the Red Queen imagery proposed in 1973, some of its principles can be traced back to the work of J. B. S. Haldane at the beginning of the twentieth century. In fact, Haldane was the first to propose that infectious diseases should be considered as a major selective pressure in our species 4 .

In this Review we present some of the most recent advances in the field of evolutionary biology applied to the study of infectious diseases. In particular, we focus on inter-species comparisons among mammals and on the way in which these analyses have helped to clarify the genetic determinants of species-specific infection and disease, as well as the reasons behind pathogen emergence. Although arms races involve both the host and the pathogen, in this Review we only focus on genetic diversity in mammalian hosts. Host–pathogen genetic conflicts are not confined to mammals (and their pathogens): they drive molecular evolution in most realms of life, including bacterial–bacteriophage systems 5 , plants and their infectious agents 6 , as well as invertebrates and their pests 7 , 8 .

Although we review studies and methods ( Boxes 1 , 2 , 3 ) that analyse genetic diversity at the inter-species level, the investigation of intra-species and intra-population signatures of pathogen-driven selection has also provided extremely valuable insight into infectious disease susceptibility, especially in our species. The interested reader is directed towards several recent reviews for more information 9 , 10 , 11 , 12 , 13 .

Box 1: Detecting natural selection

Comparisons among species take a snapshot of selective events that have been unfolding over long timescales. Most of these approaches use extant genetic diversity and phylogenetic relationships among species to infer underlying evolutionary patterns. Briefly, inter-species approaches rely on the alignment of orthologous coding sequences, analyse these alignments site-by-site, and at each site determine which, among all possible substitutions, would be non-synonymous (amino acid replacing) or synonymous (non-amino acid replacing) (see the figure). The observed number of non-synonymous differences per non-synonymous site (dN) and the observed number of synonymous differences per synonymous site (dS) are then estimated. Under neutral evolution, the rate at which amino acid replacements accumulate is expected to be comparable to the rate for silent changes and, therefore, dN/dS should be equal to 1 (green codons in the figure). Nonetheless, most amino acid replacements are deleterious and, as a consequence, are eliminated by selection; this results in a large preponderance of sites with dN/dS <1, a situation referred to as purifying (or negative) selection (shown in blue in the figure). Conversely, the selective pressure exerted, for instance, by a pathogen, may favour amino acid replacements (for example, changes that modify the sequence and structure of a cellular receptor): in this case, dN/dS may reach values greater than 1, a hallmark of positive (or diversifying) selection (red in the figure).

The figure shows a hypothetical example whereby a virus uses a cellular receptor to infect the host. To prevent viral binding and infection, selection favours variants that modify the sequence and structure of the host receptors; on the other side, the virus adapts to such changes by gaining mutations that keep re-establishing receptor binding. This process fuels a genetic conflict, which is evident at the interaction surfaces. Some lineages may be under stronger selective pressure than others and may display lineage-specific selected sites (episodic selection; cyan). In this case the branch of the phylogeny leading to these species may show significant evidence of positive selection ( Box 3 ).

Box 2: Detection of positively selected genes and sites

The 'site models' implemented in the phylogenetic analysis by maximum likelihood (PAML) package 91 are widely used to infer positive selection and to identify positively selected sites. These models allow dN/dS to vary from site to site, assuming a constant rate at synonymous sites. Data (alignment and phylogenetic tree) are fitted to models that allow (selection models) or do not allow (neutral model) a class of codons to evolve with dN/dS >1. Likelihood ratio tests are then applied to determine whether the neutral model can be rejected in favour of the positive selection model. If so, the gene is declared to be positively selected. Also, if (and only if) the null hypothesis of neutral selection is rejected, a Bayes empirical Bayes (BEB) approach can be used to detect specific sites targeted by selection (BEB calculates the posterior probability that each site belongs to the class with dN/dS >1) 92 , 93 .

The PAML approach implicitly assumes that the strength and direction of natural selection is uniform across all lineages. Because this is often not the case, Murrell and co-workers recently developed the mixed effects model of evolution (MEME, HyPhy package) 94 . MEME allows the distribution of dN/dS to vary from site to site and from branch to branch; thus, the method has greater power to detect episodic selection, especially if it is confined to a small subset of branches in the phylogeny. A major issue related to these approaches is their extreme sensitivity to errors in sequence (coverage), annotation and alignment. Misalignments and incorrect sequence information may result in apparently fast evolutionary rates and thus inflate the false-positive rate 95 , 96 , 97 . The use of specific alignment algorithms (for example, PRANK) and filtering procedures (for example, GUIDANCE) may partially overcome this problem 98 . Likewise, genetic variability that is generated by recombination can be mistaken for positive selection 99 . Thus, to limit false positives, alignments should be screened for recombination before running positive selection tests (and, if necessary, split on the basis of recombination breakpoints) or recombination should be incorporated into the model.

Box 3: Detection of lineages under positive selection and lineage-specific sites

Signatures of selection along specific branches can be detected through the so called 'branch-site' models implemented in the phylogenetic analysis by maximum likelihood (PAML) package 100 . In analogy to the site models described in Box 2 , alignment errors result in high false-positive rates when branch-site models are applied 101 ; this issue can be partially mitigated by the use of specific aligners 101 . Branch-site models require the phylogeny to be divided into 'foreground' and 'background' branches. A likelihood ratio test is then applied to compare a model that allows positive selection on a class of codons for the foreground branches with a model that does not allow such selection 100 . Designation of the foreground branches needs a priori information, possibly based on biological evidence. If no clues are available as to which branches are more likely to have undergone selection, it is still possible to run the analysis by designating each branch of the tree as 'foreground'; this generates a multiple-hypothesis testing problem that must be appropriately corrected 102 .

Two alternative methods can detect selection at specific lineages without a priori branch partition. The branch site-random effects likelihood (BS-REL) method considers three different evolutionary scenarios (purifying, neutral and diversifying selection) for all branches in a given tree, and each branch is considered independently from the others; the algorithm applies sequential likelihood ratio tests to identify branches with significant evidence of positive selection 103 . The second method, the covarion-like codon model (FitModel) 104 , allows each site to switch between selective regimes at any time on the phylogeny. Thus, switches are not necessarily associated with tree nodes. Recently, this approach was shown to be more powerful than the branch-site tests if a priori information is available 105 . Both FitModel and the PAML branch-site methods envisage a Bayesian approach to identify sites evolving under episodic positive selection. However, extensive simulations revealed that the branch-site approach is accurate but has limited power at detecting sites 106 . This problem has been referred to as the 'selection inference uncertainty principle' — that is, it is difficult to simultaneously infer both the site and the branch that are subject to positive selection 94 .

The dynamics of host–pathogen interactions

A central tenet of the Red Queen hypothesis is that organisms must continually adapt to survive and thrive in the face of continually evolving opposing organisms. Nonetheless, evolution is not all about biotic interactions. At a macroevolutionary level, mixed models of evolution are likely to operate; biotic factors mainly shape species diversity locally and over short time spans, whereas shifts in the physical environment (for example, climate changes and oceanographic and tectonic events) drive evolution at a large scale, across much longer time periods 14 . Recently, a new interpretation of the Red Queen hypothesis was proposed 15 ; the analysis of several phylogenies from different taxa indicated that speciation mostly occurs at a constant rate through rare stochastic events that cause reproductive isolation 15 . This view curtails the role of biotic interactions as major determinants of species diversity 15 .

Despite these observations, the Red Queen hypothesis has proven to be an extremely useful framework for the study of host–pathogen interactions. In this context, Red Queen dynamics can be divided into different types (see Ref. 3 for a recent review). Frequency-dependent selection, for example, determines allele frequency fluctuations in both host and pathogen populations. In this scenario, rare alleles are favoured by selection (the pathogen, for instance, may be adapted to the most common host genotype and may fail to infect hosts carrying a rare allele), and diversity within populations is maintained. Escalatory arms races are another form of selection that usually apply to quantitative or polygenic traits and proceed through recurrent selective sweeps. Selection results in an escalation in the phenotypes of both the host (for example, resistance) and the pathogen (for example, virulence). Finally, in chase Red Queen scenarios the host is under pressure to reduce the strength of the interaction through de novo evolution of novelty, whereas the pathogen evolves to tighten the interaction by reducing phenotypic distance. Chase scenarios occur when host–pathogen interactions have a complex genetic basis (polygenic); they determine selective sweeps and tend to reduce genetic diversity within populations.

Over the years, the Red Queen hypothesis has been supported by the description of rapid rates of evolution in genes involved in genetic conflicts and, in a few instances, by the temporal reconstruction of host–pathogen co-evolution in natural settings 16 . More recently, the development of experimental evolution approaches has allowed its formal testing 17 , 18 . Although extremely valuable, laboratory-based studies often use an isogenic host population that is infected by one or a few pathogen strains, and such studies only partially recapitulate the complex nature of host–pathogen interactions that occur in real life. For instance, phenotypic plasticity (an environmentally based change in the phenotype) and multiway host–pathogen interactions are common in nature. A remarkable example of phenotypic plasticity is the vertebrate adaptive immune system: through rearrangement and somatic hypermutation, the same genetic arsenal is used to combat a wide array of pathogens and to develop lifelong resistance to some infections. Despite the relevance of adaptive immunity for host defence, its action does not preclude pathogen-driven selection at several genes involved in innate immunity or, more generally, in the interaction with pathogens (these represent the focus of this Review). As for multiway interactions, these represent the norm: the same host can be infected by multiple pathogens (or even by multiple strains of the same infectious agent) during its lifetime, whereas pathogens differ in their ability to infect one or more host species. Thus, multiple host–pathogen interactions might drive the evolution of the same or different molecular systems, blurring the expectations of the Red Queen hypothesis. Finally, hosts with long generation times (such as mammals, which are the focus of this Review), evolve at lower rates compared with most of their pathogens and also display smaller population sizes, resulting in an asymmetry of the arms race (although parasites with life cycles involving two or more species may be constrained in their ability to adapt (reviewed in Ref. 19 )). Even in the presence of a strong selective pressure (for example, a fatal infection), several generations may be required before the molecular signatures of the genetic conflict can be detected in mammalian host genomes 19 . Nevertheless, natural selection signatures have been described at several mammalian genes that interact with recently emerged human infectious agents (for example, HIV-1), possibly as a result of the pressure imposed by extinct pathogens or because these agents have established long-lasting interactions with non-human hosts.

Ancient and recent infections

Since 1940, 335 emerging infectious diseases (EIDs) have been reported in humans; EID events are increasing significantly over time and are dominated by zoonoses, most of which originate from wildlife 20 . Recent zoonoses are exemplified by Ebola virus (EBOV) outbreaks, which have occurred episodically in Africa since 1976, and by the emergence of Middle East respiratory syndrome coronavirus (MERS-CoV) as a dangerous human pathogen. Both EBOV and MERS-CoV are thought to have originated in bats and spread to humans either directly or through an intermediate host. Because EIDs are almost inevitably caused by an existing pathogen that adapts to infect a new host, comparative analyses of different species may help to unveil the genetic and immunological determinants underlying pathogen spillover and infection susceptibility.

HIV-1, for example, originated from the cross-species transmission of the simian immunodeficiency virus SIV cpz , which naturally infects chimpanzees 21 . Old World monkeys are resistant to HIV-1 infection owing to a post-entry viral block operated by cellular restriction factors. This differential susceptibility to infection was exploited to isolate tripartite motif-containing protein 5 (TRIM5; also known as TRIM5α), a major retrovirus restriction factor, from a rhesus macaque cDNA library 22 . The protein product of TRIM5 binds directly to the incoming viral capsid and targets it for disassembly. Whereas macaque TRIM5 is highly efficient against HIV-1, the human protein is not 22 . Most species-specific determinants of antiviral activity were mapped to a short amino acid stretch in the so-called B30.2 (or SPRY) domain of TRIM5 (Ref. 23 ). In primates, this region has evolved under positive selection , and the human lineage shows some of the strongest selection signatures 23 . Why then is human TRIM5 so highly inefficient against HIV-1? Possibly because the human gene evolved to fight another retrovirus. In a seminal paper, Kaiser and co-workers resurrected an extinct Pan troglodytes endogenous retrovirus (PtERV1) and showed that the amino acid status of a single residue in the TRIM5 B30.2 domain modulates its activity against PtERV1 and HIV-1, with the gain of restriction for one virus resulting in decreased control of the other one 24 . Human TRIM5 is very active against PtERV1, suggesting that our ancestors adapted to fight this virus or some related retrovirus, and this left them (us) unprepared against the HIV-1 epidemic.

More recently, several genes identified as HIV-1 host factors were analysed in primates, and evidence emerged of positive selection at five of these (ankyrin repeat domain 30A ( ANKRD30A ), CD4 , microtubule-associated protein 4 ( MAP4 ), nucleoporin 153 kDa ( NUP153 ) and RAN binding protein 2 ( RANBP2 )) 25 . Importantly, most of the positive selection targets in CD4, MAP4 and NUP153 are located in protein regions or domains that are responsible for direct interaction with the virus. The authors suggested that the selective pressure on these genes was exerted by ancient lentiviruses 25 , 26 .

Overall, a number of concepts can be taken from these studies: past infection events may leave a signature that affects the ability of extant species to fight emerging pathogens. Evolution may act through trade-offs, whereby changes that are favourable in one specific environment (in this case, the presence of a specific pathogen) may be unfavourable when conditions change. Protein regions at the host–pathogen interface are expected to be targeted by the strongest selective pressure. Evolutionary studies based on inter-species comparisons allow the identification of molecular determinants of infection susceptibility at single amino acid resolution.

Susceptibility to infection in mammals

Mammals display different susceptibility to distinct pathogens, and infection with the same agent can have extremely different outcomes in diverse species (see Ref. 27 for a recent review). Thus, domestic and wild mammalian (and non-mammalian) species represent natural reservoirs of human pathogens and/or may provide the adaptive environment for pathogen spillover. Because host reservoir species and their pathogens often co-evolve for millions of years, evolutionary analyses may help to explain host adaptive events associated with low susceptibility and mild disease outcomes. The most extensive body of knowledge on host–pathogen specificity focuses on viral infections, as the example of TRIM5 mentioned above testifies, but recent work has also shed new light on bacterial diseases.

Complement evasion. Leptospirosis, one of the most prevalent human bacterial zoonoses worldwide, is caused by bacteria of the Leptospira genus. Wild rodents are considered to be the main reservoirs for human leptospirosis, but a study of Malagasy small mammals indicated that several endemic species of tenrecs and bats are also infected with Leptospira species that are markedly specific to their hosts, suggesting long-term adaptation of the bacterium to different hosts 28 . A feature that pathogenic Leptospira species share with other bacteria is complement evasion. Indeed, these spirochetes have evolved different strategies to elude complement-mediated killing; thus, leptospiral immunoglobulin-like (Lig) proteins can bind complement factor H (CFH) and C4b-binding protein (C4BP) to mediate complement inactivation at the bacterial surface. A genome-wide analysis of positive selection in six mammalian species indicated that the complement system has been the target of extremely intense selective pressure 29 . Similar results were obtained by analysing positively selected genes in the bat Myotis brandtii 30 . Thus, selection-driven species-specific differences at complement genes might explain differential susceptibility to infections. In line with this view, human-specific pathogens such as Neisseria gonorrhoeae and Neisseria meningitidis bind CFH of human origin, but not CFH from other primates, and a single amino acid change (N1203R) in the chimpanzee molecule restores CFH binding to sialylated gonococci and bacterial killing 31 . Several sequenced mammalian genomes are now available; it will be important to study the detailed pattern of molecular evolution at complement genes, with the aim of gaining insight into the determinants of species-specific complement evasion.

Toll-like receptor evolution. Yersinia pestis provides another remarkable example of differential susceptibility to a bacterial infection. Again, rodents act as a natural reservoir for this human pathogen. As with other Gram-negative bacteria, lipid A, the biologically active component of Y. pestis lipopolysaccharide (LPS), is recognized by Toll-like receptor 4 (TLR4) and its co-receptor lymphocyte antigen 96 (LY96; also known as MD2) (see below). Recent data showed that, compared with mouse cells, human cells respond less efficiently to hypoacylated lipid A; this effect is almost entirely due to differences in TLR4 and LY96 sequences, as assessed by the generation of humanized mice 32 . Different responsiveness to variably acylated LPS from other sources (for example, Escherichia coli ) had previously been described 33 . Starting from this premise, Ohto and co-workers 34 solved the crystal structure of the mouse TLR4–LY96–LPS and TLR4–LY96–lipid IVa (a synthetic tetra-acylated lipid A precursor) complexes and compared them to the human counterparts. Structural differences were detected in the interaction of lipid IVa with the two mammalian receptors, with some amino acid replacements in LY96 and TLR4 possibly being responsible for the observed differential binding 34 . Analysis of TLR4 in mammals revealed that the receptor has evolved adaptively 35 . We mapped positively selected sites onto the structure of the human and mouse complexes and observed that some of these may indeed account for structural differences between humans and mice ( Fig. 1 ).

As discussed in Box 1 , regions at the host–pathogen contact interface are expected to be targeted by the strongest selective pressure. Three examples are shown here. a | Detail of the Toll-like receptor 4 (TLR4)–lymphocyte antigen 96 (LY96)–lipid IVa complex. Mouse TLR4 and LY96 are in white and grey, respectively; lipid IVa is in blue. Sites that are positively selected in mammals 35 are mapped onto the TLR4 structure (red): several of these flank or correspond (orange) to residues that differ between humans and mice and that surround the phosphate groups of lipid IVa (yellow) 34 . If Lys367 and Arg434 are replaced with the human residues (Glu369 and Gln436, respectively), the responsiveness of mouse TLR4–LY96 to lipid IVa is abolished. b | Structures of human CD86 (white; transmembrane and juxtamembrane region) and MIR2 (grey; encoded by Kaposi sarcoma-associated herpesvirus). CD86 sites that are involved in the interaction and that are positively selected in mammals are shown in red. c | Complex of transferrin receptor protein 1 (TFR1) with the surface glycoprotein (GP1) of Machupo virus (MACV), a rodent arenavirus that can also infect humans through zoonotic transmission. TFR1 residues involved in the interaction with GP1 are in yellow, positively selected sites are in red and positively selected sites that directly interact with GP1 are in orange.

PowerPoint slide

Exploring natural reservoirs of infectious agents. Rodents are the most established animal model for human disease, including for susceptibility to infection. In recent years, however, technological advances have made the sequencing of whole genomes a relatively quick and inexpensive process. The genome sequences of non-model mammals that serve as natural reservoirs of human infectious agents are now available, allowing the unprecedented opportunity to exploit these data for molecular evolution studies. Bats, for example, are known to host a wide range of viruses that are highly pathogenic to humans 36 . The genomes of six bat species have been sequenced so far, and three of these ( M. brandtii, Pteropus alecto and Myotis davidii ) were analysed in detail to unveil the evolutionary history of specific traits 37 . Results showed that different families of immune receptors — including killer cell immunoglobulin-like receptors (KIRs), killer cell lectin-like receptors (KLRs), sialic acid-binding immunoglobulin-like lectins (SIGLECs) and leukocyte immunoglobulin-like receptors (LILRs) — have expanded or contracted in distinct bat species. Also, in these three bat species, as well as in the common ancestor of P. alecto and M. davidii , genes involved in immunity represented preferential targets of positive selection 37 . This is not unexpected: immune-response genes have been shown to have evolved rapidly in most mammalian species analysed to date 9 . Thus, although these sequenced bat genomes have not yet provided an explanation as to why bats are tolerant to EBOV, for instance, they pave the way for further analyses to test specific hypotheses and/or to address the molecular determinants of host–pathogen interactions. In a recent study, Demogines and co-workers 38 showed how this can be accomplished. The authors focused on angiotensin-converting enzyme 2 (ACE2), which serves as a receptor for severe acute respiratory syndrome coronavirus (SARS-CoV) cell entry. In particular, the receptor-binding domain of the viral spike protein is responsible for ACE2 binding and is a major determinant of host range 39 . Although the human SARS epidemic was suggested to have originated from the zoonotic transmission of SARS-CoV from bats to humans, possibly via an intermediate host (for example, palm civets) 40 , 41 , no ACE2-binding SARS-CoV-like virus had been identified in bats when Demogines and collaborators started their work 38 . The authors analysed ACE2 genes in 11 bat species, and results revealed that the gene evolved adaptively and that the positively selected residues of the bat genes map at the ACE2–SARS-CoV interaction surface ( Fig. 2 ). These data led to the conclusion that ACE2-binding coronaviruses originated in bats 38 . This finding was confirmed in a subsequent study that isolated an ACE2-binding SARS-like coronavirus from horseshoe bats in China 42 , highlighting the power of evolutionary studies in predicting host range and disease emergence.

The receptor-binding domains (RBDs) are structurally similar in the spike proteins of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) 47 , but these proteins bind distinct cellular receptors. The structure of the SARS-CoV and MERS-CoV RBDs in complex with angiotensin-converting enzyme 2 (ACE2) and dipeptidyl peptidase 4 (DPP4), respectively, are shown with the binding interfaces enlarged. In both panels, sites that directly interact with the RBD are shown in yellow. a | ACE2 residues that are responsible for RBD binding and that are also positively selected in bats are shown in orange 38 . b | DPP4 residues that are positively selected at the RBD binding interface are shown in red (positively selected) and orange (positively selected and interacting); sites in cyan were found to be positively selected along specific branches ( Supplementary information S1 , S2 (box, table)), as shown in the tree panel. The tree includes a subset of relevant branches, with those showing evidence of episodic positive selection represented with thick lines and red dots. Branch colours indicate the strength of selection (dN/dS): red indicates positive selection (dN/dS>5); blue indicates purifying selection (dN/dS=0); and grey indicates neutral evolution (dN/dS=1). Human residues that modify the binding energy if they are replaced with their hamster counterparts are labelled 45 . One of these (Val341) is positively selected (orange). A three amino acid deletion in bats is shown in green (see Supplementary information S1 , S2 (box, table)).

Similarly to SARS-CoV, MERS-CoV is thought to have originated in bats and to have spread to humans via an intermediate host, possibly dromedary camels 43 . Infection is initiated by binding of the MERS-CoV spike protein to human dipeptidyl peptidase 4 (DPP4; also known as CD26) 44 . Recent data indicate that five amino acids in DPP4 that differ between humans (MERS-CoV susceptible) and hamsters (non-susceptible) are key determinants for host specificity 45 ( Fig. 2 ). We extended a previous evolutionary analysis of mammalian DPP4 (Ref. 46 ): strong evidence of positive selection was found with episodic selection in the Vespertilionidae bat family and the panda and ferret branches, as well as in the dog lineage ( Fig. 2 ; see Supplementary information S1 , S2 (box, table)). As shown in Fig. 2 , most positively selected sites are located at the DPP4–spike protein interaction surface 47 , and one of these is among those described as binding determinants 45 . Thus, as observed for ACE2, MERS-CoV and related viruses (for example, coronavirus HKU4) are likely to act as drivers of molecular evolution on mammalian DPP4 genes; it will be especially interesting to evaluate the contribution of positively selected sites in ferrets because these animals are resistant to MERS-CoV infection.

Detecting and fighting infections

Immune responses in mammals are highly coordinated processes involving multiple systems that sense infection, activate antiviral and antimicrobial responses, and trigger adaptive immunity. The evolutionary history of several such systems has been analysed in detail, and below we describe the most recent findings.

Innate immune receptors. The mammalian immune system is endowed with a repertoire of molecular sensors called pattern-recognition receptors (PRRs). These molecules detect pathogen-associated molecular patterns (PAMPs) and initiate a downstream signalling cascade that culminates in the production of cytokines and antimicrobial factors. The main families of PRRs include TLRs, NOD-like receptors (NLRs), RIG-like receptors (RLRs) and AIM2-like receptors (ALRs). In the host–pathogen arms race, these molecules represent one of the foremost detection–defence systems; consistently, several studies have reported adaptive evolution at genes encoding mammalian PRRs.

Analyses in primates, rodents and representative mammalian species indicate that positive selection shaped nucleotide diversity at most TLRs, with the strongest pressure acting on TLR4 (Refs 35 , 48 , 49 ). Similarly to TLR4 ( Fig. 1 ), several positively selected sites in other TLRs are located in PAMP-binding regions, raising questions as to whether species-specific host–pathogen co-evolution is occurring, and how these sequence changes translate into differential PAMP recognition. In fact, as mentioned above for LPS, species-specific differences in ligand binding by TLRs seem to be common and potentially affect the overall immune response to specific pathogens 50 . Integration of evolutionary, immunological and genetic studies will be instrumental in the future for medical applications, especially in light of the proposed use of TLR ligands as vaccine adjuvants, a step that may require tailoring to distinct species 50 .

Compared with TLRs, mammalian ALRs are much less conserved and more dynamic, with distinct species carrying different sets of functional genes (ranging from 13 in mice to none in some bats) 37 , 51 . As a consequence, analysis of several mammals indicated that, with the exception of absent in melanoma 2 ( AIM2 ), which is non-functional in several species, no unequivocal orthologues can be inferred for the remaining ALR genes. This prevents the application of standard codon-based tests across the entire mammalian phylogeny, although closely related species can be analysed. Thus, interferon-γ-inducible protein 16 ( IFI16 ) and AIM2 were shown to have evolved under positive selection in primates. Positively selected sites were observed to mainly localize near to regions or domains involved in DNA binding and protein–protein interaction, suggesting modulation of substrate specificity or genetic conflicts with viral inhibitors 52 . Positive selection was also described for the three mammalian RLRs (retinoic acid-inducible gene I ( RIGI ; also known as DDX58 ), melanoma differentiation-associated 5 ( MDA5 ; also known as IFIH1 ) and LGP2 (also known as DHX58 )), the primate NLR family apoptosis inhibitory protein ( NAIP ) and rodent Naip2 genes 53 , 54 . Indeed, as is the case for ALRs, rodents have multiple NAIP paralogues that show widespread evidence of inter-locus recombination. This led to the application of a dN / dS sliding window approach: the Naip2 sites evolving with dN/dS >1 were found to be located in the bacterial ligand domain 54 .

Antiviral effectors and restriction factors. Studies on antiviral restriction factors have been extensive because these molecules represent obvious targets in host–pathogen arms races. Specifically, genetic conflicts between host restriction factors and viral components often play out in terms of binding-seeking dynamics (the host factor adapts to bind the viral component) and binding-avoidance dynamics (the virus counter-adapts to avoid binding and restriction by the host factors). The evolutionary history of antiviral restriction factors has been comprehensively reviewed elsewhere 55 , 56 , 57 , and we only highlight a few recent developments here.

The first restriction factor to be identified was the product of the mouse gene Friend virus susceptibility 1 ( Fv1 ), a protein that protects against murine leukaemia virus (MLV) infection 58 . The origin and evolution of FV1 is extremely interesting: early sequence analysis revealed that it derives from the gag gene of an ancient endogenous retrovirus that is not directly related to MLV 58 . Thus, FV1 exemplifies a paradoxical twist of the arms race scenario whereby a viral gene is co-opted by the host to serve an antiviral function (this is not the only instance, see Ref. 59 ). Recent results showed that the Fv1 gene was inserted into the mouse genome between 4 million and 7 million years ago, long before the appearance of MLV. Thus, the selective pressure exerted by other viruses must have maintained FV1 function and driven its evolution 60 . Indeed, analysis of FV1 from wild-type mice indicates that different Fv1 products can recognize and block multiple genera of retroviruses (for example, equine infectious anaemia virus and feline foamy virus), and a number of positively selected sites in the carboxy-terminal region of FV1 are directly involved in restriction specificity 60 . Thus, in a similar way to TRIM5, FV1 was identified for its ability to restrict an extant virus, but its evolution was driven by different waves of retroviral species, some of which are likely to be extinct.

Other restriction factors that have been the topic of recent investigation are encoded by two paralogous genes, myxovirus resistance 1 ( MX1 ; also known as MxA ) and MX2 (also known as MxB ). The protein products of the two genes display high sequence identity but different antiviral specificity. MX1 has broad activity against RNA and DNA viruses. Recently, Mitchell and collaborators 61 showed the potential of evolutionary analyses to generate experimentally testable hypotheses on the nature of genetic changes that affect species-specific susceptibility to infection. The authors applied an evolution-guided approach and identified a cluster of positively selected residues in an unstructured surface-exposed MX1 loop (loop 4), which confers antiviral specificity; genetic variation in loop 4 is a major determinant of MX1 antiviral activity against Thogoto and avian influenza A viruses, and replacements at a single positively selected site alter the ability of MX1 to restrict these pathogens 61 .

More recently, the selection pattern at the MX2 gene, which encodes an antiretroviral effector 62 , was shown to parallel that of MX1, with most selected sites located in loop 4 (Ref. 63 ). In MX2, sites selected in the primate lineage were detected outside loop 4, and MX1 also showed evidence of selection in other domains 61 , 63 ; these sites are promising candidates for being additional determinants of antiviral activity.

Antigen presentation, T cell activation and immunoglobulin G receptors. Antigen presentation and the ensuing T cell activation are central processes in mammalian cell-mediated immune response ( Fig. 3 ). Therefore, a convenient strategy for pathogens to elude immune surveillance is to hijack the molecular pathways responsible for these processes 64 , 65 . In line with the arms race scenario, there is evidence of positive selection at several mammalian genes involved in antigen presentation and the regulation of T cell activation 66 , 67 ( Fig. 3 ). The pathogen-driven mechanisms underlying evolution at these genes are likely to be manifold. One mechanism is genetic conflict with a pathogen-encoded component, evidence of which can be seen in the protein CD86. Positively selected sites in the transmembrane and juxtamembrane region of CD86 interact with MIR2 ( Fig. 3 ), a Kaposi sarcoma-associated herpesvirus (KSHV) protein that downmodulates CD86 expression 67 , 68 . A second mechanism is the use of cell-surface molecules as viral receptors: some adenovirus strains, for example, have been reported to exploit CD80 and CD86 for cellular attachment 69 , 70 . A third mechanism is the broadening or tuning of the host's ability to process and present pathogen-derived components. For example, a positively selected site in the carbohydrate-recognition domain of CD207 (also known as langerin; a Birbeck granule molecule) affects an amino acid position that is directly involved in the binding of pathogen-derived glycoconjugates 71 .

All pathway components are designated using official gene names (excluding the major histocompatibility complex (MHC) and T cell receptor (TCR)) and are highlighted in red if they are targets of positive selection in mammals or primates 25 , 66 , 67 . The molecular components of different antigen processing and presentation pathways are shown (details from Refs 107 , 108 ) to provide a general overview of the extent of positive selection and to highlight the function of positively selected genes, as most of their protein products directly interact with the antigen. Thus, the figure is not meant to show all molecules involved in the process or to convey mechanistic insights. Also, some genes may show tissue-specific expression or may be induced under specific circumstances: their products are nonetheless included for the sake of completeness. As for T cell regulatory molecules, the representation does not reflect the stoichiometry of binding (for example, CD28 functions as a dimer). Notably, the same molecule may be expressed by different populations of T cells, although here each molecule is shown on one T cell type only (to avoid redundancy). The dashed arrows and '?' indicate steps that lack clear molecular definition or are only inferred. The orange circles, and red and blue shapes at the bottom of the figure represent proteolytic fragments. B2M, β2-microglobulin; BLMH, bleomycin hydrolase; CALR, calreticulin; CD40LG, CD40 ligand; CTLA4, cytotoxic T lymphocyte protein 4; CTS, cathepsin; CYB, cytochrome b; ERAP, endoplasmic reticulum aminopeptidase; HAVCR2, hepatitis A virus cellular receptor 2; HLA-DM, major histocompatibility complex, class II, DM; ICOS, inducible T cell co-stimulator; ICOSLG, ICOS ligand; IFI30, interferon-γ-inducible protein 30; iNKT, invariant natural killer T; iTCR, invariant TCR; LGMN, legumain; LNPEP, leucyl-cystinyl aminopeptidase; NCF, neutrophil cytosol factor; NPEPPS, puromycin-sensitive aminopeptidase (also known as PSA); NRD1, nardilysin; PDCD1, programmed cell death 1; PDCD1LG2, programmed cell death 1 ligand 2; PDIA3, protein disulfide-isomerase A3; ROS, reactive oxygen species; TAP, antigen peptide transporter; TAPBP, TAP-binding protein (also known as tapasin); THOP1, thimet oligopeptidase 1; TPP2, tripeptidyl-peptidase 2.

These mechanisms are not mutually exclusive. For example, a plethora of viral pathogens (such as herpes simplex virus 1, human papillomavirus, HIV-1 and KSHV) interfere with CD1D trafficking and recycling 72 , 73 . As a consequence, the cytoplasmic and transmembrane regions of CD1D display positively selected sites, one of which is within a primate-specific trafficking signal. Additional positively selected sites are located in the CD1D extracellular region and flank the T cell receptor interaction surface and the lipid-binding pocket, which suggests that they exert an effect on antigen-binding specificity 67 .

Finally, we draw attention to one of the few attempts at assessing the part that helminth infections have played as selective pressures for mammals and at integrating epidemiological information into molecular evolutionary approaches. Machado and co-workers 74 found evidence of positive selection at the mammalian gene Fc fragment of IgG, low affinity IIIb, receptor ( FCGR3B ), which is expressed by eosinophils and is involved in the binding of immunoglobulin G (IgG)-coated parasites. Notably, the authors also tested a specific hypothesis whereby mammalian lineages hosting a wider range of helminth species should show stronger evidence of selection compared with other species (this was accomplished by running the phylogenetic analysis by maximum likelihood (PAML) branch-site models with helminth-rich lineages as foreground branches 74 ; Box 3 ). Their hypothesis was verified, providing a plausible explanation for the evolutionary pattern at FCGR3B and suggesting that similar approaches may be used to detect other mammalian genes involved in genetic conflicts with helminth parasites.

Examples other than immune effectors

As exemplified by ACE2, host–pathogen interactions are not limited to immune system components. The reasons why genes with no specific defence function may be targeted by the selective pressure imposed by infectious agents are manifold. The best known instances probably refer to gene products that act as incidental receptors for pathogens, as is the case with ACE2. Other host gene products that engage in genetic conflicts include those that participate in the coagulation cascade and the contact system, which are commonly hijacked by bacterial pathogens to promote tissue invasion or to elude detection by immune cells (see Ref. 75 for a review). An alternative possibility is that the host builds a line of defence based on the sequestration of essential micronutrients from the pathogen, a phenomenon known as 'nutritional immunity'.

Housekeeping genes. Incidental receptors are often represented by the products of housekeeping genes, which are typically expressed at high levels by different cell types. Among these, the transferrin receptor ( TFRC ) gene encodes a cell-surface molecule (transferrin receptor protein 1 (TFR1)) that is essential for iron uptake. TFR1 is used as a receptor by mouse mammary tumour virus, canine parvovirus and rodent New World arenaviruses. In line with the arms race scenario, TFRC evolved adaptively in rodents and caniforms, and positively selected sites are mainly located in the extracellular domain regions that interact with rodent-infecting arenaviruses ( Fig. 1 ) and carnivore-infecting parvoviruses, respectively 76 , 77 . Interestingly, positive selection at the primate transferrin ( TF ) gene, which encodes the TFR1 ligand, was also recently described 78 ; in this case, selection is driven by bacteria, not viruses 78 . Transferrin is the major circulating iron transporter in mammals and is also thought to participate in nutritional immunity by sequestering iron from bacteria. Consistently, most positively selected sites were found to have evolved to counteract binding by bacterial transferrin surface receptors that scavenge host iron 78 . Thus, different selective pressures exerted by distinct molecular mechanisms contributed to shaping the evolution of a central homeostatic process — in this case, iron transport in mammals.

Another housekeeping gene product that acts as a viral receptor is Niemann–Pick C1 protein (NPC1), a sterol transporter located in the membrane of late endosomes and lysosomes. NPC1 is expressed by most cell types and is used by filoviruses (such as EBOV and Marburg virus). Evolutionary analysis of mammalian NPC1 genes indicated that three positively selected residues are located in the amino-terminal portion of the second NPC1 luminal loop; binding of this loop by the EBOV glycoprotein (GP) is necessary and sufficient for the viral receptor activity of the sterol transporter 79 , 80 ( Fig. 4 ). The second luminal loop of NPC1 is also bound with high affinity by the GP encoded by Lloviu virus, a bat-derived, EBOV-like filovirus 81 . Thus, NPC1 may represent a universal receptor for filoviruses, and the constant selective pressure exerted by such viruses might have greatly contributed to shaping mammalian genetic diversity at loop 2. These data may have great and immediate practical values. In fact, small molecules that directly target NPC1 and disrupt GP binding are regarded as possible therapeutic compounds against EBOV 82 , 83 , 84 ( Fig. 4 ). Because mammalian NPC1 diversity at the interaction surface is driven by selection, future efforts in this direction are likely to benefit from the incorporation of evolutionary analysis; this would be especially important when testing therapeutic molecules on model organisms and non-human mammals. In humans, mutations in NPC1 cause Niemann–Pick disease type C1, a progressive neurodegenerative condition. This is in line with the central role of this transporter in housekeeping functions; thus purifying selection . is expected to constrain variation in the gene. Indeed, the human–mouse dN/dS calculated for the NPC1 whole-gene region is definitely lower than 1, as is the case for most genes ( Fig. 4 ). In fact, mammalian NPC1 genes show a large preponderance of codons evolving with dN/dS <1, and positive selection is extremely localized in loop 2 ( Fig. 4 ). This specific example illustrates a general concept, whereby molecules involved in central homeostatic processes may be engaged in genetic conflicts with pathogens, although in several instances the sequence space accessible for adaptive mutation without a high fitness cost is expected to be limited.

a | Distribution of dN/dS values for human–mouse one-to-one orthologues. The values for some of the genes discussed in this Review are indicated. Data were derived from the Ensembl BioMart database (see Further information). b | Natural selection acting on mammalian Niemann–Pick C1 (NPC1) genes. NPC1 is shown with its predicted membrane topology and protein regions coloured in hues of blue that represent the percentage of negatively selected sites (as detected by the single-likelihood ancestor counting method using Datamonkey ); the darker the blue, the higher the percentage. The location of three positively selected residues (red) is indicated on the left, and an alignment of the corresponding region is shown on the protein to the right (with red and blue representing positively and negatively selected sites, respectively). The interaction with the glycoprotein (GP; green) of filoviruses (such as Ebola virus, Marburg virus or Lloviu virus) is shown. GP binds NPC1 after processing by cellular proteases. ACE2 , angiotensin-converting enzyme 2; DARC , Duffy blood group, atypical chemokine receptor; MX1 , myxovirus resistance 1; SSD, sterol-sensing domain; TFRC , transferrin receptor; TLR4 , Toll-like receptor 4.

The coagulation cascade and contact system. As anticipated above, several components of the coagulation cascade and contact system evolved adaptively in mammals, most likely as a result of genetic conflicts with bacterial pathogens 85 , 86 . For instance, Staphylococcus aureus is endowed with an arsenal of proteins that target such systems, including two cysteine proteinases (ScpA and SspB) that cleave plasma kininogen at each terminal side of the bradykinin domain to generate kinins, with a consequent increase of vascular leakage 87 . These events are central for bacterial virulence and are linked to the pathogenesis of sepsis. In kininogen 1 ( KNG1 ), positively selected sites are located in all domains, with the exception of the highly conserved bradykinin region 85 . One of the positively selected sites defines the N-terminal cleavage site of ScpA and SspB, suggesting that sites flanking the bradykinin sequence are evolving to avoid recognition and cleavage by bacterial-encoded proteases. In analogy to the strong purifying selection acting on the bradykinin region, analysis of calculation cascade genes indicated that disease-causing mutations are more likely to occur at sites targeted by purifying selection and are rarer at positively selected sites 86 . Again, these observations highlight the coexistence of distinct selective regimes at the same gene regions and exemplify the concept of evolutionary trade-offs.

Conclusions

The advent of high-throughput sequencing technologies has allowed for the generation of an unprecedented wealth of genetic data, including the whole-genome sequences of host reservoir species for human pathogens, as well as genetic information for multiple microbial and viral species and strains. Moreover, large-scale approaches such as RNA interference and mass spectrometry are providing detailed pictures of host–pathogen interactomes 88 , 89 . Finally, an increasing number of crystal structures of interacting host and pathogen proteins solved in complex are available, allowing the opportunity to determine the structural basis of these interactions to identify regions or amino acids that lie at the host–pathogen contact surface. Integration of these data with evolutionary analysis will allow the testing of specific hypotheses, including which species have responded to the pressure exerted by one or more pathogens (see the SARS-CoV example), which molecules and residues have participated in the arms race and which host–pathogen interacting partners are expected to have co-evolved. These advances are also expected to progressively change evolutionary genetics from a hypothesis-driven to a hypothesis-generating discipline. In this respect, we note that although the arms race scenarios we have described in this Review imply some form of host–pathogen co-evolution over time, the nature of the interaction and its dynamics have often been inferred from the observed pattern of variation. Indeed, the fact that the same residues that affect specific host–pathogen interactions are targeted by positive selection does not necessarily imply a causal link, and in many instances the specific selective agents underlying molecular adaptations remain to be determined. As shown above, these may well be accounted for by extinct pathogens or by agents that had a major co-evolutionary role in the past but that are now fading away from the landscape of common infections. With a few exceptions 16 , 24 , evolutionary studies only investigate extant genetic variation and modern pathogens, with little reconstruction of past events. Nevertheless, we do not necessarily need to go back in time: evolutionary analyses can be used as predictive tools to pinpoint which genes and residues are more likely to contribute to present-day host–pathogen interaction and help explain species-specific susceptibility to infection. Several studies mentioned above, including those investigating selection at MX1 (Ref. 61 ), TFRC (Refs 76 , 77 ), TF 78 and other protein-coding genes 23 , 24 , 54 , 60 , used experimental analyses to show that evolutionary information can indeed be exploited to gain high-resolution insight into the molecular determinants of binding affinities at host–pathogen interfaces.

The studies of iron transporters 78 hold particular value because the authors analysed the genetic variability of both the host and the pathogen and showed that both parties evolved in response to mutually exerted pressures, in line with the Red Queen principles. So far, few attempts have been made at integrating evolutionary analyses of host and pathogen interacting partners into a common framework. However, efforts in this direction hold the promise of improving our understanding of the strategies used by both hosts and pathogens to adapt and counter-adapt. In turn, this knowledge has possible biomedical and therapeutic implications, given the ability of different pathogens or distinct strains of the same infectious agent to elude not only natural host defences but also drugs and vaccination strategies.

As a final note, we mention that we have exclusively focused on adaptive events involving coding gene regions. Nevertheless, several recent studies (see Ref. 10 for a review) have highlighted the role of non-coding variants as important determinants of susceptibility to infection within species. Thus, host–pathogen conflicts are more than likely to have contributed to adaptive evolution at regulatory elements during speciation. Detection of these adaptive events will benefit from the availability of high-throughput techniques (for example, RNA sequencing and chromatin immunoprecipitation followed by sequencing) and the development of methodological approaches for dissecting molecular evolution in non-coding regions; notably, recent data have shown the usefulness of a framework similar to dN/dS to analyse the evolutionary history of mammalian transcriptional enhancers 90 . Application of this methodology (or extensions thereof) to the study of host–pathogen interactions will provide valuable information on which non-coding sequence changes have been targeted by selection and thus modulate susceptibility to infection or related phenotypes.

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Acknowledgements

D.F. is supported by fellowships of the Doctorate School of Molecular Medicine, University of Milan, Italy.

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Manuela Sironi, Rachele Cagliani & Diego Forni

Department of Physiopathology and Transplantation, University of Milan, Milan, 20090, Italy

Mario Clerici

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Powerpoint slide for fig. 1, powerpoint slide for fig. 2, powerpoint slide for fig. 3, powerpoint slide for fig. 4, supplementary information, supplementary information s1 (box).

Evolutionary analysis of mammalian DDP4 . (PDF 2101 kb)

Supplementary information S2 (table)

LRT statistics for DPP4 . (PDF 260 kb)

The accumulation of favourable amino acid-replacing substitutions, which results in more non-synonymous changes than expected under neutrality (dN/dS > 1).

Positive selection localized to a subset of sites or confined to a few species in a phylogeny.

Genes that evolved from a common ancestral gene through speciation.

Homologous genes created by a duplication event within the same genome.

The observed number of non-synonymous substitutions per non-synonymous site.

The observed number of synonymous substitutions per synonymous site.

The elimination of deleterious amino acid-replacing substitutions, which results in fewer non-synonymous changes than expected under neutrality (dN/dS < 1).

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Sironi, M., Cagliani, R., Forni, D. et al. Evolutionary insights into host–pathogen interactions from mammalian sequence data. Nat Rev Genet 16 , 224–236 (2015). https://doi.org/10.1038/nrg3905

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Published : 18 March 2015

Issue Date : April 2015

DOI : https://doi.org/10.1038/nrg3905

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