Corresponding author: Thomas Lenormand ( thomas.lenormand@cefe.cnrs.fr ) Academic editor: Stephane Boyer
© 2018 Thomas Lenormand, Noémie Harmand, Romain Gallet.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Lenormand T, Harmand N, Gallet R (2018) Cost of resistance: an unreasonably expensive concept. Rethinking Ecology 3: 51-70. https://doi.org/10.3897/rethinkingecology.3.31992
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The concept of “cost of resistance” has been very important for decades, for fundamental reasons (theory of adaptation), with a wide range of applications for the genetics and genomics of resistance: resistance to antibiotics, insecticide, herbicide, fungicides; resistance to chemotherapy in cancer research; coevolution between all kinds of parasites and their hosts. This paper reviews this history, including latest developments, shows the interest of the idea but also challenges the usefulness and limits of this widely used concept, based on the most recent development of adaptation theory. It explains how the concept can be flawed and how this can impede research efforts in the field of resistance at large, including all applied aspects. In particular, it would be clearer to simply measure the fitness effects of mutations across environments and to better distinguish those effects from ‘pleiotropic effects’ of those mutations. Overall, we show how to correct the concept, and how this correction helps to better understand the wealth of data that has accumulated in recent years. The main points are:
1. The concept of «cost of resistance» needs to be carefully used, to avoid misconceptions, false paradox and flawed applications. The recent developments in adaptation theory are useful to clarify this.
2. “Cost of resistance” and pleiotropy have to be distinguished. More than one trait is required to discuss pleiotropy. Resistance evolution must at least involve the modification of one trait. If there is an irreducible trade-off on that trait between environments with and without drug, it creates a fitness effect that is not due to pleiotropy. Pleiotropic effects can, but need not, occur in addition.
3. “Cost of resistance” must depend on the pair of environments considered with and without drug. Hence, there are as many measures of cost as there are environments without drug. If the focal genotype is not well adapted to one focal environment, it is relatively easy to observe “negative” costs of resistance. There is nothing surprising about this, and it does not indicate an absence of trade-off.
4. Environments with drug can differ according to the dose. It may be more informative to measure the possible trade-offs among all doses than to focus exclusively on the fitness contrast between the presence and the absence of drug.
Pleiotropy, fitness, adaptation, drugs, parasites, evolution, genetics, environment
The study of resistance to antibiotics, insecticides, acaricides, fungicides, herbicides, chemotherapy drugs etc. is, for obvious reasons, a very active field of research. We are in the middle of a “crisis,” which has important consequences for public health and agriculture (e.g.
Resistance evolution is a particular case of the more general situation of adaptation to new environmental conditions. Processes of adaptation have been intensely studied from Darwin’s time, since refined by powerful population genetic concepts that have been put forward in the modern synthesis (
This point leads to a very simple and obvious idea: the selective effects of mutations depend on ecological conditions. This conclusion is somewhat trivial (
In presence of such heterogeneity with both treated and non-treated environments, the concept of ‘cost of resistance’ becomes important. This cost is defined as the selection coefficient of resistance mutations in absence of treatments (or similarly in absence of predator, parasite or pathogen when considering resistance in the context of biotic interactions). The idea of a “lower adaptive value” of resistant genotypes in the absence of treatment can be traced back quite far (e.g. in
A likely reason is that the concept was helpful to bring attention to the fact that a mutation could be both beneficial or deleterious depending on circumstances, something well known in ecological genetics but somewhat ignored in resistance studies. It helped introduce some ecology in the understanding of the fitness effect of resistance mutations. This can have important consequences as the cost of resistance is a powerful force that can keep resistance in check (see e.g.
A second reason is that the concept of cost was tightly associated to the notion of trade-off among traits, an idea borrowed from life history theory. When resistance is viewed as a trait, or a “defense” function, it is natural to consider that it may trade-off with other organismal traits and functions (e.g. in terms of resource). In other words, resistance mutations should influence many traits, i.e. have pleiotropic effects. Besides resistance, the variation of all these traits is likely to be deleterious, and therefore represent a ‘cost’, since these traits were previously optimized by natural selection. This connection with life history theory is entirely explicit in the first papers mentioning the concept (
Today, the term ‘cost of resistance’ is widely used, but the concept suffers from several ambiguity that can be understood in the light of this short history. First, the concept seems unnecessary to study adaptation in different environments (it would be sufficient to simply consider fitness effects in each environment). It also introduces an asymmetry, which is quite arbitrary, and somewhat misleading (susceptibility too is costly). Second, it conflates effects across traits (pleiotropy) and effects across environments, which can also be misleading. Third, the word ‘cost’, still reflects an essentialization of mutation/genotypes. The deeply engrained view “one mutation – one fitness effect” was not really challenged by the introduction of the ‘cost’ idea. It was merely replaced by the idea that one resistance mutation corresponded to two important characteristics: its benefit and its cost.
For these reasons, we think that the concept of cost of resistance presents important shortcomings, to the point, that it is now becoming a problem and hindrance to conceptually clarify the process of adaptation. We now try to explain these issues in more details.
The first problem with the concept of cost is its interpretation in terms of pleiotropic effects of mutations. To be very clear, a simple situation is sketched below where adaptation is represented by the optimization of many traits simultaneously, like in Fisher’s model of adaptation or multivariate models of stabilizing selection in quantitative genetics (
Graph of treated and non-treated environments, with distinct phenotypic requirements (phenotypic optima A and O, respectively) in a two-trait space. Assuming a wild-type positioned in O, the resistance mutation R brings the phenotype closer to A. Relative to the wild type, R is therefore a beneficial mutation in the treated environment. Its cost is usually defined as its fitness in the non-treated environment relative to the wild type, which depends on the distance between R and O on the figure. Note that the cost (and all fitness measures on this figure, and similar figure below) depends on Euclidian distances in phenotypic space, and a mapping function converting this distance to fitness (i.e. the cost is not distance OR, but a monotonic function of this distance). The mapping is left implicit on the figure, but can be thought as a third orthogonal axis representing fitness for each trait 1 – trait 2 combination, which defines a “fitness landscape”. The coloured inset figure represents such a fitness mapping in 3D. Fitness values, when projected on the phenotypic space correspond to isofitness curves (like altitude on a geographic map is indicated by contour lines). For instance all phenotypes on the light grey circles have the same fitness than mutation R in the treated environment (optimum A). The direction of the two optima (OA axis) defines a phenotypic trait of ‘resistance’. Variation of trait(s) orthogonal to this axis may be defined as pleiotropic effects. Point P1 on OA axis is such that AR = AP1. It represents the phenotypic point that would confer the same fitness in the treated environment compared to the mutation R, but that would only alter the phenotype in the exact direction of the optimum A. Point P2 is the orthogonal projection of R on OA axis. It represents the phenotypic point that would be reached if all the pleiotropic effects of the mutation R were compensated (e.g. by subsequent compensatory mutations).
Correspondence between a fitness landscape model and a dose-response model. On the left panel, a fitness landscape model is illustrated as in Figure
Let us label “O” the optimal phenotype in the non-treated environment and “A” the phenotypic optimum in the treated environment. For simplicity, we can assume that fitness monotonically declines with the (Euclidean) distance from the peak in any given environment. Again, this simplified model could be more specific (with a particular mapping of distance to fitness) or complex but the core argument does not require the use of more complex situation or assumptions. We can also assume that the wild type is very close to O, as one would expect from the effect of past selection in absence of drug, and represent a resistance mutation by a vector pointing from O to R, where R is a phenotype closer to A than to O. The mutant is beneficial (relative to the wild type) in the treated environment because the distance AR is smaller than the distance AO. The difference between these two distances scales with the selection coefficient of the resistance mutation in the treated environment. The OA axis, by definition, represents the phenotypic direction of the ‘resistance’ phenotype. Point P1 on this axis such that AR = AP1 allows representation of the phenotypic point that would confer the same benefit in the treated environment compared to our resistance mutation R, but that would only alter the phenotype in the exact direction of the optimum A. In other words, the distance OP1 scales with the selective advantage of the resistance mutation in the treated environment, relative to the wild type. The cost of resistance depends on the distance OR, as it is defined as the fitness effect of the resistance mutation in the non-treated environment (again, relative to the wild type). What about the pleiotropic effects? Here, the OA axis represents the phenotypic axis of resistance and therefore, the orthogonal direction represents all other traits (here, there is only one other trait, because we consider only a two dimensional trait space but with n phenotypic traits, there would be n - 1 such traits). Hence, the ‘other’ pleiotropic effects all project on these axes orthogonal to OA. Furthermore, note the point P2, the projection of point R on the OA axis. The vector RP2 represents the pleiotropic effects of the resistance mutation. Should these effects be totally compensated, the phenotype would be in P2 and it would indeed enjoy a greater fitness in both the treated and non-treated environments (since AP2 < AR=AP1 and OP2 < OR, respectively).
This simple geometric argument indicates several things. First, pleiotropic effects and the ‘cost of resistance’ are two different things – biologically and geometrically – contrary to what is usually considered. Pleiotropic effects will be eventually compensated through the well-known process of “amelioration” when the population reaches the phenotypic optimum after remaining exposed to the treated environment and involves new resistance mutations, compensatory mutations or a mixture of mutations with the two properties (
From an experimentalist perspective, defining a non-treated and treated environment is straightforward. You first select a given environment, then you can either add the drug or not. With this definition, it is possible to make a very clear and clean experiment demonstrating the effect of the drug, with a control. Yet, the problem is that there is virtually an infinite set of possible environments to start with. Which pair of treated/non-treated environments is relevant? This is difficult to know. It is difficult to represent the complexity of natural conditions in controlled experiment in the laboratory. Even trying to determine which environmental conditions corresponds to those an organism has been adapting to is challenging. Using ‘absolute’ demographic performance to answer this question may not be reliable. For instance, habitat quality varies and can even obscure the relationship between ‘absolute’ measures of fitness and environment variables (
In fact, it is quite straightforward to see that the cost of a mutation will be different in varying non-treated environments. There is not “a” cost, but as many costs as there are different (non-treated) environments (
Graph of treated and non-treated environments, with distinct phenotypic requirements (phenotypic optima A and B, respectively) in a two-trait space. Relative to a wild-type positioned in O, the resistance mutation R brings the phenotype closer to A. As in Figure
Such situation happens when RB ≤ OB i.e. when the wild-type and the resistance mutant are at least equally distant from the non-treated optimal phenotype. Because such situations are quite common (either because we cannot properly reproduce natural conditions experimentally, or because wild type genotypes are not well adapted to their environment), it is perhaps not very surprising that sometimes “cost free” resistance mutation or even “negative cost” are found. All these situations can occur but are unrelated to the pleiotropic effects of the resistance mutation (as defined in the previous section). Importantly, finding an absence of cost or even ‘negative costs’ do not indicate necessarily that there is no phenotypic trade-off between the treated and non-treated environment (and indeed, on the Figure
If resistance mutations cannot be defined by the fact that they are beneficial in the treated environment, relative to wild type (as we did up to now), then, how do we classify them? In fact, it might be possible to define them more specifically by saying that they are beneficial in the treated environment relatively to wild type, provided that the wild type is perfectly well adapted to the corresponding non-treated environment. In principle, this definition makes sense, as it avoids conflating adaptation to conditions that are shared by both treated and non- treated environments, with adaptation to the drug itself. Yet, with this definition, the mutation R illustrated on Figure
Sketch of treated and non-treated environments, with distinct phenotypic requirements (phenotypic optima A and B, respectively) in a two-trait space. Relative to a wild-type positioned in O, both the resistance mutation R and R’ brings the phenotype closer to A. As in Figure
The problem is that measures of fitness will be made against a wild-type and it may not be straightforward to determine whether this wild type is well adapted to the non-treated environment (but not impossible, as e.g. in cases of experimental evolution in the laboratory where the wild type can be chosen as the type that evolved in the non treated environment for a long time). Without this knowledge, it may be very difficult to classify mutations that are specifically beneficial in the treated environment (i.e. “resistance” mutations as defined here), versus mutations that are beneficial in both treated and non-treated environments (see e.g.
There is yet another difficulty lurking in the vast range of possible natural situations. Just as there are many non-treated environments, there may be many treated environments as well. In particular, any drug may be added in different quantities or concentrations to any given environment. Here resides a problem, which is rarely addressed in studies on resistance: different drug concentrations can correspond to different intensities of selection (
If different drug doses correspond to different optima (say A1 and A2 on Figure
Sketch of a non-treated environment (phenotypic optimum in B) and two treated environments, with different doses of drug (optima A1 and A2). Two mutations are illustrated, with fitness effects compared to a wild type well adapted to the non-treated environment (i.e. located at B). Using the definition from Figure
Taking into account the ‘cost of resistance’ has been a major progress because it is essential to distinguish the fitness effects of resistance mutations in treated versus non-treated environments. However, this cost is also very expensive conceptually as it is associated with too many simplifications, to the point that it may even be misleading. In practical terms, should the term ‘cost of resistance’ be avoided? The term is extremely widespread and in many cases, it has at least the merit of attracting attention to the fact that fitness effects are different in different environments. In most cases and depending on context, the more robust concepts of fitness trade-off across environments or pleiotropy could be used, although the concision of the expression “cost of resistance” will be difficult to match. We hope that this perspective will help correcting some of the sloppy usage of the term and dismiss the implicit expectations based on this terminology.
(1) The interpretation of “cost” in terms of pleiotropic effects is very unclear. Pleiotropic effects may be better defined as effects projected on phenotypic axes orthogonal to resistance phenotype, than in terms of fitness effect in the non-treated environment (see Figure
(2) There are as many costs as there are non-treated environments. The idea that there is one cost associated with a resistance mutation is an extreme and naïve simplification. In practice, this indicates that studying the fitness effects of resistance mutations “in the wild” (i.e. beyond the simplified laboratory conditions) is very important.
(3) Costs of resistance are ill defined when several precautions are not taken. For instance, failing to measure costs relative to a well-adapted wild type to the non-treated environment can lead to absurd notions, such as ‘negative costs’ that serve no conceptual clarification (Figure
(4) The concept of cost leads to an oversimplified and often erroneous view of trade-offs across environments. Finding costs equal to zero (i) cannot be used to say that there is no trade-offs between treated and non-treated environments; (ii) cannot be interpreted by saying that pleiotropic effects were compensated; (iii) completely misses the possibility that fitness trade-off may occur among different doses. In practice, finding a mutation with no “cost” (or a “negative cost”), should not be surprising. It does not prove the existence of “Darwinian demons”; it should not be used to imply that there are no trade-off across environments and therefore that resistance management strategies will necessary fail. As we have shown, this finding can simply result from a particular choice for the reference genotype and environment. Considering fitness effects across the range of possible doses is also important beyond the simplified conditions of most lab-based ecotoxicological tests and ecotoxicological fitness proxies such as LD50, MIC, dose responses etc. Importantly, finding that resistance mutations are not favourable at all doses cannot be attributed to their “cost”. For instance, high resistance mutations may not be beneficial at low doses, not because they have a high cost, but simply because they do not match phenotypic requirements that are optimal at low dose. Here again, the concept of cost leads in practice to biased expectations. Finally, observing that a resistance mutation does not decrease in frequency after an arrest of treatment, is not proving that there is “no cost” or no trade-off across environments. Other causes of frequency changes must be first investigated (i.e. effect of gene flow, effect of residual or hidden treatments, drift, indirect selection) as well as possible ascertainment biases (low power to detect slow frequency change).
(5) Overall, it may be safer in most cases to simply discuss and measure the fitness effects of mutation in different environments and to carefully consider the role of the reference genotype/phenotype when interpreting relative fitnesses. All these points hold for many other situations of adaptation besides resistance, but this is perhaps where the vocabulary and the conceptual issues are the most acute and widespread. Differences among selective conditions and the occurrence of pleiotropy are both important ideas in evolutionary ecology. However, they cannot be solely summarized by assigning resistance mutations a ‘benefit’ and ‘a cost’ and essentialize their properties. There is a problem with a reduction of evolutionary thinking to a cost-benefit thinking, with “fitness” as a universal currency, valid regardless of ecological conditions. Although fitness is a general concept and a universal currency for adaptation, different conditions entail different fitnesses and possibly different phenotypic requirements (different adaptations). The vocabulary that we use should not oversimplify these ideas.
Designed and wrote the manuscript: TL, NH, RG. Prepared the figures: TL.
Authors | Contribution | ACI |
---|---|---|
TL | 0.70 | 4.667 |
NH | 0.15 | 0.353 |
RG | 0.15 | 0.353 |
We thank Helen Alexander, Danna Gifford, Inês Fragata and Claudia Bank for comments on the manuscript. We thank G. Martin for insightful discussions. This paper was reviewed and recommended by Peer Community in Evolutionary Biology (https://bit.ly/2ExMgZg). This work was supported by the ANR grant SilentAdapt.