Let’s move beyond costs of resistance!
Cost of resistance: an unreasonably expensive concept
Recommendation: posted 29 May 2018, validated 03 June 2018
The increase in the prevalence of (antibiotic) resistance has become a major global health concern and is an excellent example of the impact of real-time evolution on human society. This has led to a boom of studies that investigate the mechanisms and factors involved in the evolution of resistance, and to the spread of the concept of "costs of resistance". This concept refers to the relative fitness disadvantage of a drug-resistant genotype compared to a non-resistant reference genotype in the ancestral (untreated) environment.
In their paper, Lenormand et al.  discuss the history of this concept and highlight its caveats and limitations. The authors address both practical and theoretical problems that arise from the simplistic view of "costly resistance" and argue that they can be prejudicial for antibiotic resistance studies. For a better understanding, they visualize their points of critique by means of Fisher's Geometric model.
The authors give an interesting historical overview of how the concept arose and speculate that it emerged (during the 1980s) in an attempt by ecologists to spread awareness that fitness can be environment-dependent, and because of the concept's parallels to trade-offs in life-history evolution. They then identify several problems that arise from the concept, which, besides the conceptual misunderstandings that they can cause, are important to keep in mind when designing experimental studies.
The authors highlight and explain the following points:
1. Costs of resistance do not necessarily imply pleiotropic effects of a resistance mutation, and pleiotropy is not necessarily the cause of fitness trade-offs.
2. Any non-treated environment and any treatment dose can result in a different cost.
3. Different reference genotypes may result in different costs. Specifically, the reference genotype has to be "optimally" adapted to the reference environment to provide an accurate measurement of costs.
Lenormand et al.'s paper  is a timely perspective piece in light of the ever-increasing efforts to understand and tackle resistance evolution . Although some readers may shy away from the rather theoretical presentation of the different points of concern, it will be useful for both theoretical and empirical readers by illustrating the misconceptions that can arise from the concept of the cost of resistance. Ultimately, the main lesson to be learned from this paper may not be to ban the term "cost of resistance" from one's vocabulary, but rather to realize that the successful fight against drug resistance requires more differential information than the measurement of fitness effects in a drug-treated vs. non-treated environment in the lab [3-4]. Specifically, a better integration of the ecological aspects of drug resistance evolution and maintenance is needed , and we are far from a general understanding of how environmental factors interact and influence an organism's (absolute and relative) fitness and the effect of resistance mutations.
 Lenormand T, Harmand N, Gallet R. 2018. Cost of resistance: an unreasonably expensive concept. bioRxiv 276675, ver. 3 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/276675
 Andersson DI and Hughes D. Persistence of antibiotic resistance in bacterial populations. 2011. FEMS Microbiology Reviews, 35: 901-911. doi: 10.1111/j.1574-6976.2011.00289.x
 Chevereau G, Dravecká M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach T. 2015. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS biology 13, e1002299. doi: 10.1371/journal.pbio.1002299
 Bengtsson-Palme J, Kristiansson E, Larsson DGJ. 2018. Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews 42: 68–80. doi: 10.1093/femsre/fux053
 Hiltunen T, Virta M, Laine AL. 2017. Antibiotic resistance in the wild: an eco-evolutionary perspective. Philosophical Transactions of the Royal Society B: Biological Sciences 372: 20160039. doi: 10.1098/rstb.2016.0039
Inês Fragata and Claudia Bank (2018) Let’s move beyond costs of resistance!. Peer Community in Evolutionary Biology, 100052. 10.24072/pci.evolbiol.100052
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.
Evaluation round #1
DOI or URL of the preprint: https://doi.org/10.1101/276675
Version of the preprint: 1
Author's Reply, 17 May 2018
Decision by Inês Fragata and Claudia Bank, posted 17 May 2018
This Perspective was reviewed by 3 external reviewers, with whom l agree that a criticism of the misleading use ot the term "costs of resistance" is a timely issue and that this manuscript can be of relevance for both empirical and theoretical studies. However, all reviewers provided excellent suggestions that would allow for the manuscript to reach a larger target audience and that would improve its clarity. Most importantly, all reviewers suggest that the link to empirical studies and the implication for such studies needs to be more developed, and that there should be more concrete suggestions on on how to move beyond the term cost both theoretically and empirically. The reviewers also provide several interesting references that may complement the literature review provided by the authors.
Although we appreciate Reviewer 3's concern that complementing the existing discussion via Fisher's Geometric model (FGM; which should indeed be defined to the "naive" reader) by a discussion of models based on dose-response curves may be illustrative and helpful for readers less familiar with FGM, we feel that this may go beyond the scope of the current manuscript. However, this alternative and commonly considered model of fitness effects across environments should be discussed.
For people unfamiliar with FGM, it could also be helpful to indicate the important aspects of the model in each figure directly, i.e., "Optimum AB-" instead of "O", etc.