Antimalarial resistance is a major hurdle to malaria eradication efforts. The spread of drug resistance follows basic evolutionary principles, with competitive interactions between resistant and susceptible malaria strains being central to the fitness of resistant parasites. These competitive interactions can be used to design resistance management strategies, whereby a fitness cost of resistant parasites can be exploited through maintaining competitive suppression of the more fit drug-susceptible parasites. This can potentially be achieved using lower drug dosages or lower frequency of drug treatments. This approach has been demonstrated to work empirically in a rodent malaria model [1,2] and has been demonstrated to have clinical success in cancer treatments [3]. However, these resistance management approaches assume a fitness cost of the resistant pathogen, and, in the case of malaria parasites in general, and for artemisinin resistant parasites in particular, there is limited information on the presence of such fitness cost. The best suggestive evidence for the presence of fitness costs comes from the discontinuation of the use of the drug, which, in the case of chloroquine, was followed by a gradual drop in resistance frequency over the following decade [see e.g. 4,5]. However, with artemisinin derivative drugs still in use, alternative ways to study the presence of fitness costs need to be undertaken. 
There are several good in vitro studies demonstrating artemisinin resistant parasites being competitively suppressed by wildtype parasites [see e.g. 6–9], however, these have the limitation that they will only be able to detect the fitness cost during the blood stage of the infection and in an artificial environment. So far, there have not been animal models that have thoroughly studied the presence of resistance fitness costs for artemisinin resistant parasites. Moreover, in these types of studies, the focus is mostly on the fitness cost as detected in the vertebrate host. However, malaria parasites spent a significant portion of their life cycle in the mosquito host, where fitness costs could also be expressed. Overall, it is the fitness over the entire life cycle of the parasite that would determine if and to what extent a reduction in resistance frequency would be observed when the use of a drug is stopped. 
Here, Villa and colleagues present a study to quantify such fitness cost of artesunate-resistant parasites, not only in a vertebrate host, but also in the mosquito vector [10]. They used the underutilized model system of avian malaria species Plasmodium relictum in canaries. Villa and colleagues selected for several different resistance strains, which had a similar delayed clearance phenotype as observed in the field. Interestingly, they did not find evidence of a fitness cost in the vertebrate host. In fact, the resistant strains reached greater parasitaemia than the susceptible strains. From this set of experiments it is unclear whether this is an anomaly or a relevant result. Future work should establish this, though fitness benefits associated with drug resistance have been seen before in Leishmania parasites [11]. An important caveat to the present study is that the parasites were grown in the absence of competition and it is feasible that a cost is not detected when growing by themselves, but would become apparent when in competition. However, these types of experiments are technologically more challenging to perform as it would require an accurate quantification methodology able to distinguish based on one SNP. This problem has been circumvented by either using relative peak height in sanger sequencing [12], or via the likely more accurate route of pyrosequencing [7–9], though these methodologies only give relative frequencies rather than absolute densities. 
 
The most interesting observation in the study by Villa et al is that the authors detected a fitness cost being played out in the mosquito vector, where the resistant strains had a decreased infectivity compared to the susceptible strain. This finding is important because 1) it demonstrates that the whole life cycle needs to be taken into account when understanding fitness costs, 2) resistance management strategies that are based on treatment within the vertebrate host may not have the intended effect if the cost does not play out in this host, and 3) it opens new research avenues to explore the possibility of exploiting fitness costs in mosquito vector. Future research should focus on incorporating these assays on fitness costs in mosquitoes, particularly for P. falciparum parasites. Additionally, it would be interesting to expand this work in a competitive environment, both in the vertebrate host as in the mosquito host. Finally, it would be important to establish the generalizability of such fitness cost in mosquitoes. If it is a significant factor, mathematical models could incorporate this effect in predictions on the spread of resistance.

References

[1] Huijben S, Bell AS, Sim DG, Tomasello D, Mideo N, Day T, et al. 2013. Aggressive chemotherapy and the selection of drug resistant pathogens. PLoS Pathog. 9(9): e1003578. https://doi.org/10.1371/journal.ppat.1003578
 
[2] Huijben S, Nelson WA, Wargo AR, Sim DG, Drew DR, Read AF. 2010. Chemotherapy, within-host ecology and the fitness of drug-resistant malaria parasites. Evolution (N Y). 64(10): 2952-68. https://doi.org/10.1111/j.1558-5646.2010.01068.x
 
[3] Zhang J, Cunningham JJ, Brown JS, Gatenby RA. 2017. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat Commun. 8(1). https://doi.org/10.1038/s41467-017-01968-5
 
[4] Laufer MK, Takala-Harrison S, Dzinjalamala FK, Stine OC, Taylor TE, Plowe C v. 2010. Return of chloroquine-susceptible falciparum malaria in Malawi was a reexpansion of diverse susceptible parasites. J Infect Dis. 202(5): 801-808. https://doi.org/10.1086/655659 

[5] Mharakurwa S, Matsena-Zingoni Z, Mudare N, Matimba C, Gara TX, Makuwaza A, et al. 2021. Steep rebound of chloroquine-sensitive Plasmodium falciparum in Zimbabwe. J Infect Dis. 223(2): 306-9. https://doi.org/10.1093/infdis/jiaa368
 
[6] Tirrell AR, Vendrely KM, Checkley LA, Davis SZ, McDew-White M, Cheeseman IH, et al. 2019. Pairwise growth competitions identify relative fitness relationships among artemisinin resistant Plasmodium falciparum field isolates. Malar J. 18: 295. https://doi.org/10.1186/s12936-019-2934-4
 
[7] Hott A, Tucker MS, Casandra D, Sparks K, Kyle DE. 2015. Fitness of artemisinin-resistant Plasmodium falciparum in vitro. J Antimicrob Chemother. 70(10): 2787-2796. https://doi.org/10.1093/jac/dkv199
 
[8] Straimer J, Gnädig NF, Stokes BH, Ehrenberger M, Crane AA, Fidock DA. 2017. Plasmodium falciparum K13 mutations differentially impact ozonide susceptibility and parasite fitness in vitro. mBio. 8(2): e00172-17. https://doi.org/10.1128/mBio.00172-17
 
[9] Nair S, Li X, Arya GA, McDew-White M, Ferrari M, Nosten F, et al. 2018. Fitness costs and the rapid spread of kelch13-C580Y substitutions conferring artemisinin resistance. Antimicrob Agents Chemother. 62(9). https://doi.org/10.1128/AAC.00605-18
 
[10] Villa M, Berthomieu A, Rivero A. Fitness costs and benefits in response to artificial artesunate selection in Plasmodium. 2022. bioRxiv, 20220128478164, ver 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.01.28.478164
 
[11] Vanaerschot M, Decuypere S, Berg M, Roy S, Dujardin JC. 2013. Drug-resistant microorganisms with a higher fitness--can medicines boost pathogens? Crit Rev Microbiol. 39(4): 384-394. https://doi.org/10.3109/1040841X.2012.716818
 
[12] Hassett MR, Roepe PD. In vitro growth competition experiments that suggest consequences of the substandard artemisinin epidemic that may be accelerating drug resistance in P. falciparum malaria. 2021. PLoS One. 16(3): e0248057. https://doi.org/10.1371/journal.pone.0248057

The genome of eukaryotic species is a complex structure that experience many different interactions within itself and with the surrounding environment. The genetic architecture of a phenotype (that is, the set of genetic elements affecting a trait of the organism) plays a fundamental role in understanding the adaptation process of a species to, for example, different climate environments, or to its interaction with other species. Thus, it is fundamental to study the different aspects of the genetic architecture of the species and its relationship with its surronding environment. Aspects such as modularity (the number of genetic units and the degree to which each unit is affecting a trait of the organism), pleiotropy (the number of different effects that a genetic unit can have on an organism) or linkage (the degree of association between the different genetic units) are essential to understand the genetic architecture and to interpret the effects of selection on the genome. Indeed, the knowledge of the different aspects of the genetic architecture could clarify whether genes are affected by multiple aspects of the environment or, on the contrary, are affected by only specific aspects [1,2].

The work performed by Lotterhos et al. [3] sought to understand the genetic architecture of the adaptation to different environments in lodgepole pine (Pinus contorta), considering as candidate SNPs those previously detected as a result of its extreme association patterns to different environmental variables or to extreme population differentiation. This consideration is very important because the study is only relevant if the studied markers are under the effect of selection. Otherwise, the genetic architecture of the adaptation to different environments would be masked by other (neutral) kind of associations that would be difficult to interpret [4,5]. In order to understand the relationship between genetic architecture and adaptation, it is relevant to detect the association networks of the candidate SNPs with climate variables (a way to measure modularity) and if these SNPs (and loci) are affected by single or multiple environments (a way to measure pleiotropy).

The authors used co-association networks, an innovative approach in this field, to analyse the interaction between the environmental information and the genetic polymorphism of each individual. This methodology is more appropriate than other multivariate methods - such as analysis based on principal components - because it is possible to cluster SNPs based on associations with similar environmental variables. In this sense, the co-association networks allowed to both study the genetic and physical linkage between different co-associations modules but also to compare two different models of evolution: a Modular environmental response architecture (specific genes are affected by specific aspects of the environment) or a Universal pleiotropic environmental response architecture (all genes are affected by all aspects of the environment). The representation of different correlations between allelic frequency and environmental factors (named galaxy biplots) are especially informative to understand the effect of the different clusters on specific aspects of the environment (for example, the co-association network ‘Aridity’ shows strong associations with hot/wet versus cold/dry environments).

The analysis performed by Lotterhos et al. [3], although it has some unavoidable limitations (e.g., only extreme candidate SNPs are selected, limiting the results to the stronger effects; the genetic and physical map is incomplete in this species), includes relevant results and also implements new methodologies in the field. To highlight some of them: the preponderance of a Modular environmental response architecture (evolution in separated modules), the detection of physical linkage among SNPs that are co-associated with different aspects of the environment (which was unexpected a priori), the implementation of co-association networks and galaxy biplots to see the effect of modularity and pleiotropy on different aspects of environment. Finally, this work contains remarkable introductory Figures and Tables explaining unambiguously the main concepts [6] included in this study. This work can be treated as a starting point for many other future studies in the field.

References

[1] Hancock AM, Brachi B, Faure N, Horton MW, Jarymowycz LB, Sperone FG, Toomajian C, Roux F & Bergelson J. 2011. Adaptation to climate across the Arabidopsis thaliana genome. Science 334: 83–86. doi: 10.1126/science.1209244
[2] Wagner GP & Zhang J. The pleiotropic structure of the genotype­phenotype map: the evolvability of complex organisms. Nature Review Genetics 12: 204–213. doi: 10.1038/nrg2949
[3] Lotterhos KE, Yeaman S, Degner J, Aitken S, Hodgins K. 2018. Modularity of genes involved in local adaptation to climate despite physical linkage. bioRxiv 202481, ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/202481
[4] Lotterhos KE & Whitlock MC. 2014. Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Molecular Ecology 23: 2178–2192. doi: 10.1111/mec.12725
[5] Lotterhos KE & Whitlock MC. 2015. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Molecular Ecology 24: 1031–1046. doi: 10.1111/mec.13100
[6] Paaby AB & Rockman MV. 2013. The many faces of pleiotropy. Trends in Genetics 29: 66-73. doi: 10.1016/j.tig.2012.10.010

The study of sexual selection goes as far as Darwin himself. Since then, elaborate theories concerning both intra- and inter-sexual sexual have been developed, and elegant experiments have been designed to test this body of theory. It may thus come as a surprise that the community is still debating on the correct way to measure simple components of sexual selection, such as the Bateman gradient (i.e., the covariance between the number of matings and the number of offspring)[1,2], or to quantify complex behaviours such as mate choice (the non-random choice of individuals with particular characters as mates)[3,4] and their consequences.
One difficulty in the study of sexual selection is evaluating the consequences of non-random mating. Indeed, when non-random mating is observed in a population, it is often difficult to establish whether such mating pattern leads to i) sexual selection per se (selection pressures favouring certain phenotypes), and/or ii) the non-random association of parental genes in their offspring or not. These two processes differ. In particular, assortative (and disassortative) mating can shape genetic covariances without leading to changes in gene frequencies in the population. Their distinction matters because these two processes lead to different evolutionary outcomes, which can have large ripple effects in the evolution of sexual behaviours, sexual ornamentation, and speciation.
In his paper, entitled “Multi-model inference of non-random mating from an information theoretic approach” [5], Carvajal-Rodríguez tackled this issue. The author generated a simple model in which the consequences of non-random mating can be inferred from information on the population frequencies before and after mating. The procedure is as follows: from the initial population frequencies of phenotypes (or genotypes) of both sexes, the model generates predictions on the frequencies after mating, assuming that particular mating patterns have occurred. This leads to different predictions for the phenotypic (or genotypic) frequencies after mating. The particular mating pattern leading to the best fit with the real frequencies is then identified via a model selection procedure (performing model averaging to combine different mating patterns is also possible).
This study builds on a framework introduced by Carvajal-Rodríguez’s colleagues [6] and encompasses later methodological developments involving the author himself [7]. Compared to early work, the new method proposed by the author builds on the relationship between mating pattern and information [8] to distinguish among scenarios that would lead to non-random mating due to different underlying processes, using simple model selection criterion such as the AICc.
The great asset of the proposed method is that it can be applied to the study of natural populations in which the study of mate choice and sexual selection is notoriously difficult. In the manuscript, the procedure is tested on a population of marine gastropods (Littorina saxatilis). This allows the reader to grasp how the method can be applied to a real system. In fact, anyone can try out the method thanks to the freely available software InfoMating programmed by the author. One important assumption underlying the current method is that the frequencies of unmated individuals do not change during the mating season. If this is not the case, the reader may refer to another publication of the same author which relaxes this assumption [9]. These papers are both instrumental for empiricists interested in testing sexual selection theory.

References

[1] Bateman, A. J. (1948). Intra-sexual selection in Drosophila. Heredity, 2(3), 349-368. doi: 10.1038/hdy.1948.21
[2] Jones, A. G. (2009). On the opportunity for sexual selection, the Bateman gradient and the maximum intensity of sexual selection. Evolution: International Journal of Organic Evolution, 63(7), 1673-1684. doi: 10.1111/j.1558-5646.2009.00664.x
[3] Andersson, M., & Simmons, L. W. (2006). Sexual selection and mate choice. Trends in ecology & evolution, 21(6), 296-302. doi: 10.1016/j.tree.2006.03.015
[4] Kuijper, B., Pen, I., & Weissing, F. J. (2012). A guide to sexual selection theory. Annual Review of Ecology, Evolution, and Systematics, 43, 287-311. doi: 10.1146/annurev-ecolsys-110411-160245
[5] Carvajal-Rodríguez, A. (2019). Multi-model inference of non-random mating from an information theoretic approach. bioRxiv, 305730, ver. 5 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/305730
[6] Rolán‐Alvarez, E., & Caballero, A. (2000). Estimating sexual selection and sexual isolation effects from mating frequencies. Evolution, 54(1), 30-36. doi: 10.1111/j.0014-3820.2000.tb00004.x
[7] Carvajal-Rodríguez, A., & Rolan-Alvarez, E. (2006). JMATING: a software for the analysis of sexual selection and sexual isolation effects from mating frequency data. BMC Evolutionary Biology, 6(1), 40. doi: 10.1186/1471-2148-6-40
[8] Carvajal-Rodríguez, A. (2018). Non-random mating and information theory. Theoretical population biology, 120, 103-113. doi: 10.1016/j.tpb.2018.01.003
[9] Carvajal-Rodríguez, A. (2019). A generalization of the informational view of non-random mating: Models with variable population frequencies. Theoretical population biology, 125, 67-74. doi: 10.1016/j.tpb.2018.12.004

Starting with the seminar paper by Lande & Arnold (1983), several studies have addressed phenotypic selection in natural populations of a wide variety of organisms, with a recent renewed interest in forest trees (e.g., Oddou-Muratorio et al. 2018; Alexandre et al. 2020; Westergren et al. 2023). Because of their long generation times, long-lived organisms such as forest trees may suffer the most from maladaptation due to climate change, and whether they will be able to adapt to new environmental conditions in just one or a few generations is hotly debated.

In this study, Oddou-Muratorio and colleagues (2024) extend the current framework to add two additional selection components that may alter patterns of fecundity selection and the estimation of standard selection gradients, namely sexual selection (evaluated as differences in flowering phenology conducting to assortative mating) and growth (viability) selection. Notably, the study is conducted in two contrasted environments (low vs high altitude populations) providing information on how the environment may modulate selection patterns in spring phenology. Spring phenology is a key adaptive trait that has been shown to be already affected by climate change in forest trees (Alberto et al. 2013). While fecundity selection for early phenology has been extensively reported before (see Munguía-Rosas et al. 2011), the authors found that this kind of selection can be strongly modulated by sexual selection, depending on the environment. Moreover, they found a significant correlation between early phenology and seedling growth in a common garden, highlighting the importance of this trait for early survival in European beech.

As a conclusion, this original research puts in evidence the need for more integrative approaches for the study of natural selection in the field, as well as the importance of testing multiple environments and the relevance of common gardens to further evaluate phenotypic changes due to real-time selection.

PS: The recommender and the first author of the preprint have shared authorship in a recent paper in a similar topic (Westergren et al. 2023). Nevertheless, the recommender has not contributed in any way or was aware of the content of the current preprint before acting as recommender, and steps have been taken for a fair and unpartial evaluation.

References

Alberto, F. J., Aitken, S. N., Alía, R., González‐Martínez, S. C., Hänninen, H., Kremer, A., Lefèvre, F., Lenormand, T., Yeaman, S., Whetten, R., & Savolainen, O. (2013). Potential for evolutionary responses to climate change - evidence from tree populations. Global Change Biology, 19(6), 1645‑1661.
https://doi.org/10.1111/gcb.12181
 
Alexandre, H., Truffaut, L., Klein, E., Ducousso, A., Chancerel, E., Lesur, I., Dencausse, B., Louvet, J., Nepveu, G., Torres‐Ruiz, J. M., Lagane, F., Musch, B., Delzon, S., & Kremer, A. (2020). How does contemporary selection shape oak phenotypes? Evolutionary Applications, 13(10), 2772‑2790.
https://doi.org/10.1111/eva.13082
 
Lande, R., & Arnold, S. J. (1983). The measurement of selection on correlated characters. Evolution, 37(6), 1210-1226.
https://doi.org/10.2307/2408842
 
Munguía-Rosas, M. A., Ollerton, J., Parra-Tabla, V., & De-Nova, J. A. (2011). Meta-analysis of phenotypic selection on flowering phenology suggests that early flowering plants are favoured. Ecology Letters, 14(5), 511-521
https://doi.org/10.1111/j.1461-0248.2011.01601.x

Oddou-Muratorio S, Bontemps A, Gauzere J, Klein E (2024) Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (Fagus sylvatica L.). bioRxiv, 2023.04.27.538521, ver. 2 peer-reviewed and recommended by Peer Community In Evolutionary Biology https://doi.org/10.1101/2023.04.27.538521 

Oddou-Muratorio, S., Gauzere, J., Bontemps, A., Rey, J.-F., & Klein, E. K. (2018). Tree, sex and size: Ecological determinants of male vs. female fecundity in three Fagus sylvatica stands. Molecular Ecology, 27(15), 3131‑3145.
https://doi.org/10.1111/mec.14770
 
Westergren, M., Archambeau, J., Bajc, M., Damjanić, R., Theraroz, A., Kraigher, H., Oddou‐Muratorio, S., & González‐Martínez, S.C. (2023). Low but significant evolutionary potential for growth, phenology and reproduction traits in European beech. Molecular Ecology, Early View 
https://doi.org/10.1111/mec.17196

Many parasites can manipulate their hosts, thus increasing their transmission to new hosts [1]. This is particularly the case for vector-borne parasites, which can alter the feeding behaviour of their hosts. However, predicting the optimal strategy is not straightforward because three actors are involved and the interests of the parasite may conflict with that of the vector. There are few models that consider the evolution of host manipulation by parasites [but see 2-4], but there are virtually none that investigated how parasites can manipulate the host choice of vectors. Even on the empirical side, many aspects of this choice remain unknown. Gandon [5] develops a simple evolutionary epidemiology model that allows him to formulate clear and testable predictions. These depend on which actor controls the trait (the vector or the parasite) and, when there is manipulation, whether it is realised via infected hosts (to attract vectors) or infected vectors (to change host choice). In addition to clarifying the big picture, Gandon [5] identifies some nice properties of the model, for instance an independence of the density/frequency-dependent transmission assumption or a backward bifurcation at R0=1, which suggests that parasites could persist even if their R0 is driven below unity. Overall, this study calls for further investigation of the different scenarios with more detailed models and experimental validation of general predictions.

References

[1] Hughes D, Brodeur J, Thomas F. 2012. Host manipulation by parasites. Oxford University Press.

[2] Brown SP. 1999. Cooperation and conflict in host-manipulating parasites. Proceedings of the Royal Society of London B: Biological Sciences 266: 1899–1904. doi: 10.1098/rspb.1999.0864

[3] Lion S, van Baalen M, Wilson WG. 2006. The evolution of parasite manipulation of host dispersal. Proceedings of the Royal Society of London B: Biological Sciences. 273: 1063–1071. doi: 10.1098/rspb.2005.3412

[4] Vickery WL, Poulin R. 2010. The evolution of host manipulation by parasites: a game theory analysis. Evolutionary Ecology 24: 773–788. doi: 10.1007/s10682-009-9334-0

[5] Gandon S. 2017. Evolution and manipulation of vector host choice. bioRxiv 110577, ver. 3 of 7th June 2017. doi: 10.1101/110577