Mathematical models play an essential role in current evolutionary biology, and evolutionary epidemiology is not an exception [1]. While the issues of virulence evolution and drug-resistance evolution resonate in the literature for quite some time [2, 3], the study by Alizon [4] is one of a few that consider co-evolution of both these traits [5]. The idea behind this study is the following: treating individuals with more severe symptoms at a higher rate (which appears to be quite natural) leads to an appearance of virulent drug-resistant strains, via treatment failure. The author then shows that virulence in drug-resistant strains may face different selective pressures than in drug-sensitive strains and hence proceed at different rates. Hence, treatment itself modulates evolution of virulence. As one of the reviewers emphasizes, the present manuscript offers a mathematical view on why the resistant and more virulent strains can be selected in epidemics. Also, we both find important that the author highlights that the topic and results of this study can be attributed to public health policies and development of optimal treatment protocols [6].
Mathematical models are simplified representations of reality, created with a particular purpose. It can be simple as well as complex, but even simple models can produce relatively complex and knitted results. The art of modelling thus lies not only in developing a model, but also in interpreting and unknitting the results. And this is what Alizon [4] indeed does carefully and exhaustively. Using two contrasting theoretical approaches to study co-evolution, the Price equation approach to study short-term evolution and the adaptive dynamics approach to study long-term evolution, Alizon [4] shows that a positive correlation between the rate of treatment and infection severity causes virulence in drug-sensitive strains to decrease. Clearly, no single model can describe and explain an examined system in its entirety, and even this aspect of the work is taken seriously. Many possible extensions of the study are laid out, providing a wide opportunity to pursue this topic even further. Personally, I have had an opportunity to read many Alizon’s papers and use, teach or discuss many of his models and results. All, including the current one, keep high standard and pursue the field of theoretical (evolutionary) epidemiology.

References

[1] Gandon S, Day T, Metcalf JE, Grenfell BT (2016) Forecasting epidemiological and evolutionary dynamics of infectious diseases. Trends Ecol Evol 31: 776-788. doi: https://doi.org/10.1016/j.tree.2016.07.010
[2] Berngruber TW, Froissart R, Choisy M, Gandon S (2013) Evolution of virulence in emerging epidemics. PLoS Pathog 9(3): e1003209. doi: https://doi.org/10.1371/journal.ppat.1003209
[3] Spicknall IH, Foxman B, Marrs CF, Eisenberg JNS (2013) A modeling framework for the evolution and spread of antibiotic resistance: literature review and model categorization. Am J Epidemiol 178: 508-520. doi: https://doi.org/10.1093/aje/kwt017
[4] Alizon S (2020) Treating symptomatic infections and the co-evolution of virulence and drug resistance. bioRxiv, 2020.02.29.970905, ver. 3 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.02.29.970905
[5] Carval D, Ferriere R (2010) A unified model for the coevolution of resistance, tolerance, and virulence. Evolution 64: 2988–3009. doi: https://doi.org/10.1111/j.1558-5646.2010.01035.x
[6] Read AF, T Day, and S Huijben (2011). The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proc Natl Acad Sci USA 108 Suppl 2, 10871–7. doi: https://doi.org/10.1073/pnas.1100299108

Sex chromosomes are special in the genome because they are often highly differentiated over much of their lengths and marked by degenerative evolution of their gene content. Understanding why sex chromosomes differentiate requires deciphering the forces driving their recombination patterns. Suppression of recombination may be subject to selection, notably because of functional effects of locking together variation at different traits, as well as longer-term consequences of the inefficient purge of deleterious mutations, both of which may contribute to patterns of differentiation [1]. As an example, male and female functions may reveal intrinsic antagonisms over the optimal genotypes at certain genes or certain combinations of interacting genes. As a result, selection may favour the recruitment of rearrangements blocking recombination and maintaining the association of sex-antagonistic allele combinations with the sex-determining locus.
The hypothesis that sexually antagonistic selection might drive recombination suppression along the sex chromosomes is not new, but there are surprisingly few studies examining this empirically [1]. Support mainly comes from the study of guppy populations Poecilia reticulata in which the level of sexual dimorphism (notably due to male ornaments, subject to sexual selection) varies among populations, and was found to correlate with the length of the non-recombining region on the sex chromosome [2]. But the link is not always that clear. For instance in the fungus Microbotryum violaceum, the mating type loci is characterized by adjacent segments with recombination suppression, despite the near absence of functional differentiation between mating types [3].
In this study, Shearn and colleagues [4] explore the patterns of recombination suppression on the sex chromosomes of primates. X and Y chromosomes are strongly differentiated, except in a small region where they recombine with each other, the pseudoautosomal region (PAR). In the clade of apes and monkeys, including humans, large rearrangements have extended the non recombining region stepwise, eroding the PAR. Could this be driven by sexually antagonistic selection in a clade showing strong sexual differentiation?
To evaluate this idea, Shearn et al. have compared the structure of recombination in apes and monkeys to their sister clade with lower levels of sexual dimorphism, the lemurs and the lorises. If sexual antagonism was important in shaping recombination suppression, and assuming lower measures of sexual dimorphism reflect lower sexual antagonism [5], then lemurs and lorises would be predicted to show a shorter non-recombining region than apes and monkeys.
Lemurs and lorises were terra incognita in terms of genomic research on the sex chromosomes, so Shearn et al. have sequenced the genomes of males and females of different species. To assess whether sequences came from a recombining or non-recombining segment, they used coverage information in males vs females to identify sequences on the X whose copy on the Y is absent or too divergent to map, indicating long-term differentiation (absence of recombination). This approach reveals that the two lineages have undergone different recombination dynamics since they split from their common ancestor: regions which have undergone further structural rearrangements extending the non-recombining region in apes and monkeys, have continued to recombine normally in lemurs and lorises. Consistent with the prediction, macroevolutionary variation in the differentiation of males and females is indeed accompanied by variation in the size of the non-recombining region on the sex chromosome.
Sex chromosomes are excellent examples of how genomes are shaped by selection. By directly exploring recombination patterns on the sex chromosome across all extant primate groups, this study comes as a nice addition to the short series of empirical studies evaluating whether sexual antagonism may drive certain aspects of genome structure. The sexual selection causing sometimes spectacular morphological or behavioural differences between sexes in many animals may be the visible tip of the iceberg of all the antagonisms that characterise male vs. female functions generally [5]. Further research should bring insight into how different flavours or intensities of antagonistic selection can contribute to shape genome variation.

References

[1] Charlesworth D (2017) Evolution of recombination rates between sex chromosomes. Philosophical Transactions of the Royal Society B: Biological Sciences, 372, 20160456. https://doi.org/10.1098/rstb.2016.0456
[2] Wright AE, Darolti I, Bloch NI, Oostra V, Sandkam B, Buechel SD, Kolm N, Breden F, Vicoso B, Mank JE (2017) Convergent recombination suppression suggests role of sexual selection in guppy sex chromosome formation. Nature Communications, 8, 14251. https://doi.org/10.1038/ncomms14251
[3] Branco S, Badouin H, Vega RCR de la, Gouzy J, Carpentier F, Aguileta G, Siguenza S, Brandenburg J-T, Coelho MA, Hood ME, Giraud T (2017) Evolutionary strata on young mating-type chromosomes despite the lack of sexual antagonism. Proceedings of the National Academy of Sciences, 114, 7067–7072. https://doi.org/10.1073/pnas.1701658114
[4] Shearn R, Wright AE, Mousset S, Régis C, Penel S, Lemaitre J-F, Douay G, Crouau-Roy B, Lecompte E, Marais GAB (2020) Evolutionary stasis of the pseudoautosomal boundary in strepsirrhine primates. bioRxiv, 445072. https://doi.org/10.1101/445072
[5] Connallon T, Clark AG (2014) Evolutionary inevitability of sexual antagonism. Proceedings of the Royal Society B: Biological Sciences, 281, 20132123. https://doi.org/10.1098/rspb.2013.2123

Plants display an amazing diversity of reproductive strategies with and without sex. This diversity is particularly remarkable in flowering plants, as highlighted by Charles Darwin, who wrote several botanical books scrutinizing plant reproduction. One particularly influential work concerned floral variation [1]. Darwin recognized that flowers may present different forms within a single population, with or without sex specialization. The number of species concerned is small, but they display recurrent patterns, which made it possible for Darwin to invoke natural and sexual selection to explain them. Most of early evolutionary theory on the evolution of reproductive strategies was developed in the first half of the 20th century and was based on animals. However, the pioneering work by David Lloyd from the 1970s onwards excited interest in the diversity of plant sexual strategies as models for testing adaptive hypotheses and predicting reproductive outcomes [2]. The sex specialization of individual flowers and plants has since become one of the favorite topics of evolutionary biologists. However, attention has focused mostly on cases related to sex differentiation (dioecy and associated conditions [3]). Separate unisexual flower types on the same plant (monoecy and related cases, rendering the plant functionally hermaphroditic) have been much less studied, apart from their possible role in the evolution of dioecy [4] or their association with particular modes of pollination [5].
Two specific non-mutually exclusive hypotheses on the evolution of separate sexes in flowers (dicliny) have been proposed, both anchored in Lloyd’s views and Darwin’s legacy, with selfing avoidance and optimal limited resource allocation. Intermediate sex separation, in which sex morphs have different combinations of unisexual and hermaphrodite flowers, has been crucial for testing these hypotheses through comparative analyses of optimal conditions in suggested transitions. Again, cases in which floral unisexuality does not lead to sex separation have been studied much less than dioecious plants, at both the microevolutionary and macroevolutionary levels. It is surprising that the increasing availability of plant phylogenies and powerful methods for testing evolutionary transitions and correlations have not led to more studies, even though the frequency of monoecy is probably highest among diclinous species (those with unisexual flowers in any distribution among plants within a population [6]).
The study by Torices et al. [7] aims to fill this gap, offering a different perspective to that provided by Diggle & Miller [8] on the evolution of monoecious conditions. The authors use heads of a number of species of the sunflower family (Asteraceae) to test specifically the effect of resource limitation on the expression of sexual morphs within the head. They make use of the very particular and constant architecture of inflorescences in these species (the flower head or “capitulum”) and the diversity of sexual conditions (hermaphrodite, gynomonoecious, monoecious) and their spatial pattern within the flower head in this plant family to develop an elegant means of testing this hypothesis. Their results are consistent with their expectations on the effect of resource limitation on the head, as determined by patterns of fruit size within the head, assuming that female fecundity is more strongly limited by resource availability than male function.
The authors took on a huge challenge in choosing to study the largest plant family (about 25 thousand species). Their sample was limited to only about a hundred species, but species selection was very careful, to ensure that the range of sex conditions and the available phylogenetic information were adequately represented. The analytical methods are robust and cast no doubt on the reported results. However, I can’t help but wonder what would happen if the antiselfing hypothesis was tested simultaneously. This would require self-incompatibility (SI) data for the species sample, as the presence of SI is usually invoked as a powerful antiselfing mechanism, rendering the unisexuality of flowers unnecessary. However, SI is variable and frequently lost in the sunflower family [9]. I also wonder to what extent the very specific architecture of flower heads imposes an idiosyncratic resource distribution that may have fixed these sexual systems in species and lineages of the family. Although not approached in this study, intraspecific variation seems to be low. It would be very interesting to use similar approaches in other plant groups in which inflorescence architecture is lax and resource distribution may differ. A whole-plant approach might be required, rather than investigations of single inflorescences as in this study. This study has no flaws, but instead paves the way for further testing of a long-standing dual hypothesis, probably with different outcomes in different ecological and evolutionary settings. In the end, sex is not only in the head.

References

[1] Darwin, C. (1877). The different forms of flowers on plants of the same species. John Murray.
[2] Barrett, S. C. H., and Harder, L. D. (2006). David G. Lloyd and the evolution of floral biology: from natural history to strategic analysis. In L.D. Harder, L. D., and Barrett, S. C. H. (eds) Ecology and Evolution of Flowers. OUP, Oxford. Pp 1-21.
[3] Geber, M. A., Dawson, T. E., and Delph, L. F. (eds) (1999). Gender and sexual dimorphism in flowering plants. Springer, Berlin.
[4] Charlesworth, D. (1999). Theories of the evolution of dioecy. In Geber, M. A., Dawson T. E. and Delph L. F. (eds) (1999). Gender and sexual dimorphism in flowering plants. Springer, Berlin. Pp. 33-60.
[5] Friedman, J., and Barrett, S. C. (2008). A phylogenetic analysis of the evolution of wind pollination in the angiosperms. International Journal of Plant Sciences, 169(1), 49-58. doi: 10.1086/523365
[6] Renner, S. S. (2014). The relative and absolute frequencies of angiosperm sexual systems: dioecy, monoecy, gynodioecy, and an updated online database. American Journal of botany, 101(10), 1588-1596. doi: 10.3732/ajb.1400196
[7] Torices, R., Afonso, A., Anderberg, A. A., Gómez, J. M., and Méndez, M. (2019). Architectural traits constrain the evolution of unisexual flowers and sexual segregation within inflorescences: an interspecific approach. bioRxiv, 356147, ver. 3 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/356147
[8] Diggle, P. K., and Miller, J. S. (2013). Developmental plasticity, genetic assimilation, and the evolutionary diversification of sexual expression in Solanum. American journal of botany, 100(6), 1050-1060. doi: 10.3732/ajb.1200647
[9] Ferrer, M. M., and Good‐Avila, S. V. (2007). Macrophylogenetic analyses of the gain and loss of self‐incompatibility in the Asteraceae. New Phytologist, 173(2), 401-414. doi: 10.1111/j.1469-8137.2006.01905.x

It is well known that the rearing environment has strong effects on life history and fitness traits of organisms. Microbes are part of every environment and as such likely contribute to such environmental effects. Gut bacteria are a special type of microbe that most animals harbor, and as such they are part of most animals’ environment. Such microbial symbionts therefore likely contribute to local adaptation [1]. The main question underlying the laboratory study by Guilhot et al. [2] was: How much do particular gut bacteria affect the organismal phenotype, in terms of life history and larval foraging traits, of the fruit fly Drosophila melanogaster, a common laboratory model species in biology?
To investigate the above question, the authors isolated 4 taxa of bacteria from the gut of a (randomly picked) Drosophila melanogaster lab strain, and subsequently let Drosophila melanogaster eggs and larvae (stemming from their own, different lab strain) develop both in the typical artificial laboratory medium as well as in grapes, a natural “new” habitat for Drosophila larvae, inoculated with theses bacteria, singly and in combination, also including a bacteria-free control. By investigating various relevant developmental and size traits, the authors found that adding particularly Enterobacteria had some visible effects on several traits, both upward (indicting improvement) and downward (being detrimental) (with three other types of bacteria showing only minor or even no effects). In general, the grape medium reduced performance relative to the standard lab medium. Strongest interactive effects occurred for development time and body size, together making up growth plasticity [3], with lesser such effects on some related behavioral (feeding) traits (Figs. 2,3).
The study premise is interesting, its general objectives are clearly laid out, and the practical work was conducted correctly as far as I can evaluate. The study remains largely descriptive in that no particular a priori hypotheses or predictions in relation to the specific bacteria isolated were formulated, not least because the bacteria were necessarily somewhat arbitrarily chosen and there were apparently no prior studies from which to derive concrete predictions. Overall, the results of this study should be of interest to the community of evolutionary ecologists, especially those working on nutritional and microbiome effects on animal life histories. I consider this work to be primarily ecological, with limited evolutionary content (e.g. no genetics) though some evolutionary implications, as mentioned in the paper’s Conclusions. So this paper would best fit in a microbial or physiological ecology outlet/journal.
The inclusion of a natural medium (grapes) must be commended because this permits inferences and conclusions for at least one natural environment, whereas inferences drawn from laboratory studies in the artificial medium that most Drosophila researchers seem to use are typically limited. Unsurprisingly perhaps, the study showed that Drosophila melanogaster fared generally better in the artificial than the chosen natural medium (grape). Crucially, however, the bacterial symbionts modified both media differentially. Although common bacterial taxa were chosen, the particular bacteria isolated and used remain arbitrary, as there are many. I note that the main and strongest interactive effects between medium and bacterial type are apparent for the Enterobacteria, and they probably also strongly, if not exclusively, mediate the overall effect of the bacterial mixture.
While these specific data are novel, they are not very surprising. If we grow animals in different environments we can expect some detectable effects of these environments, including the bacterial (microbiome) environment, on the hosts life history. The standard and predicted [4] life history response of Drosophila melanogaster (but not all insects [3]) facing stressful nutritional environments, as apparently created by the Enterobacteria, is to extend development but come out smaller in the end. This is what happened here for the laboratory medium ([2]: Fig. 5). The biological interpretation is that individuals have more trouble ingesting and/or digesting the nutrients available (thus prolonging their foraging period and development), yet cannot convert the nutrients effectively into body size increments (hence emerging smaller). This is what the authors here refer to as developmental plasticity, which is ultimately nutritionally mediated. However, interestingly, a signal in the opposite direction was indicated for the bacterial mixture in the grape medium (flies emerging larger after accelerated development: Fig. 5), suggesting some positive effects on growth rate of the natural medium, perhaps related to grapes being a limited resource that needs to be escaped quickly [3]? The reversal of sexual size dimorphism across bacterial treatments in the grape environment detectable in Fig. 4 is interesting, too, though I don’t understand why this happens, and this is not discussed.
In general, more encompassing and increased questions in this context to be researched in the future could be: 1) are these effects predictable (not (yet) at this point, or so it seems); and 2) how strong are these environmental bacterial effects relative to other, more standard effects (e.g. relative to genetic variation, population variation, etc., or relative to other types of environmental effects like, say, temperature)? (3) It could further be asked why not natural but laboratory populations of Drosophila were used for this experiment, if the aim was to draw inferences for the wild situation. (4) Although Genotype x Environment effects are invoked in the Discussion, they were not tested here, lacking genetically different Drosophila families or populations. From an evolutionary standpoint, I consider this the greatest weakness of the study. I was also not too thrilled by the particular statistical analyses employed, though this ultimately does not negate the results. Nevertheless, this work is a good start in this huge field investigating the microbiome. In conclusion, I can recommend this paper after review by PCI Evol Biol.

References

[1] Kawecki, T. J. and Ebert, D. (2004) Conceptual issues in local adaptation. Ecology Letters 7: 1225-1241. doi: 10.1111/j.1461-0248.2004.00684.x
[2] Guilhot, R., Rombaut, A., Xuéreb, A., Howell, K. and Fellous, S. (2019). Environmental specificity in Drosophila-bacteria symbiosis affects host developmental plasticity. BioRxiv, 717702, v3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/717702
[3] Blanckenhorn, W.U. (1999) Different growth responses to temperature and resource limitation in three fly species with similar life histories. Evolutionary Ecology 13: 395-409. doi: 10.1023/A:1006741222586
[4] Stearns, S. C. and Koella, J. (1986) The evolution of phenotypic plasticity in life history traits: predictions of reaction norms for age and size at maturity. Evolution 40: 893-914. doi: 10.1111/j.1558-5646.1986.tb00560.x

The preprint by Pratlong et al. [1] is a well thought quest for genomic regions involved in local adaptation to depth in a species a red coral living the Mediterranean Sea. It first describes a pattern of structuration and then attempts to find candidate genes involved in local adaptation by contrasting deep with shallow populations. Although the pattern of structuration is clear and meaningful, the candidate genomic regions involved in local adaptation remain to be confirmed. Two external reviewers and myself found this preprint particularly interesting regarding the right-mindedness of the authors in front of the difficulties they encounter during their experiments. The discussions on the pros and cons of the approach are very sound and can be easily exported to a large number of studies that hunt for local adaptation. In this sense, the lessons one can learn by reading this well documented manuscript are certainly valuable for a wide range of evolutionary biologists.
More precisely, the authors RAD-sequenced 6 pairs of 'shallow vs deep' samples located in 3 geographical sea areas (Banyuls, Corsica and Marseilles). They were hoping to detect genes involved in the adaptation to depth, if there were any. They start by assessing the patterns of structuration of the 6 samples using PCA and AMOVA [2] and also applied the STRUCTURE [3] assignment software. They show clearly that the samples were mostly differentiated between geographical areas and that only 1 out the 3 areas shows a pattern of isolation by depth (i.e. Marseille). They nevertheless went on and scanned for variants that are highly differentiated in the deep samples when compared to the shallow paired samples in Marseilles, using an Fst outliers approach [4] implemented in the BayeScEnv software [5]. No clear functional signal was in the end detected among the highly differentiated SNPs, leaving a list of candidates begging for complementary data.
The scan for local adaptation using signatures of highly divergent regions is a classical problem of population genetics. It has been applied on many species with various degrees of success. This study is a beautiful example of a well-designed study that did not give full satisfactory answers. Readers will especially appreciate the honesty and the in-depth discussions of the authors while exposing their results and their conclusions step by step.

References

[1] Pratlong, M., Haguenauer, A., Brener, K., Mitta, G., Toulza, E., Garrabou, J., Bensoussan, N., Pontarotti P., & Aurelle, D. (2018). Separate the wheat from the chaff: genomic scan for local adaptation in the red coral Corallium rubrum. bioRxiv, 306456, ver. 3 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/306456
[2] Excoffier, L., Smouse, P. E. & Quattro, J. M. (1992). Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131(2), 479-491.
[3] Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155(2), 945-959.
[4] Lewontin, R. C., & Krakauer, J. (1973). Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genetics, 74(1), 175-195.
[5] de Villemereuil, P., & Gaggiotti, O. E. (2015). A new FST‐based method to uncover local adaptation using environmental variables. Methods in Ecology and Evolution, 6(11), 1248-1258. doi: 10.1111/2041-210X.12418