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

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

In some taxa such as fish and arthropods, closely related species can have different mechanisms of sex determination and in particular different sex chromosomes, which implies that new sex chromosomes are constantly evolving [1]. Several models have been developed to explain this pattern but empirical data are lacking and the causes of the fast sex chromosome turn over remain mysterious [2-4]. Leclerq et al. [5] in a paper that just came out in PNAS have focused on one possible explanation: Wolbachia. This widespread intracellular symbiont of arthropods can manipulate its host reproduction in a number of ways, often by biasing the allocation of resources toward females, the transmitting sex. Perhaps the most spectacular example is seen in pillbugs, where Wolbachia commonly turns infected males into females, thus doubling its effective transmission to grandchildren. Extensive investigations on this phenomenon were initiated 30 years ago in the host species Armadillidium vulgare. The recent paper by Leclerq et al. beautifully validates an hypothesis formulated in these pioneer studies [6], namely, that a nuclear insertion of the Wolbachia genome caused the emergence of new female determining chromosome, that is, a new sex chromosome.

Many populations of A. vulgare are infected by the feminising Wolbachia strain wVulC, where the spread of the bacterium has also induced the loss of the ancestral female determining W chromosome (because feminized ZZ individuals produce females without transmitting any W). In these populations, all individuals carry two Z chromosomes, so that the bacterium is effectively the new sex-determining factor: specimens that received Wolbachia from their mother become females, while the occasional loss of Wolbachia from mothers to eggs allows the production of males. Intriguingly, studies from natural populations also report that some females are devoid both of Wolbachia and the ancestral W chromosome, suggesting the existence of new female determining nuclear factor, the hypothetical “f element”.

Leclerq et al. [5] found the f element and decrypted its origin. By sequencing the genome of a strain carrying the putative f element, they found that a nearly complete wVulC genome got inserted in the nuclear genome and that the chromosome carrying the insertion has effectively become a new W chromosome. The insertion is indeed found only in females, PCRs and pedigree analysis tell. Although the Wolbachia-derived gene(s) that became sex-determining gene(s) remain to be identified among many possible candidates, the genomic and genetic evidence are clear that this Wolbachia insertion is determining sex in this pillbug strain. Leclerq et al. [5] also found that although this insertion is quite recent, many structural changes (rearrangements, duplications) have occurred compared to the wVulC genome, which study will probably help understand which bacterial gene(s) have retained a function in the nucleus of the pillbug. Also, in the future, it will be interesting to understand how and why exactly the nuclear inserted Wolbachia rose in frequency in the pillbug population and how the cytoplasmic Wolbachia was lost, and to tease apart the roles of selection and drift in this event. We highly recommend this paper, which provides clear evidence that Wolbachia has caused sex chromosome turn over in one species, opening the conjecture that it might have done so in many others.

References

[1] Bachtrog D, Mank JE, Peichel CL, Kirkpatrick M, Otto SP, Ashman TL, Hahn MW, Kitano J, Mayrose I, Ming R, Perrin N, Ross L, Valenzuela N, Vamosi JC. 2014. Tree of Sex Consortium. Sex determination: why so many ways of doing it? PLoS Biology 12: e1001899. doi: 10.1371/journal.pbio.1001899

[2] van Doorn GS, Kirkpatrick M. 2007. Turnover of sex chromosomes induced by sexual conflict. Nature 449: 909-912. doi: 10.1038/nature06178

[3] Cordaux R, Bouchon D, Grève P. 2011. The impact of endosymbionts on the evolution of host sex-determination mechanisms. Trends in Genetics 27: 332-341. doi: 10.1016/j.tig.2011.05.002

[4] Blaser O, Neuenschwander S, Perrin N. 2014. Sex-chromosome turnovers: the hot-potato model. American Naturalist 183: 140-146. doi: 10.1086/674026

[5] Leclercq S, Thézé J, Chebbi MA, Giraud I, Moumen B, Ernenwein L, Grève P, Gilbert C, Cordaux R. 2016. Birth of a W sex chromosome by horizontal transfer of Wolbachia bacterial symbiont genome. Proceeding of the National Academy of Science USA 113: 15036-15041. doi: 10.1073/pnas.1608979113

[6] Legrand JJ, Juchault P. 1984. Nouvelles données sur le déterminisme génétique et épigénétique de la monogénie chez le crustacé isopode terrestre Armadillidium vulgare Latr. Génétique Sélection Evolution 16: 57–84. doi: 10.1186/1297-9686-16-1-57

Perhaps because of its deadliness, the 2013-2016 Ebola Virus (EBOV) epidemics in West-Africa has led to unprecedented publication and sharing of full virus genome sequences. This was both rapid (90 full genomes were shared within weeks [1]) and important (more than 1500 full genomes have been released overall [2]). Furthermore, the availability of the metadata (especially GPS location) has led to depth analyses of the geographical spread of the epidemics [3].
In their work, Dellicour et al. [4] pursue earlier phylogeographical investigations in an original and yet simple approach to address questions of key public health importance. The originality of the approach is dual. First, from a technical standpoint, they capture the spread of infectious diseases in a continuous framework using a novel model that allows for rare long-distance dispersal events. Second, in a more classical discrete meta-population framework, they simulate the effect of public health interventions by pruning the phylogenetic tree and assessing how this affects key parameters. For instance, to simulate the effect of closing borders they remove subsets of the phylogeny that involved dispersal between countries and to simulate the effect of protecting a region by quarantine they remove all the leaves (i.e. the infections sampled) from this region. This phylogeny pruning is both original and simple. It is however limited because it currently assumes that policies are 100% effective and earlier modelling work on human influenza showed that long distance travel bans had to be implemented with >99% efficiency in order to slow epidemic growth from a time scale of days to weeks [5].
From a biological standpoint, Dellicour et al. [4] corroborate earlier findings that highly populated locations (>1,000,000 inhabitants) were crucial in explaining the magnitude of the epidemics but also show the importance of the transmission between the three capital cities. They also show that rare long-distance dispersing events of the virus are not key to explaining the magnitude of the epidemics (even though they assume 100% efficiency of suppressing long-distance event). Finally, thanks to their continuous model they estimate the speed of spread of the epidemics and are able to detect the effect of border closing on this speed.
Overall, this study [4], which involves state-of-the-art Bayesian inference methods of infection phylogenies using MCMC, stands out because of its effort to simulate public health interventions. It stands as an encouragement for the development of intervention models with increased realism and for even faster and larger virus sequence data sharing.

References

[1] Gire et al. 2014. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345: 1369–1372. doi: 10.1126/science.1259657.
[2] Holmes EC, Dudas G, Rambaut A and Andersen KG. 2016. The evolution of Ebola virus: insights from the 2013-2016 epidemic. Nature 538: 193–200. doi: 10.1038/nature19790.
[3] Dudas et al. 2017. Virus genomes reveal factors that spread and sustained the Ebola epidemic. Nature 544: 309–315 (2017). doi: 10.1038/nature22040.
[4] Dellicour S, Baele G, Dudas G, Faria NR, Pybus OG, Suchard MA, Rambaud A and Lemey P. 2018. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. bioRxiv, 163691, ver. 3 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/163691.
[5] Hollingsworth TD, Ferguson NM and Anderson RM. 2006. Will travel restrictions control the international spread of pandemic influenza? Nature Medicine 12, 497–499. doi: 10.1038/nm0506-497.

The role of microbial symbionts in governing social traits of their hosts is an exciting and developing research area. Just like symbionts influence host reproductive behaviour and can cause mating incompatibilities to promote symbiont transmission through host populations (Engelstadter and Hurst 2009; Correa and Ballard 2016; Johnson and Foster 2018) (see also discussion on conflict resolution in Johnsen and Foster 2018), microbial symbionts could enhance transmission by promoting the social behaviour of their hosts (Ezenwa et al. 2012; Lewin-Epstein et al. 2017; Gurevich et al. 2020). Here I apply the term ‘symbiosis’ in the broad sense, following De Bary 1879 as “the living together of two differently named organisms“ independent of effects on the organisms involved (De Bary 1879), i.e. the biological interaction between the host and its symbionts may include mutualism, parasitism and commensalism.
So far, we have relative few studies that explore the role of symbionts in promoting social behaviours such as parental care. Clearly, disentangling cause and effect when assessing the functional significance of symbiotic relationships in general is extremely challenging, and perhaps even more caution is needed when assessing the role of symbionts in the evolution of parental care, given the high fitness benefits to the offspring of receiving care. An interesting study on the symbiotic relationship between termites and their eukaryotic gut symbionts proposes a role of gut flagellates in the origin of subsocial behaviour (extended offspring care) in the termites through proctodeal trophallaxis (i.e. anus-to-mouth feeding), driven by mutualistic beneficial interactions (Nalepa 2020). Van Meyel et al. (2021) hypothesized a role of gut symbionts in promoting maternal care behaviour in the European earwig, and set out to test this idea in a carefully executed experimental study. They used a broad-spectrum antibiotic treatment to alter gut microbiota in mothers and examined its effect on maternal care provisioning. While the antibiotic treatment altered the gut microbiome, no effect on pre- or post-hatching maternal care was detected. The authors also investigated a broad range of physiological and reproductive traits measured over a major part of a female’s lifetime, and detected no effect of microbiome alteration on these traits. The study therefore does not provide evidence for a direct role of the gut microbiome in shaping offspring care in this population of European earwigs.
Within populations, earwigs show inter-individual variation in the expression of maternal care (Meunier et al. 2012; Ratz et al. 2016), and there is evidence that genetic and environmental factors contribute to this this variation (Meunier and Kolliker 2012; Kramer et al. 2017). The study by Van Meyel et al. (2021) is the first to analyse microbiome composition of the European earwig, and they study host-symbiont associations in a single population. A next step could be to explore among population variation in the gut microbiome, to achieve a better understanding on host-microbiome variation and dynamics in wild populations. Depending on the nature of host-symbiont associations across populations, new perspectives on their functional significance may arise (Hird 2017; Johnson and Foster 2018). It is therefore too early to conclusively confirm or reject the role of microbial symbionts in the expression of parental care in this system.

References

Correa, C. C., and Ballard, J. W. O. (2016). Wolbachia associations with insects: winning or losing against a master manipulator. Frontiers in Ecology and Evolution, 3, 153. doi: https://doi.org/10.3389/fevo.2015.00153

De Bary, A. (1879). Die Erscheinung der Symbiose. Verlag von Karl J. Trubner, Strassburg.

Engelstädter, J., and Hurst, G. D. (2009). The ecology and evolution of microbes that manipulate host reproduction. Annual Review of Ecology, Evolution, and Systematics, 40, 127-149. doi: https://doi.org/10.1146/annurev.ecolsys.110308.120206

Ezenwa, V. O., Gerardo, N. M., Inouye, D. W., Medina, M., and Xavier, J. B. (2012). Animal behavior and the microbiome. Science, 338(6104), 198-199. doi: https://doi.org/10.1126/science.1227412

Gurevich, Y., Lewin-Epstein, O., and Hadany, L. (2020). The evolution of paternal care: a role for microbes?. Philosophical Transactions of the Royal Society B, 375(1808), 20190599. doi: https://doi.org/10.1098/rstb.2019.0599

Hird, S. M. (2017). Evolutionary biology needs wild microbiomes. Frontiers in microbiology, 8, 725. doi: https://doi.org/10.3389/fmicb.2017.00725

Johnson, K. V. A., and Foster, K. R. (2018). Why does the microbiome affect behaviour?. Nature reviews microbiology, 16(10), 647-655. doi: https://doi.org/10.1038/s41579-018-0014-3

Kramer et al. (2017). When earwig mothers do not care to share: parent–offspring competition and the evolution of family life. Functional Ecology, 31(11), 2098-2107. doi: https://doi.org/10.1111/1365-2435.12915

Lewin-Epstein, O., Aharonov, R., and Hadany, L. (2017). Microbes can help explain the evolution of host altruism. Nature communications, 8(1), 1-7. doi: https://doi.org/10.1038/ncomms14040

Meunier, J., and Kölliker, M. (2012). Parental antagonism and parent–offspring co-adaptation interact to shape family life. Proceedings of the Royal Society B: Biological Sciences, 279(1744), 3981-3988. doi: https://doi.org/10.1098/rspb.2012.1416

Meunier, J., Wong, J. W., Gómez, Y., Kuttler, S., Röllin, L., Stucki, D., and Kölliker, M. (2012). One clutch or two clutches? Fitness correlates of coexisting alternative female life-histories in the European earwig. Evolutionary Ecology, 26(3), 669-682. doi: https://doi.org/10.1007/s10682-011-9510-x

Nalepa, C. A. (2020). Origin of mutualism between termites and flagellated gut protists: transition from horizontal to vertical transmission. Frontiers in Ecology and Evolution, 8, 14. doi: https://doi.org/10.3389/fevo.2020.00014

Ratz, T., Kramer, J., Veuille, M., and Meunier, J. (2016). The population determines whether and how life-history traits vary between reproductive events in an insect with maternal care. Oecologia, 182(2), 443-452. doi: https://doi.org/10.1007/s00442-016-3685-3

Van Meyel, S., Devers, S., Dupont, S., Dedeine, F. and Meunier, J. (2021) Alteration of gut microbiota with a broad-spectrum antibiotic does not impair maternal care in the European earwig. bioRxiv, 2020.10.08.331363. ver. 5 peer-reviewed and recommended by PCI Evol Biol. https://doi.org/10.1101/2020.10.08.331363