Monnin et al. [1] here studied how Drosophila populations are affected when exposed to a high virulent endosymbiotic wMelPop Wolbachia strain and why virulent vertically transmitting endosymbionts persist in nature. This virulent wMelPop strain has been described to be a blocker of dengue and other arboviral infections in arthropod vector species, such as Aedes aegypti. Whereas it can thus function as a mutualistic symbiont, it here acts as an antagonist along the mutualism-antagonism continuum symbionts operate. The wMelPop strain is not a natural occurring strain in Drosophila melanogaster and thus the start of this experiment can be seen as a novel host-pathogen association. Through experimental evolution of 17 generations, the authors studied how high temperature affects wMelPop Wolbachia virulence and Drosophila melanogaster survival. The authors used Drosophila strains that were selected for late reproduction, given that this should favor evolution to a lower virulence. Assumptions for this hypothesis are not given in the manuscript here, but it can indeed be assumed that energy that is assimilated to symbiont tolerance instead of reproduction may lead to reduced virulence evolution. This has equally been suggested by Reyserhove et al. [2] in a dynamics energy budget model tailored to Daphnia magna virulence evolution upon a viral infection causing White fat Cell disease, reconstructing changing environments through time.
Contrary to their expectations for vertically transmitting symbionts, the authors did not find a reduction in wMelPop Wolbachia virulence during the course of the experimental evolution experiment under high temperature. Important is what this learns for virulence evolution, also for currently horizontal transmitting disease epidemics (such as COVID-19). It mainly reflects that evolution of virulence for new host-pathogen associations is difficult to predict and that it may take multiple generations before optimal levels of virulence are reached [3,4]. These optimal levels of virulence will depend on trade-offs with other life history traits of the symbiont, but also on host demography, host heterogeneity, amongst others [5,6]. Multiple microbial interactions may affect the outcome of virulence evolution [7]. Given that no germ-free individuals were used, it can be expected that other components of the Drosophila microbiome may have played a role in the virulence evolution. In most cases, microbiota have been described as defensive or protective for virulent symbionts [8], but they may also have stimulated the high levels of virulence. Especially, given that upon higher temperatures, Wolbachia growth may have been increased, host metabolic demands increased [9], host immune responses affected and microbial communities changed [10]. This may have resulted in increased competitive interactions to retrieve host resources, sustaining high virulence levels of the symbiont.
A nice asset of this study is that the phenotypic results obtained in the experimental evolution set-up were linked with wMelPop density measurement and octomom copy number quantifications. Octomom is a specific 8-n genes region of the Wolbachia genome responsible for wMelPop virulence, so there is a link between the phenotypic and molecular functions of the involved symbiont. The authors found that density, octomom copy number and virulence were correlated to each other. An important note the authors address in their discussion is that, to exclude the possibility that octomom copy number has an effect on density, and density on virulence, the effect of these variables should be assessed independently of temperature and age. The obtained results are a valuable contribution to the ongoing debate on the relationship between wMelPop octomom copy number, density and virulence.

References

[1] Monnin, D., Kremer, N., Michaud, C., Villa, M., Henri, H., Desouhant, E. and Vavre, F. (2020) Experimental evolution of virulence and associated traits in a Drosophila melanogaster – Wolbachia symbiosis. bioRxiv, 2020.04.26.062265, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.04.26.062265
[2] Reyserhove, L., Samaey, G., Muylaert, K., Coppé, V., Van Colen, W., and Decaestecker, E. (2017). A historical perspective of nutrient change impact on an infectious disease in Daphnia. Ecology, 98(11), 2784-2798. doi: https://doi.org/10.1002/ecy.1994
[3] Ebert, D., and Bull, J. J. (2003). Challenging the trade-off model for the evolution of virulence: is virulence management feasible?. Trends in microbiology, 11(1), 15-20. doi: https://doi.org/10.1016/S0966-842X(02)00003-3
[4] Houwenhuyse, S., Macke, E., Reyserhove, L., Bulteel, L., and Decaestecker, E. (2018). Back to the future in a petri dish: Origin and impact of resurrected microbes in natural populations. Evolutionary Applications, 11(1), 29-41. doi: https://doi.org/10.1111/eva.12538
[5] Day, T., and Gandon, S. (2007). Applying population‐genetic models in theoretical evolutionary epidemiology. Ecology Letters, 10(10), 876-888. doi: https://doi.org/10.1111/j.1461-0248.2007.01091.x
[6] Alizon, S., Hurford, A., Mideo, N., and Van Baalen, M. (2009). Virulence evolution and the trade‐off hypothesis: history, current state of affairs and the future. Journal of evolutionary biology, 22(2), 245-259. doi: https://doi.org/10.1111/j.1420-9101.2008.01658.x
[7] Alizon, S., de Roode, J. C., and Michalakis, Y. (2013). Multiple infections and the evolution of virulence. Ecology letters, 16(4), 556-567. doi: https://doi.org/10.1111/ele.12076
[8] Decaestecker, E., and King, K. (2019). Red queen dynamics. Reference module in earth systems and environmental sciences, 3, 185-192. doi: https://doi.org/10.1016/B978-0-12-409548-9.10550-0
[9] Kirk, D., Jones, N., Peacock, S., Phillips, J., Molnár, P. K., Krkošek, M., and Luijckx, P. (2018). Empirical evidence that metabolic theory describes the temperature dependency of within-host parasite dynamics. PLoS biology, 16(2), e2004608. doi: https://doi.org/10.1371/journal.pbio.2004608
[10] Frankel-Bricker, J., Song, M. J., Benner, M. J., and Schaack, S. (2019). Variation in the microbiota associated with Daphnia magna across genotypes, populations, and temperature. Microbial ecology, 1-12. doi: https://doi.org/10.1007/s00248-019-01412-9

Heliconius butterflies are famous for their colorful wing patterns acting as a warning of their chemical defenses [1]. Most species are involved in Müllerian mimicry assemblies, as predators learn to associate common wing patterns with unpalatability and preferentially target rare variants. Such positive-frequency dependent selection homogenizes wing patterns at different localities, and in several species, all individuals within a community belong to the same morph [2]. In this respect, H. numata stands out. This species shows stable local polymorphism across multiple localities, with local populations home to up to seven distinct morphs [2]. Although a balance between migration and local positive-frequency dependent selection can allow some degree of local polymorphism, theory suggests that this occurs only when migration is within a narrow window [3].
One factor that potentially enhances local polymorphism in H. numata is disassortative mating. Mate choice assays have in fact revealed that females of this species tend to reject males with the same wing pattern [4]. However the evolution of such mating behavior and its effect on polymorphism remain unclear when selection is locally positive-frequency dependent. Using a mathematical model, Maisonneuve et al. [5] clarify the conditions that favor the evolution of disassortative mating in the complicated system of H. numata. In particular, they investigate whether the genetic basis of wing colour can favor the emergence of disassortative mating. Variation in wing pattern in H. numata is controlled by the supergene P, which is a single genomic region harboring multiple protein coding genes that have ceased to recombine due to chromosomal inversions [6]. If such remarkable genetic configuration allows for the co-adaptation of multiple loci participating to a complex phenotype such as wing color pattern, the absence of recombination can also result in the accumulation of deleterious mutations [7]. In fact, alleles at the P locus have been associated with a recessive genetic load, leading to a fitness advantage for heterozygotes at this locus [8]. Can this fitness advantage to heterozygotes lead to the evolution of disassortative mating? And if so, can such evolution lead to the maintenance of local polymorphism in spite of strong positive frequency-dependent selection?
To investigate these questions, Maisonneuve et al. [5] model evolution at two loci, one is the P locus for wing pattern, and the other influences mating behavior. The population is divided among two connected patches that differ in their butterfly communities, so that different alleles at the P locus are favored by positive frequency-dependent selection in different patches. The different alleles at the P locus are ordered in dominance relationships such that the most dominant over wing color pattern are also those with the highest load. By tracking the dynamics of haplotype frequencies in the population, the authors first show that disassortative mating readily evolves via the invasion of an allele causing females carrying it to reject males that resemble them phenotypically. Such “self-referencing” mechanism of mate choice, however, has never been reported and has been argued to be rare due to its complicated nature [9].
Maisonneuve et al. [5] then compare the evolution of disassortative mating via two alternative mechanisms: attraction and rejection. In these cases, alleles at the mating locus determine attraction to or rejection of specific phenotypes (e.g., under attraction rule, allele “B” encodes attraction to males with phenotype B). With the P and mating loci fully linked, disassortative mating can evolve under all three mechanisms (self-referencing, attraction and rejection), but tends to be less prevalent at equilibrium under attraction rule. This in turn results in the maintenance of less genetic variation under attraction compared to the other mating mechanisms. The loss of variation that occurs under attraction rules is due to a combination of dominance relationships between alleles at the P locus and the searching cost to females in finding rare types of males. When a particular wing pattern, say B, is only expressed in homozygotic form, B males are relatively rare. Females that carry the allele at the mating locus causing them to be attracted to such males then suffer a fitness cost due to lost mating opportunities. This mating allele is therefore purged, and in turn so is the recessive allele for B phenotype at the P locus. Under self-referencing and rejection rules, however, choosy females only reject males of a specific phenotype. They can therefore potentially mate with larger pool of males than females attracted to a single type. As a result, self-referencing and rejection rules are less sensitive to demographic effects and so are more conducive to disassortative mating evolution.
In their final analysis, Maisonneuve et al. [5] investigate the influence of recombination among the P and mating loci. They show that recombination has different effects on disassortative mating evolution depending on the mechanism of mate choice. Under the self-referencing rule, loose linkage leads to higher levels of disassortative mating and polymorphism than when linkage is tight. Under attraction or rejection rule, however, even very limited recombination completely inhibits the evolution of disassortative mating. This is because, with alleles at the mating locus coding for attraction/rejection to specific males, recombination breaks the association between the P and mating loci necessary for disassortative mating. By contrast, disassortative mating via a self-referencing rule does not depend on the linkage among the P and mating loci: females choose males that are different to themselves independently from the alleles they carry at the P locus.
Taken together, Maisonneuve et al.’s analyses [5] show that disassortative mating can readily evolve in a system like H. numata, but that this evolution depends on the genetic architecture of mating behavior. The architectures that are more conducive to the evolution of disassortative mating are: (1) epistatic interactions among the P and mating loci such that females are able to recognize their own phenotype and base their mating decision upon this information (self-referencing rule); and (2) full linkage among the P supergene and a mating locus that triggers rejection of a specific color pattern. While the mechanisms behind disassortative mating remain to be elucidated, assortative mating seems to rely on alleles triggering attraction to specific cues with variation in attraction and cues linked together [10]. These observations support the notion that disassortative mating is due to alleles causing rejection, in tight linkage to the P locus. If so, mating loci would in fact be part of the P supergene, thus controlling not only intricate wing color pattern but also mating behavior.
Beyond the specific system of H. numata, Maisonneuve et al.’s study [5] helps understand the evolution of disassortative mating and its association with the genetic architecture of correlated traits. In particular, Maisonneuve et al. [5] expands the role of supergenes for ecologically relevant traits to mating behavior, further bolstering the relevance of these remarkable genetic elements in the maintenance of variation in complex and elaborate phenotypes.

References

[1] Merrill, R M, K K Dasmahapatra, J W Davey, D D Dell'Aglio, J J Hanly, B Huber, C D Jiggins, et al. (2015). The Diversification of Heliconius butterflies: What Have We Learned in 150 Years? Journal of Evolutionary Biology 28 (8), 1417–38. https://doi.org/10.1111/jeb.12672.
[2] Joron M, IR Wynne, G Lamas, and J Mallet (1999). Variable selection and the coexistence of multiple mimetic forms of the butterfly Heliconius numata. Evolutionary Ecology 13, 721– 754. https://doi.org/10.1023/A:1010875213123
[3] Joron M and Y Iwasa (2005). The evolution of a Müllerian mimic in a spatially distributed community. Journal of Theoretical Biology 237, 87–103. https://doi.org/10.1016/j.jtbi.2005.04.005
[4] Chouteau M, V Llaurens, F Piron-Prunier, and M Joron (2017). Polymorphism at a mimicry su- pergene maintained by opposing frequency-dependent selection pressures. Proceedings of the National Academy of Sciences 114, 8325–8329. https://doi.org/10.1073/pnas.1702482114
[5] Maisonneuve, L, Chouteau, M, Joron, M and Llaurens, V. (2020). Evolution and genetic architecture of disassortative mating at a locus under heterozygote advantage. bioRxiv, 616409, ver. 9 peer-reviewed and recommended by PCI Evolutionary Biology. https://doi.org/10.1101/616409
[6] Joron M, L Frezal, RT Jones, NL Chamberlain, SF Lee, CR Haag, A Whibley, M Becuwe, SW Baxter, L Ferguson, et al. (2011). Chromosomal rearrangements maintain a polymorphic super- gene controlling butterfly mimicry. Nature 477, 203. https://doi.org/10.1038/nature10341
[7] Schwander T, R Libbrecht, and L Keller (2014). Supergenes and Complex Phenotypes.” Current Biology. 24 (7), 288–94. https://doi.org/10.1016/j.cub.2014.01.056.
[8] Jay P, M Chouteau, A Whibley, H Bastide, V Llaurens, H Parrinello, and M Joron (2019). Mutation accumulation in chromosomal inversions maintains wing pattern polymorphism in a butterfly. bioRxiv. https://doi.org/ 10.1101/736504.
[9] Kopp M, MR Servedio, TC Mendelson, RJ Safran, RL Rodrıguez, ME Hauber, EC Scordato, LB Symes, CN Balakrishnan, DM Zonana, et al. (2018). Mechanisms of assortative mating in speciation with gene flow: connecting theory and empirical research. The American Naturalist 191, 1–20. https://doi.org/10.1086/694889
[10] Merrill RM, P Rastas, SH Martin, MC Melo, S Barker, J Davey, WO McMillan, and CD Jiggins (2019). Genetic dissection of assortative mating behavior. PLoS biology 17, e2005902. https://doi.org/10.1371/journal.pbio.2005902

Sequencing and analyzing SARS-Cov-2 genomes in nearly real time has the potential to quickly confirm (and inform) our knowledge of, and response to, the current pandemic [1,2]. In this manuscript [3], Danesh and colleagues use the earliest set of available SARS-Cov-2 genome sequences available from France to make inferences about the timing of the major epidemic wave, the duration of infections, and the efficacy of lockdown measures. Their phylodynamic estimates -- based on fitting genomic data to molecular clock and transmission models -- are reassuringly close to estimates based on 'traditional' epidemiological methods: the French epidemic likely began in mid-January or early February 2020, and spread relatively rapidly (doubling every 3-5 days), with people remaining infectious for a median of 5 days [4,5]. These transmission parameters are broadly in line with estimates from China [6,7], but are currently unknown in France (in the absence of contact tracing data). By estimating the temporal reproductive number (Rt), the authors detected a slowing down of the epidemic in the most recent period of the study, after mid-March, supporting the efficacy of lockdown measures.
Along with the three other reviewers of this manuscript, I was impressed with the careful and exhaustive phylodynamic analyses reported by Danesh et al. [3]. Notably, they take care to show that the major results are robust to the choice of priors and to sampling. The authors are also careful to note that the results are based on a limited sample size of SARS-Cov-2 genomes, which may not be representative of all regions in France. Their analysis also focused on the dominant SARS-Cov-2 lineage circulating in France, which is also circulating in other countries. The variations they inferred in epidemic growth in France could therefore be reflective on broader control policies in Europe, not only those in France. Clearly more work is needed to fully unravel which control policies (and where) were most effective in slowing the spread of SARS-Cov-2, but Danesh et al. [3] set a solid foundation to build upon with more data. Overall this is an exemplary study, enabled by rapid and open sharing of sequencing data, which provides a template to be replicated and expanded in other countries and regions as they deal with their own localized instances of this pandemic.

References

[1] Grubaugh, N. D., Ladner, J. T., Lemey, P., Pybus, O. G., Rambaut, A., Holmes, E. C., & Andersen, K. G. (2019). Tracking virus outbreaks in the twenty-first century. Nature microbiology, 4(1), 10-19. doi: 10.1038/s41564-018-0296-2
[2] Fauver et al. (2020) Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States. Cell, 181(5), 990-996.e5. doi: 10.1016/j.cell.2020.04.021
[3] Danesh, G., Elie, B., Michalakis, Y., Sofonea, M. T., Bal, A., Behillil, S., Destras, G., Boutolleau, D., Burrel, S., Marcelin, A.-G., Plantier, J.-C., Thibault, V., Simon-Loriere, E., van der Werf, S., Lina, B., Josset, L., Enouf, V. and Alizon, S. and the COVID SMIT PSL group (2020) Early phylodynamics analysis of the COVID-19 epidemic in France. medRxiv, 2020.06.03.20119925, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/2020.06.03.20119925
[4] Salje et al. (2020) Estimating the burden of SARS-CoV-2 in France. hal-pasteur.archives-ouvertes.fr/pasteur-02548181
[5] Sofonea, M. T., Reyné, B., Elie, B., Djidjou-Demasse, R., Selinger, C., Michalakis, Y. and Samuel Alizon, S. (2020) Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France. medRxiv, 2020.05.22.20110593. doi: 10.1101/2020.05.22.20110593
[6] Rambaut, A. (2020) Phylogenetic analysis of nCoV-2019 genomes. virological.org/t/phylodynamic-analysis-176-genomes-6-mar-2020/356
[7] Li et al. (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med, 382: 1199-1207. doi: 10.1056/NEJMoa2001316

In most animal species, males and females display distinct survival prospect, a phenomenon known as sex gap in longevity (SGL, Marais et al. 2018). The study of SGLs is crucial not only for having a full picture of the causes underlying organisms’ health, aging and death but also to initiate the development of sex-specific anti-aging interventions in humans (Austad and Bartke 2015). Three non-mutually evolutionary causes have been proposed to underlie SGLs (Marais et al. 2018). First, SGLs could be the consequences of sex-differences in life history strategies. For example, evolving dimorphic traits (e.g. body size, ornaments or armaments) may imply unequal physiological costs (e.g. developmental, maintenance) between the sexes and this may result in differences in longevity and aging. Second, mitochondria are usually transmitted by the mother and thus selection is blind to mitochondrial deleterious mutations affecting only males. Such mutations can freely accumulate in the mitochondrial genome and may reduce male longevity, a phenomenon called the mother’s curse (Frank and Hurst 1996). Third, in species with sex chromosomes, all recessive deleterious mutations will be expressed on the single X chromosome in XY males and may reduce their longevity (the unguarded X effect). In addition, the numerous transposable elements (TEs) on the Y chromosome may affect aging. TE activity is normally repressed by epigenetic regulation (DNA methylation, histone modifications and small RNAs). However, it is known that this regulation is disrupted with increasing age. Because of the TE-rich Y chromosome, more TEs may become active in old males than in old females, generating more somatic mutations, accelerating aging and reducing longevity in males (the toxic Y effect, Marais et al. 2018).
The relative contributions of these different effects to SGLs remain unknown. Sex-differences in life history strategies have been considered as the most important cause of SGLs for long (Tidière et al. 2015) but this effect remain equivocal (Lemaître et al. 2020) and cannot explain alone the diversity of patterns observed across species (Marais et al. 2018). Similarly, while studies in Drosophila and humans have shown that the mother’s curse contributes to SGLs in those organisms (e.g. Milot et al. 2007), its contribution may not be strong. Recently, two large-scale comparative analyses have shown that in species with XY chromosomes males show a shorter lifespan compared to females, while in species with ZW chromosomes (a system in which the female are the heterogametic sex and are ZW, and the males ZZ) the opposite pattern is observed (Pipoly et al. 2015; Xirocostas et al. 2020). Apart from these correlational studies, very little experimental tests of the effect of sex chromosomes on longevity have been conducted. In Drosophila, the evidence suggests that the unguarded X effect does not contribute to SGLs (Brengdahl et al. 2018). Whether a toxic Y effect exists in this species was unknown.
In a very elegant study, Brown et al. (2020) provided strong evidence for such a toxic Y effect in Drosophila melanogaster. First, they checked that in the D. melanogaster strain that they were studying (Canton-S), males were indeed dying younger than females. They also confirmed that in this strain, as in others, the male genomes include more repeats and heterochromatin than the female ones using cytometry. A careful analysis of the heterochromatin (using H3K9me2, a repressive histone modification typical of heterochromatin, as a proxy) in old flies revealed that heterochromatin loss was much more important in males than in females, in particular on the Y chromosome (but also to a lesser extent at the pericentromric regions of the autosomes). This change in heterochromatin had two outputs, they found. First, the expression of the genes in those regions was affected. They highlighted that many of such genes are involved in immunity and regulation with a potential impact on longevity. Second, they found a striking TE reactivation. These two effects were stronger in males. While females showed clear reactivation of 6 TEs, with the total fraction of repeats in the transcriptome going from 2% (young females) to 4.6% (old females), males experienced the reactivation of 32 TEs, with the total fraction of repeats in the transcriptome going from 1.6% (young males) to 5.8% (old males). It appeared that most of these TEs are Y-linked. And when focusing on Y-linked repeats, they found that 32 Y-linked TEs became upregulated during male aging and the fraction of Y-linked TEs in the transcriptome increased ninefold.
All these observations clearly suggested that male longevity was decreased because of a toxic Y effect. To really uncover a causal relationship between having a Y chromosome and shorter longevity, Brown et al. (2020) artificially produced flies with atypical karyotypes: X0 males, XXY females and XYY males. This is very interesting as they could uncouple the effect of the phenotypical sex (being male or female) and having a Y chromosome or not, as in fruit flies sex is determined not by the Y chromosome but by the X/autosome ratio. Their results are striking. They found that longevity of the X0 males was the highest (higher than XX females in fact), and that of the XYY males the lowest. Females XXY had intermediate longevities. Importantly, this was found to be robust to genomic background as results were the same using crosses from different strains. When analysing TEs of these flies, they found a particularly strong expression of the Y-linked TEs in old XXY and XYY flies. Interestingly, in young XXY and XYY flies Y-linked TEs expression was also strong, suggesting the chromatin regulation of the Y chromosome is disrupted in these flies.
This work points to the idea that SGLs in D. melanogaster are mainly explained by the toxic Y effect. The molecular details however remain to be elucidated. The effect of the Y chromosome on aging might be more complex than envisioned in the toxic Y model presented above. Brown et al. (2020) indeed found that heterochromatin loss was globally faster in males, both at the Y chromosome and the autosomes. The organisation of the nucleus, in particular of the nucleolus, which is involved in heterochromatin maintenance, involves the sex chromosomes in D. melanogaster as discussed in the paper, and may explain this observation. The epigenetic status of the Y chromosome is known to affect that of all the autosomes in Drosophila (Lemos et al. 2008). Also, in Brown et al. (2020) most of the work (in particular the genomic part) has been done on Canton-S. Only D. melanogaster was studied but limited data suggest different Drosophila species may have different SGLs. The TE analysis is known to be tricky, different tools to analyse TE expression exist (e.g. Lerat et al. 2017; Lanciano and Cristofari 2020). Future work should focus on testing the toxic Y effect on other D. melanogaster strains and other Drosophila species, using different tools to study TE expression, and on dissecting the molecular details of the toxic Y effect.

References

Austad, S. N., and Bartke, A. (2015). Sex differences in longevity and in responses to anti-aging interventions: A Mini-Review. Gerontology, 62(1), 40–46. 10.1159/000381472
Brengdahl, M., Kimber, C. M., Maguire-Baxter, J., and Friberg, U. (2018). Sex differences in life span: Females homozygous for the X chromosome do not suffer the shorter life span predicted by the unguarded X hypothesis. Evolution; international journal of organic evolution, 72(3), 568–577. 10.1111/evo.13434
Brown, E. J., Nguyen, A. H., and Bachtrog, D. (2020). The Y chromosome may contribute to sex-specific ageing in Drosophila. Nature ecology and evolution, 4(6), 853–862. 10.1038/s41559-020-1179-5 or preprint link on bioRxiv
Frank, S. A., and Hurst, L. D. (1996). Mitochondria and male disease. Nature, 383(6597), 224. 10.1038/383224a0
Lanciano, S., and Cristofari, G. (2020). Measuring and interpreting transposable element expression. Nature reviews. Genetics, 10.1038/s41576-020-0251-y. Advance online publication. 10.1038/s41576-020-0251-y
Lemaître, J. F., Ronget, V., Tidière, M., Allainé, D., Berger, V., Cohas, A., Colchero, F., Conde, D. A., Garratt, M., Liker, A., Marais, G., Scheuerlein, A., Székely, T., and Gaillard, J. M. (2020). Sex differences in adult lifespan and aging rates of mortality across wild mammals. Proceedings of the National Academy of Sciences of the United States of America, 117(15), 8546–8553. 10.1073/pnas.1911999117
Lemos, B., Araripe, L. O., and Hartl, D. L. (2008). Polymorphic Y chromosomes harbor cryptic variation with manifold functional consequences. Science (New York, N.Y.), 319(5859), 91–93. 10.1126/science.1148861
Lerat, E., Fablet, M., Modolo, L., Lopez-Maestre, H., and Vieira, C. (2017). TEtools facilitates big data expression analysis of transposable elements and reveals an antagonism between their activity and that of piRNA genes. Nucleic acids research, 45(4), e17. 10.1093/nar/gkw953
Marais, G., Gaillard, J. M., Vieira, C., Plotton, I., Sanlaville, D., Gueyffier, F., and Lemaitre, J. F. (2018). Sex gap in aging and longevity: can sex chromosomes play a role?. Biology of sex differences, 9(1), 33. 10.1186/s13293-018-0181-y
Milot, E., Moreau, C., Gagnon, A., Cohen, A. A., Brais, B., and Labuda, D. (2017). Mother's curse neutralizes natural selection against a human genetic disease over three centuries. Nature ecology and evolution, 1(9), 1400–1406. 10.1038/s41559-017-0276-6
Pipoly, I., Bókony, V., Kirkpatrick, M., Donald, P. F., Székely, T., and Liker, A. (2015). The genetic sex-determination system predicts adult sex ratios in tetrapods. Nature, 527(7576), 91–94. 10.1038/nature15380
Tidière, M., Gaillard, J. M., Müller, D. W., Lackey, L. B., Gimenez, O., Clauss, M., and Lemaître, J. F. (2015). Does sexual selection shape sex differences in longevity and senescence patterns across vertebrates? A review and new insights from captive ruminants. Evolution; international journal of organic evolution, 69(12), 3123–3140. 10.1111/evo.12801
Xirocostas, Z. A., Everingham, S. E., and Moles, A. T. (2020). The sex with the reduced sex chromosome dies earlier: a comparison across the tree of life. Biology letters, 16(3), 20190867. 10.1098/rsbl.2019.0867

Whether speciation through ecological specialization occurs has been a thriving research area ever since Mayr (1942) stated this to play a central role. In herbivorous insects, ecological specialization is most likely to happen through host plant differentiation (Funk et al. 2002). Therefore, after Dorothy Pashley had identified two host strains in the Fall armyworm (FAW), Spodoptera frugiperda, in 1988 (Pashley 1988), researchers have been trying to decipher the evolutionary history of these strains, as this seems to be a model species in which speciation is currently occurring through host plant specialization. Even though FAW is a generalist, feeding on many different host plant species (Pogue 2002) and a devastating pest in many crops, Pashley identified a so-called corn strain and a so-called rice strain in Puerto Rico. Genetically, these strains were found to differ mostly in an esterase, although later studies showed additional genetic differences and markers, mostly in the mitochondrial COI and the nuclear TPI. Recent genomic studies showed that the two strains are overall so genetically different (2% of their genome being different) that these two strains could better be called different species (Kergoat et al. 2012). So far, the most consistent differences between the strains have been their timing of mating activities at night (Schoefl et al. 2009, 2011; Haenniger et al. 2019) and hybrid incompatibilities (Dumas et al. 2015; Kost et al. 2016). Whether and to what extent host plant preference or performance contributed to the differentiation of these sympatrically occurring strains has remained unclear.
In the current study, Orsucci et al. (2020) performed oviposition assays and reciprocal transplant experiments with both strains to measure fitness effects, in combination with a comprehensive RNAseq experiment, in which not only lab reared larvae were analysed, but also field-collected larvae. When testing preference and performance on the two host plants corn and rice, the authors did not find consistent fitness differences between the two strains, with both strains performing less on rice plants, although larvae from the corn strain survived more on corn plants than those from the rice strain. These results mostly confirm findings of a number of investigations over the past 30 years, where no consistent differences on the two host plants were observed (reviewed in Groot et al. 2016). However, the RNAseq experiments did show some striking differences between the two strains, especially in the reciprocally transplanted larvae, where both strains had been reared on rice or on corn plants for one generation: both strains showed transcriptional responses that correspond to their respective putative host plants, i.e. overexpression of genes involved in digestion and metabolic activity, and underexpression of genes involved in detoxification, in the corn strain on corn and in the rice strain on rice. Interestingly, similar sets of genes were found to be overexpressed in the field-collected larvae with which a RNAseq experiment was conducted as well.
The most interesting result of the study performed by Orsucci et al. (2020) is the underexpression in the corn strain of so-called numts, small genomic sequences that corresponded to fragments of the mitochondrial COI and COIII. These two numts were differentially expressed in the two strains in all RNAseq experiments analysed. This result coincides with the fact that the COI is one of the main diagnostic markers to distinguish these two strains. The authors suggestion that a difference in energy production between these two strains may be linked to a shift in host plant preference matches their finding that rice plants seem to be less suitable host plants than corn plants. However, as the lower suitability of rice plants was true for both strains, it remains unclear whether and how this difference could be linked to possible host plant differentiation between the strains. The authors also suggest that COI and potentially other mitochondrial genes may be the original target of selection between these two strains. This is especially interesting in light of the fact that field-collected larvae have frequently been found to have a rice strain mitochondrial genotype and a corn strain nuclear genotype, also in this study, while in the lab (female rice strain x male corn strain) hybrid females (i.e. females with a rice strain mitochondrial genotype and a corn strain nuclear genotype) are behaviorally sterile (Kost et al. 2016). Whether and how selection on mitochondrial genes or on mitonuclear interactions has indeed affected the evolution of these strains in the New world, and will affect the evolution of FAW in newly invaded habitats in the Old world, including Asia and Australia – where, so far, only corn strain and (female rice strain x male corn strain) hybrids have been found (Nagoshi 2019), will be a challenging research question for the coming years.

References

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