Sex chromosomes evolve as recombination is suppressed between the X and Y chromosomes. The loss of recombination on the sex-limited chromosome (the Y in mammals) leads to degeneration of both gene expression and gene content for many genes [1]. Loss of gene expression or content from the Y chromosome leads to differences in gene dose between males and females for X-linked genes. Because expression levels are often correlated with gene dose [2], these hemizygous genes have a lower expression levels in the heterogametic sex. This in turn disrupts the stoichiometric balance among genes in protein complexes that have components on both the sex chromosomes and autosomes [3], which could have serious deleterious consequences for the heterogametic sex.
To overcome these deleterious effects of degeneration, the expression levels of dosage sensitive X-linked genes, and in some organisms, entire X chromosomes, are compensated, the expression of the single copy of in the heterogametic sex being increased. Dosage compensation for such genes has evolved in several species, restoring similar expression levels as in the ancestral state in males and/or equal gene expression in males and females [4-8]. The mechanisms for dosage compensation are variable among species and their evolutionary paths are not fully understood, as the few model sex chromosomes studied so far have old, and highly degenerate sex chromosomes [4-7].
Muyle et al. [9] studied the young sex chromosomes of the plant Silene latifolia, which has young sex chromosomes (4 MY) and highly variable dosage compensation [10, 11]. The authors used both an outgroup species without sex chromosomes for obtaining a proxy for ancestral expression levels before Y degeneration, and implemented methods to identify sex-linked genes and disentangle paternal versus maternal allele expression [12]. Using these elements, Muyle et al. [9] reveal upregulation of maternal X alleles in both males and females in the young S. latifolia sex chromosomes [9], possibly by genomic imprinting. The upregulation in both sexes of the maternal X alleles likely yields non-optimal gene expression in females, which is strikingly consistent with the theoretical first step of dosage compensation as postulated by Ohno [8], which predicts restoration of ancestral expression in males, over-expression in females, and unequal expression in the two sexes. These findings provide surprising insight into the earliest stages of dosage compensation, one of the most intriguing aspects of evolutionary biology.

References
[1] Bachtrog D. 2013. Y chromosome evolution: emerging insights into processes of Y-chromosome degeneration? Nature Reviews Genetics 14: 113–124. doi: 10.1038/nrg3366
[2] Malone JH, Cho D-Y, Mattiuzzo NR, Artieri CG, Jiang L, Dale RK, Smith HE, McDaniel J, Munro S, Salit M, Andrews J, Przytycka TM and Oliver B. 2012. Mediation of Drosophila autosomal dosage effects and compensation by network interactions. Genome Biology 13: R28. doi: 10.1186/gb-2012-13-4-r28
[3] Pessia E, Makino T, Bailly-Bechet M, McLysaght A and Marais GAB. 2012. Mammalian X chromosome inactivation evolved as a dosage-compensation mechanism for dosage-sensitive genes on the X chromosome. Proceedings of the National Academy of Sciences of the United States of America. 109: 5346–5351. doi: 10.1073/pnas.1116763109.
[4] Graves JAM. 2016. Evolution of vertebrate sex chromosomes and dosage compensation. Nature Reviews Genetics 17: 33–46. doi: 10.1038/nrg.2015.2
[5] Mank JE. 2013. Sex chromosome dosage compensation: definitely not for everyone. Trends in Genetics 12: 677–683. doi: 10.1016/j.tig.2013.07.005
[6] Pessia E and Engelstädter J. 2014. The evolution of X chromosome inactivation in mammals: the demise of Ohno’s hypothesis? Cellular and Molecular Life Sciences 71: 1383–1394. doi: 10.1007/s00018-013-1499-6
[7] Muyle A, Shearn R and Marais GAB. 2017. The evolution of sex chromosomes and dosage compensation in plants. Genome Biology and Evolution 9: 627–645. doi: 10.1093/gbe/evw282
[8] Ohno S. 1967. Sex chromosomes and sex linked genes. Springer, Berlin Heidelberg New York.
[9] Muyle A, Zemp N, Fruchard C, Cegan R, Vrana J, Deschamps C, Tavares R, Picard F, Hobza R, Widmer A and Marais GAB. 2018. Genomic imprinting mediates dosage compensation in a young plant XY system. bioRxiv 118695, ver. 6 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/179044
[10] Papadopulos AST, Chester M, Ridout K and Filatov DA. 2015. Rapid Y degeneration and dosage compensation in plant sex chromosomes. Proceedings of the National Academy of Sciences of the United States of America 112: 13021–13026. doi: 10.1073/pnas.1508454112
[11] Bergero R, Qiu S and Charlesworth D. 2015. Gene loss from a plant sex chromosome system. Current Biology 25: 1234–1240. doi: 10.1016/j.cub.2015.03.015
[12] Muyle A, Kafer J, Zemp N, Mousset S, Picard F and Marais GAB. 2016. SEX-DETector: a probabilistic approach to study sex chromosomes in non-model organisms. Genome Biology and Evolution 8: 2530–2543. doi: 10.1093/gbe/evw172

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 breeder’s equation is a classical equation in evolutionary theory, and basically states that the response of a trait to selection is equal to the strength of selection on this trait multiplied by the heritability of the trait. There can be several reasons why reality does not conform to a narrow interpretation of this equation, but in spite of that, let us actually indulge in a broad interpretation of the equation. Typically, the heritability of a trait is interpreted to refer to the genetic basis of a trait (the additive genetic variance). However, a broad interpretation of the breeder’s equation would be that if selection acts on a trait and that trait is somehow heritable, we should expect to observe an evolutionary change in that trait. In other words, the source and mechanism of inheritance does not matter for the breeder’s equation to have applicability.

In this paper, Pujol et al. (2025) follow this rationale for epigenetic variation, and more specifically for differentially methylated regions in the model plant Arabidopsis thaliana. They used 120 lines which vary across 126 differentially methylated regions of the genome, but are genetically identical, thereby eliminating additive genetic variance from the heritability component of the breeder’s equation. Previous studies have already shown that these differentially methylated regions are associated with different phenotypes, suggesting they somehow functionally affect the development of the plants. Previous studies have also shown that these differentially methylated regions are stably transmitted across generation, i.e., that they are heritable. This predicts that if one selects for different phenotypes, there should be an evolutionary response in the phenotypes, and an associated shift in the differentially methylated regions that are responsible for the phenotypic variation.

By and large, this is what Pujol et al. (2025) found. They selected divergently on four different plants traits (biomass, rosette size, flowering time, and height at first fruit), in two different populations, and with two different selection strengths. For most traits they found a phenotypic shift, and this largely corresponded with associated shifts in the differentially methylated regions. The patterns were very congruent between the different selection strengths, and very similar for the two different populations. We can therefore confidently conclude that epigenetic variation has effects on fitness-relevant phenotypic traits, that therefore natural selection can act on this epigenetic variation, and if so that this would cause an evolutionary change in the phenotypes under selection and in the differentially methylated regions causing this phenotypic variation. I.e., the breeder’s equation can indeed by applied to more than just genetically heritable variation, it can also be applied to non-genetically heritable variation. 

While this seems like a very obvious result, there are still very few studies that show how selection on non-genetic heritable variation results in a between-generation shift in this non-genetic heritable variation. In that sense, this study can function as a case in favour of a broader interpretation of evolution; to refer to a heritable between-generation change in the composition of a population, independent of whether the heredity is genetic or non-genetic. Critics might say that this study has only limited validity, since the epigenetic variation has been artificially increased in the used plant lines, and may not reflect naturally occurring variation. Indeed, more studies on natural selection on epigenetic variation are called for, but these will have the added complication that they need to correct for genetic variation, which is often correlated to epigenetic variation. Here, the authors could exclude genetic variation by working with epigenetically different lines that were genetically identical.

The study stands out in yet another respect – it uses a highly unusual and apparently novel selection design (although the authors do not know for sure if their idea is actually novel). Typically, in a selection study only a subset of parents is allowed to reproduce out of the total set of potential parents. If one wants to select for different traits separately, or with different selection strengths, separate selection studies need to be undertaken. Here the authors took a different approach. They let all their parents reproduce (3 offspring per plant), and phenotyped all offspring. They then, virtually, selected for different traits or for different selection strengths in the parents by excluding from their dataset the offspring of parents that are selected not to reproduce – after they had actually already reproduced! It is almost as if selection acts with a time delay, destroying not only the selected parents but also their offspring. This has the benefit that a single dataset is generated (all phenotyped offspring of all parents), and then selection can be exerted as often as one wants, for example for distinct types of selection, or on different (combinations) of traits. While the initial effort to phenotype offspring of all potential parents is large, and exponentially so if multiple generations are selected, the subsequent evaluation of distinct types of selection on any of the phenotyped traits is virtually effortless. For example, the authors applied divergent selection, but could reuse their dataset to apply stabilising selection. This novel selection design may therefore be a very attractive approach for many other researchers.

References

Benoit Pujol, Mathieu Latutrie, Pierick Mouginot, Nelia Luviano- Aparicio, Jésaëlle Piquet, Sara Marin, and Stéphane Maury (2025) Experimental evidence for short term directional selection of epigenetic trait variation. Zenodo, ver.2 peer-reviewed and recommended by PCI Evolutionary Biology https://doi.org/10.5281/zenodo.15227609

Biological invasions are natural experiments, and often show that evolution can affect dynamics in important ways [1-3]. While we often think of invasions as a conservation problem stemming from anthropogenic introductions [4,5], biological invasions are much more commonplace than this, including phenomena as diverse as natural range shifts, the spread of novel pathogens, and the growth of tumors. A major question across all these settings is which set of traits determine the ability of a population to invade new space [6,7]. Traits such as: increased growth or reproductive rate, dispersal ability and ability to defend from predation often show large evolutionary shifts across invasion history [1,6,8]. Are such multi-trait shifts driven by selection on multiple traits, or a correlated response by multiple traits to selection on one? Resolving this question is important for both theoretical and practical reasons [9,10]. But despite the importance of this issue, it is not easy to perform the necessary manipulative experiments [9].
Foucaud et al. [11] tackled this issue by performing experimental evolution on source populations of the invasive ladybug Harmonia axyridis. The authors tested if selection on a single trait could generate correlated responses in other life history traits. Specifically, they used experimental evolution to impose divergent selection on female mass, and reproductive timing. After ten generations, they found that selection for weight did not affect almost any other life history trait. However, nine generations of selection for faster reproduction led to correlated phenotypic changes in developmental, reproduction and survival rate of populations, although not always in the direction we might have expected. Despite this correlated response, none of their selected lines were able to fully recapitulate the trait shifts seen in natural invasions of this species. This implies that selection during natural invasions is operating on multiple traits; a finding in agreement with our growing understanding of how selection acts during introduction and invasion [12,13].
Populations undergoing a colonization process may also be subject to a multitude of different selective pressures [14,15]. The authors expanded their work in this direction by testing whether food availability alters the observed correlations between life history traits. The pervasiveness of genotype by environment interactions observed also points to a role for multiple selective pressures in shaping the suite of life-history shifts observed in wild ladybug populations. The work from Foucaud and colleagues [11] adds to a small but growing list of important studies that use experimental evolution to investigate how life-history traits evolve, and how they evolve during invasions in particular.

References

[1] Sakai, A.K., Allendorf, F.W., Holt, J.S. et al. (2001). The population biology of invasive species. Annual review of ecology and systematics, 32(1), 305-332. doi: 10.1146/annurev.ecolsys.32.081501.114037
[2] Hairston Jr, N. G., Ellner, S. P., Geber, M. A., Yoshida, T. and Fox, J. A. (2005). Rapid evolution and the convergence of ecological and evolutionary time. Ecology letters, 8(10), 1114-1127. doi: 10.1111/j.1461-0248.2005.00812.x
[3] Chuang, A. and Peterson, C. R. (2016). Expanding population edges: theories, traits, and trade‐offs. Global change biology, 22(2), 494-512. doi: 10.1111/gcb.13107
[4] Whitney, K. D. and Gabler, C. A. (2008). Rapid evolution in introduced species,‘invasive traits’ and recipient communities: challenges for predicting invasive potential. Diversity and Distributions, 14(4), 569-580. doi: 10.1111/j.1472-4642.2008.00473.x
[5] Catullo, R. A., Llewelyn, J., Phillips, B. L. and Moritz, C. C. (2019). The Potential for Rapid Evolution under Anthropogenic Climate Change. Current Biology, 29(19), R996-R1007. doi: 10.1016/j.cub.2019.08.028
[6] Suarez, A. V. and Tsutsui, N. D. (2008). The evolutionary consequences of biological invasions. Molecular Ecology, 17(1), 351-360. doi: 10.1111/j.1365-294X.2007.03456.x
[7] Deforet, M., Carmona-Fontaine, C., Korolev, K. S. and Xavier, J. B. (2019). Evolution at the edge of expanding populations. The American Naturalist, 194(3), 291-305. doi: 10.1086/704594
[8] Phillips, B. L., Brown, G. P., and Shine, R. (2010). Life‐history evolution in range‐shifting populations. Ecology, 91(6), 1617-1627. doi: 10.1890/09-0910.1
[9] Colautti, R. I. and Lau, J. A. (2015). Contemporary evolution during invasion: evidence for differentiation, natural selection, and local adaptation. Molecular ecology, 24(9), 1999-2017. doi: 10.1111/mec.13162
[10] Szűcs, M., Melbourne, B. A., Tuff, T., Weiss‐Lehman, C. and Hufbauer, R. A. (2017). Genetic and demographic founder effects have long‐term fitness consequences for colonising populations. Ecology Letters, 20(4), 436-444. doi: 10.1111/ele.12743
[11] Foucaud, J., Hufbauer, R. A., Ravigné, V., Olazcuaga, L., Loiseau, A., Ausset, A., Wang, S., Zang, L.-S., Lemenager, N., Tayeh, A., Weyna, A., Gneux, P., Bonnet, E., Dreuilhe, V., Poutout, B., Estoup, A. and Facon, B. (2020). How do invasion syndromes evolve? An experimental evolution approach using the ladybird Harmonia axyridis. bioRxiv, 849968 ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/849968
[12] Simons, A. M. (2003). Invasive aliens and sampling bias. Ecology Letters, 6(4), 278-280. doi: 10.1046/j.1461-0248.2003.00430.x
[13] Phillips, B. L. and Perkins, T. A. (2019). Spatial sorting as the spatial analogue of natural selection. Theoretical Ecology, 12(2), 155-163. doi: 10.1007/s12080-019-0412-9
[14] Lavergne, S. and Molofsky, J. (2007). Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proceedings of the National Academy of Sciences, 104(10), 3883-3888. doi: 10.1073/pnas.0607324104
[15] Moran, E. V. and Alexander, J. M. (2014). Evolutionary responses to global change: lessons from invasive species. Ecology Letters, 17(5), 637-649. doi: 10.1111/ele.12262

Parasitoid wasps have developed different mechanisms to increase their parasitic success, usually at the expense of host survival (Fellowes and Godfray, 2000). Eggs of these insects are deposited inside the juvenile stages of their hosts, which in turn deploy several immune response strategies to eliminate or disable them (Yang et al., 2020). Drosophila melanogaster protects itself against parasitoid attacks through the production of specific elongated haemocytes called lamellocytes which form a capsule around the invading parasite (Lavine and Strand, 2002; Rizki and Rizki, 1992) and the subsequent activation of the phenol-oxidase cascade leading to the release of toxic radicals (Nappi et al., 1995). On the parasitoid side, robust responses have evolved to evade host immune defenses as for example the Drosophila-specific endoparasite Leptopilina boulardi, which releases venom during oviposition that modifies host behaviour (Varaldi et al., 2006) and inhibits encapsulation (Gueguen et al., 2011; Martinez et al., 2012).
Studies have shown that the wasp parasitic capacity is correlated to venom presence and its content (Colinet et al., 2009; Poirié et al., 2014), including that evolution of venom protein composition is driven by different levels of host susceptibility to infection (Cavigliasso et al., 2019). However, it had not been determined to this day, if and how parasitic range can affect venom protein composition and to which extent host specialization requires broad-spectrum factors or a plethora of specialized components.
These outstanding questions are now approached in a study by Cavigliasso and colleagues (Cavigliasso et al., 2021), where they perform experimental evolution of L. boulardi for 9 generations exposing it to different Drosophila host species and genetic backgrounds (two strains of D. melanogaster, D. simulans and D. yakuba). The authors tested whether the parasitic success of each selection regime was host-specific and how they influenced venom composition in parasitoids. For the first part, infection outcomes were assayed for each selection regime when cross-infecting different hosts. To get a finer measurement of the mechanisms under selection, the authors differentiated three phenotypes: overall parasitic success, encapsulation inhibition and escape from capsule. Throughout the course of experimental evolution, only encapsulation inhibition did not show an improved response upon selection on any host. Importantly, the cross-infection scenario revealed a clear specificity to the selected host for each evolved resistance.
As for venom composition, a trend of differential evolution was detected between host species, although a significant part of that was due to a larger differentiation in the D. yakuba regime, which showed a completely different directionality. Importantly, the authors could identify some of the specific proteins targeted by the several selection regimes, whether selected or counter-selected for. Interestingly, the D. yakuba regime is the only case where the key parasitoid protein LbSPNy (Colinet et al., 2009) was not counter-selected and the only regime in which the overall venom composition did not evolve towards the Ism strain, one of the two ancestral strains of L. boulardi used in the study. It is possible that these two results are correlated, since LbSPNy has been described to inhibit activation of the phenoloxidase cascade in D. yakuba and is one of the most abundant proteins in the ISy venom, making it a good target for selection (Colinet et al., 2013). The authors also discuss the possibility that this difference is related to the geographical distribution of the strains of L. boulardi, since each coincide with either D. melanogaster or D. yakuba.
This methodologically broad work by Cavigliasso and colleagues constitutes an important experimental contribution towards the understanding of how parasitoid adaptation to specific hosts is achieved at different phenotypic and mechanistic levels. It provides compelling evidence that venom composition evolves differently in response to specific parasitic ranges, particularly considering the evolutionary difference between the selective hosts. In line with this result, it is also concluded that the majority of venom proteins selected are lineage-specific, although a few broad-spectrum factors could also be detected. 
The question of whether parasitic range can affect venom composition and parasitic success is still open to more contributions. A potentially interesting long-term direction will be to use a similar setup of experimental evolution on the generalist L. heterotoma (Schlenke et al., 2007) . On a more immediate horizon, comparing the venom evolution of both L. heterotoma and L. boulardi under selection with different hosts and under cross-infection scenarios could reveal interesting patterns. The recent sequencing of the L. boulardi genome together with the vast number of studies addressing mechanisms of Drosophila resistance to parasitoid infection, will enable the thorough characterization of the genetic basis of host-parasitoid interactions and the deeper understanding of these ubiquitous and economically-relevant relationships.
 
*This recommendation text has been co-written with Tânia F. Paulo who is not a recommender of PCI Evol Biol

References

Cavigliasso, F., Mathé-Hubert, H., Gatti, J.-L., Colinet, D. and Poirié, M. (2021) Parasitic success and venom composition evolve upon specialization of parasitoid wasps to different host species. bioRxiv, 2020.10.24.353417, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.10.24.353417

Cavigliasso, F., Mathé-Hubert, H., Kremmer, L., Rebuf, C., Gatti, J.-L., Malausa, T., Colinet, D., Poiré, M. and  Léne. (2019). Rapid and Differential Evolution of the Venom Composition of a Parasitoid Wasp Depending on the Host Strain. Toxins, 11(629). https://doi.org/10.3390/toxins11110629

Colinet, D., Deleury, E., Anselme, C., Cazes, D., Poulain, J., Azema-Dossat, C., Belghazi, M., Gatti, J. L. and  Poirié, M. (2013). Extensive inter- and intraspecific venom variation in closely related parasites targeting the same host: The case of Leptopilina parasitoids of Drosophila. Insect Biochemistry and Molecular Biology, 43(7), 601–611. https://doi.org/10.1016/j.ibmb.2013.03.010

Colinet, D., Dubuffet, A., Cazes, D., Moreau, S., Drezen, J. M. and  Poirié, M. (2009). A serpin from the parasitoid wasp Leptopilina boulardi targets the Drosophila phenoloxidase cascade. Developmental and Comparative Immunology, 33(5), 681–689. https://doi.org/10.1016/j.dci.2008.11.013

Fellowes, M. D. E. and  Godfray, H. C. J. (2000). The evolutionary ecology of resistance to parasitoids by Drosophila. Heredity, 84(1), 1–8. https://doi.org/10.1046/j.1365-2540.2000.00685.x

Gueguen, G., Rajwani, R., Paddibhatla, I., Morales, J. and  Govind, S. (2011). VLPs of Leptopilina boulardi share biogenesis and overall stellate morphology with VLPs of the heterotoma clade. Virus Research, 160(1–2), 159–165. https://doi.org/10.1016/j.virusres.2011.06.005

Lavine, M. D. and  Strand, M. R. (2002). Insect hemocytes and their role in immunity. Insect Biochemistry and Molecular Biology, 32(10), 1295–1309. https://doi.org/10.1016/S0965-1748(02)00092-9

Martinez, J., Duplouy, A., Woolfit, M., Vavre, F., O’Neill, S. L. and  Varaldi, J. (2012). Influence of the virus LbFV and of Wolbachia in a host-parasitoid interaction. PloS One, 7(4), e35081. https://doi.org/10.1371/journal.pone.0035081

Nappi, A. J., Vass, E., Frey, F. and  Carton, Y. (1995). Superoxide anion generation in Drosophila during melanotic encapsulation of parasites. European Journal of Cell Biology, 68(4), 450–456.

Poirié, M., Colinet, D. and  Gatti, J. L. (2014). Insights into function and evolution of parasitoid wasp venoms. Current Opinion in Insect Science, 6, 52–60. https://doi.org/10.1016/j.cois.2014.10.004

Rizki, T. M. and  Rizki, R. M. (1992). Lamellocyte differentiation in Drosophila larvae parasitized by Leptopilina. Developmental and Comparative Immunology, 16(2–3), 103–110. https://doi.org/10.1016/0145-305X(92)90011-Z

Schlenke, T. A., Morales, J., Govind, S. and  Clark, A. G. (2007). Contrasting infection strategies in generalist and specialist wasp parasitoids of Drosophila melanogaster. PLoS Pathogens, 3(10), 1486–1501. https://doi.org/10.1371/journal.ppat.0030158

Varaldi, J., Petit, S., Boulétreau, M. and  Fleury, F. (2006). The virus infecting the parasitoid Leptopilina boulardi exerts a specific action on superparasitism behaviour. Parasitology, 132(Pt 6), 747–756. https://doi.org/10.1017/S0031182006009930

Yang, L., Qiu, L., Fang, Q., Stanley, D. W. and  Gong‐Yin, Y. (2020). Cellular and humoral immune interactions between Drosophila and its parasitoids. Insect Science. https://doi.org/10.1111/1744-7917.12863