Assessing population connectivity is central to understanding population dynamics, and is therefore of great importance in evolutionary biology and conservation biology. In the marine realm, the apparent absence of physical barriers, large population sizes and high dispersal capacities of most organisms often result in no detectable structure, thereby hindering inferences of population connectivity. In a review paper, Gagnaire et al. [1] propose several ideas to improve detection of population connectivity. Notably, using simulations they show that under certain circumstances introgression from one species into another may reveal cryptic population structure within that second species.
The isolated Kerguelen archipelago in the south of Indian Ocean represents a typical situation where the structure of coastal marine organisms is expected to be difficult to detect. In an elegant genomic study, Fraïsse et al. [2] take advantage of introgression from foreign lineages to infer fine-grained population structure in a population of mussels around the Kerguelen archipelago, and investigate its association with environmental variables. Using a large panel of genome-wide markers (GBS) and applying a range of methods that unravel patterns of divergence and gene flow among lineages, they first find that the Kerguelen population is highly admixed, with a major genetic background corresponding to the southern mussel lineage Mytilus platensis introgressed by three northern lineages. By selecting a panel of loci enriched in ancestry-informative SNPs (ie, SNPs highly differentiated among northern lineages) they then detect a fine-scale genetic structure around the Kerguelen archipelago, and identify a major connectivity break. They further show an associating between the genetic structure and environmental variables, particularly the presence of Macrocystis kelp, a marker of habitat exposure to waves (a feature repeatedly evidenced to be important for mussels). While such association pattern could lead to the interpretation that differentiated SNPs correspond to loci directly under selection or linked with such loci, and even be considered as support for adaptive introgression, Fraïsse et al. [2] convincingly show by performing simulations that the genetic-environment association detected can be entirely explained by dispersal barriers associated with environmental variables (habitat-associated connectivity). They also explain why the association is better detected by ancestry-informative SNPs as predicted by Gagnaire et al. [1]. In addition, intrinsic genetic incompatibilities, which reduce gene flow, tend to become trapped at ecotones due to ecological selection, even when loci causing genetic incompatibilities are unlinked with loci involved in adaption to local ecological conditions (Bierne et al. [3]’s coupling hypothesis), leading to correlations between environmental variables and loci not involved in local adaptation. Notably, in Fraïsse et al. [2]’s study, the association between the kelp and ancestry-informative alleles is not consistent throughout the archipelago, casting further doubt on the implication of these alleles in local adaptation.
The study of Fraïsse et al. [2] is therefore an important contribution to evolutionary biology because 1) it provides an empirical demonstration that alleles of foreign origin can be pivotal to detect fine-scale connectivity patterns and 2) it represents a test case of Bierne et al. [3]’s coupling hypothesis, whereby introgressed alleles also enhance patterns of genetic-environment associations. Since genomic scan or GWAS approaches fail to clearly reveal loci involved in local adaptation, how can we disentangle environment-driven selection from intrinsic reproductive barriers and habitat-associated connectivity? A related question is whether we can reliably identify cases of adaptive introgression, which have increasingly been put forward as a mechanism involved in adaptation [4]. Unfortunately, there is no easy answer, and the safest way to go is to rely – where possible – on independent information [5], in particular functional studies of the detected loci, as is for example the case in the mimetic butterfly Heliconius literature (e. g., [6]) where several loci controlling colour pattern variation are well characterized.

References

[1] Gagnaire, P.-A., Broquet, T., Aurelle, D., Viard, F., Souissi, A., Bonhomme, F., Arnaud-Haond, S., & Bierne, N. (2015). Using neutral, selected, and hitchhiker loci to assess connectivity of marine populations in the genomic era. Evolutionary Applications, 8, 769–786. doi: 10.1111/eva.12288
[2] Fraïsse, C., Haguenauer, A., Gerard, K., Weber, A. A.-T., Bierne, N., & Chenuil, A. (2018). Fine-grained habitat-associated genetic connectivity in an admixed population of mussels in the small isolated Kerguelen Islands. bioRxiv, 239244, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/239244
[3] Bierne, N., Welch, J., Loire, E., Bonhomme, F., & David, P. (2011). The coupling hypothesis: why genome scans may fail to map local adaptation genes. Molecular Ecology, 20, 2044–2072. doi: 10.1111/j.1365-294X.2011.05080.x
[4] Hedrick, P. W. (2013). Adaptive introgression in animals: examples and comparison to new mutation and standing variation as sources of adaptive variation. Molecular Ecology, 22, 4606–4618. doi: 10.1111/mec.12415
[5] Ravinet, M., Faria, R., Butlin, R. K., Galindo, J., Bierne, N., Rafajlović, M., Noor, M. A. F., Mehlig, B., & Westram, A. M. (2017). Interpreting the genomic landscape of speciation: a road map for finding barriers to gene flow. Journal of Evolutionary Biology, 30, 1450–1477. doi: 10.1111/jeb.13047.
[6] Jay, P., Whibley, A., Frézal, L., Rodríguez de Cara, M. A., Nowell, R. W., Mallet, J., Dasmahapatra, K. K., & Joron, M. (2018). Supergene evolution triggered by the introgression of a chromosomal inversion. Current Biology, 28, 1839–1845.e3. doi: 10.1016/j.cub.2018.04.072

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

References

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

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

Organisms often show robustness to genetic or environmental perturbations. Whether these two components of robustness can evolve separately is the focus of the paper by Le Rouzic [1]. Using theoretical analysis and individual-based computer simulations of a gene regulatory network model, he shows that multiple aspects of robustness can be investigated as a set of pleiotropically linked quantitative traits. While genetically correlated, various robustness components (e.g., mutational, developmental, homeostasis) of gene expression in the regulatory network evolved more or less independently from each other under directional selection. The quantitative approach of Le Rouzic could explain both how unselected robustness components can respond to selection on other components and why various robustness-related features seem to have their own evolutionary history. Moreover, he shows that all components were evolvable, but not all to the same extent. Robustness to environmental disturbances and gene expression stability showed the largest responses while increased robustness to genetic disturbances was slower. Interestingly, all components were positively correlated and remained so after selection for increased or decreased robustness.

This study is an important contribution to the discussion of the evolution of robustness in biological systems. While it has long been recognized that organisms possess the ability to buffer genetic and environmental perturbations to maintain homeostasis (e.g., canalization [2]), the genetic basis and evolutionary routes to robustness and canalization are still not well understood. Models of regulatory gene networks have often been used to address aspects of robustness evolution (e.g., [3]). Le Rouzic [1] used a gene regulatory network model derived from Wagner’s model [4]. The model has as end product the expression level of a set of genes influenced by a set of regulatory elements (e.g., transcription factors). The level and stability of expression are a property of the regulatory interactions in the network.

Le Rouzic made an important contribution to the study of such gene regulation models by using a quantitative genetics approach to the evolution of robustness. He crafted a way to assess the mutational variability and selection response of the components of robustness he was interested in. Le Rouzic’s approach opens avenues to investigate further aspects of gene network evolutionary properties, for instance to understand the evolution of phenotypic plasticity.

Le Rouzic also discusses ways to measure his different robustness components in empirical studies. As the model is about gene expression levels at a set of protein-coding genes influenced by a set of regulatory elements, it naturally points to the possibility of using RNA sequencing to measure the variation of gene expression in know gene networks and assess their robustness. Robustness could then be studied as a multidimensional quantitative trait in an experimental setting.

References

[1] Le Rouzic, A (2022) Gene network robustness as a multivariate character. arXiv: 2101.01564, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://arxiv.org/abs/2101.01564

[2] Waddington CH (1942) Canalization of Development and the Inheritance of Acquired Characters. Nature, 150, 563–565. https://doi.org/10.1038/150563a0

[3] Draghi J, Whitlock M (2015) Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks. Evolution, 69, 2345–2358. https://doi.org/10.1111/evo.12732

[4] Wagner A (1994) Evolution of gene networks by gene duplications: a mathematical model and its implications on genome organization. Proceedings of the National Academy of Sciences, 91, 4387–4391. https://doi.org/10.1073/pnas.91.10.4387

Many species with separate sexes have evolved sex chromosomes, with the sex-limited chromosomes (i.e. the Y or W chromosomes) exhibiting a wide range of genetic divergences from their homologous X or Z chromosomes (Bachtrog et al., 2014). Variable divergences can result from the cessation of recombination between sex chromosomes that occurred at different time points, with the mechanisms of initiation and expansion of recombination suppression along sex chromosomes remaining poorly understood (Charlesworth, 2017). 

The study by Castel et al (2023) describes the serendipitous discovery of a shared XY sex chromosome system in three closely related hydrothermal vent gastropods. The X and Y chromosomes appear to still recombine but at variable rates across the three species. This variation makes the gastropod system a very promising focus for future research on sex chromosome evolution. 

An additional intriguing finding is that some females in one of three gastropod species contain male reproductive tissue in their gonads, providing a fascinating case of a mixed or transitory sexual system. Overall, the study by Castel et al (2023) offers the first insights into the reproduction and sex chromosome system of animals living in deep marine vents, which have remained poorly studied and open outstanding research perspectives on these creatures.

References

Bachtrog, D., J.E.Mank, C.L.Peichel, M.Kirkpatrick, S.P.Otto, T.L. Ashman, M.W.Hahn, J.Kitano, I.Mayrose, R.Ming, et al. 2014.Sex determination: why so many ways of doing it? PLoSBiol. 12:e1001899. https://doi.org/10.1371/journal.pbio.1001899

Charlesworth, D. Young sex chromosomes in plants and animals. 2019. New Phytologist 224: 1095–1107. https://doi.org/10.1111/nph.16002

Castel J, Pradillon F, Cueff V, Leger G, Daguin-Thiébaut C, Ruault S, Mary J, Hourdez S, Jollivet D, and Broquet T 2023. Genetic sex determination in three closely related hydrothermal vent gastropods, including one species with intersex individuals. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2023.04.11.536409

Genetic diversity in temperate and boreal forests tree species has been strongly affected by late Pleistocene climate oscillations [2,3,5], but also by anthropogenic forces. Particularly in Europe, where a long history of human intervention has re-distributed species and populations, it can be difficult to know if a given forest arose through natural regeneration and gene flow or through some combination of natural and human-mediated processes. This uncertainty can confound inferences of the causes and consequences of standing genetic variation, which may impact our interpretation of demographic events that shaped species before humans became dominant on the landscape. In their paper entitled "Genomic data provides new insights on the demographic history and the extent of recent material transfers in Norway spruce", Chen et al. [1] used a genome-wide dataset of 400k SNPs to infer the demographic history of Picea abies (Norway spruce), the most widespread and abundant spruce species in Europe, and to understand its evolutionary relationship with two other spruces (Picea obovata [Siberian spruce] and P. omorika [Serbian spruce]). Three major Norway spruce clusters were identified, corresponding to central Europe, Russia and the Baltics, and Scandinavia, which agrees with previous studies. The density of the SNP data in the present paper enabled inference of previously uncharacterized admixture between these groups, which corresponds to the timing of postglacial recolonization following the last glacial maximum (LGM). This suggests that multiple migration routes gave rise to the extant distribution of the species, and may explain why Chen et al.'s estimates of divergence times among these major Norway spruce groups were older (15mya) than those of previous studies (5-6mya) – those previous studies may have unknowingly included admixed material [4]. Treemix analysis also revealed extensive admixture between Norway and Siberian spruce over the last ~100k years, while the geographically-restricted Serbian spruce was both isolated from introgression and had a dramatically smaller effective population size (Ne) than either of the other two species. This small Ne resulted from a bottleneck associated with the onset of the iron age ~3000 years ago, which suggests that anthropogenic depletion of forest resources has severely impacted this species. Finally, ancestry of Norway spruce samples collected in Sweden and Denmark suggest their recent transfer from more southern areas of the species range. This northward movement of genotypes likely occurred because the trees performed well relative to local provenances, which is a common observation when trees from the south are planted in more northern locations (although at the potential cost of frost damage due to inappropriate phenology). While not the reason for the transfer, the incorporation of southern seed sources into the Swedish breeding and reforestation program may lead to more resilient forests under climate change. Taken together, the data and analysis presented in this paper allowed inference of the intra- and interspecific demographic histories of a tree species group at a very high resolution, and suggest caveats regarding sampling and interpretation of data from areas with a long history of occupancy by humans.

References

[1] Chen, J., Milesi, P., Jansson, G., Berlin, M., Karlsson, B., Aleksić, J. M., Vendramin, G. G., Lascoux, M. (2018). Genomic data provides new insights on the demographic history and the extent of recent material transfers in Norway spruce. BioRxiv, 402016. ver. 3 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/402016
[2] Holliday, J. A., Yuen, M., Ritland, K., & Aitken, S. N. (2010). Postglacial history of a widespread conifer produces inverse clines in selective neutrality tests. Molecular Ecology, 19(18), 3857–3864. doi: 10.1111/j.1365-294X.2010.04767.x
[3] Ingvarsson, P. K. (2008). Multilocus patterns of nucleotide polymorphism and the demographic history of Populus tremula. Genetics, 180, 329-340. doi: 10.1534/genetics.108.090431
[4] Lockwood, J. D., Aleksić, J. M., Zou, J., Wang, J., Liu, J., & Renner, S. S. (2013). A new phylogeny for the genus Picea from plastid, mitochondrial, and nuclear sequences. Molecular Phylogenetics and Evolution, 69(3), 717–727. doi: 10.1016/j.ympev.2013.07.004
[5] Pyhäjärvi, T., Garcia-Gil, M. R., Knürr, T., Mikkonen, M., Wachowiak, W., & Savolainen, O. (2007). Demographic history has influenced nucleotide diversity in European Pinus sylvestris populations. Genetics, 177(3), 1713–1724. doi: 10.1534/genetics.107.077099 "