Human economic activity is responsible for the vast majority of ongoing extinction, but that does not mean lineages are being affected willy-nilly. For amphibians [1] and South African flowering plants [2], young species have a somewhat higher than expected chance of being threatened with extinction. In contrast, older Australian marsupial lineages seem to be more at risk [3]. Both of the former studies suggested that situations leading to peripheral isolation might simultaneously increase ongoing speciation and current threat via small geographic range, while the authors of the latter study suggested that older species might have evolved increasingly narrow niches. Here, Andrew Tanentzap and colleagues [4] dig deeper into the putative links between species age, niche breadth and threat status. Across 500-some plant genera worldwide, they find that, indeed, ""younger"" species (i.e. from younger and faster-diversifying genera) were more likely to be listed as imperiled by the IUCN, consistent with patterns for amphibians and African plants. Given this, results from their finer-level analyses of conifers are initially bemusing: here, ""older"" (i.e., on longer terminal branches) species were at higher risk. This would make conifers more like Australian marsupials, with the rest of the plants being more like amphibians. However, here where the data were more finely grained, the authors detected a second interesting pattern: using an intriguing matched-pair design, they detect a signal of conifer species niches seemingly shrinking as a function of age. The authors interpret this as consistent with increasing specialization, or loss of ancestral warm wet habitat, over paleontological time. It is true that conifers in general are older than plants more generally, with some species on branches that extend back many 10s of millions of years, and so a general loss of suitable habitat makes some sense. If so, both the pattern for all plants (small initial ranges heightening extinction) and the pattern for conifers (eventual increasing specialization or habitat contraction heightening extinction) could occur, each on a different time scale. As a coda, the authors detected no effect of age on threat status in palms; however, this may be both because palms have already lost species to climate-change induced extinction, and because they are thought to speciate more via long-distance dispersal and adaptive divergence then via peripheral isolation.
Given how quickly ranges can change, how hard it is to measure niche breadth, and the qualitatively different time scales governing past diversification and present-day extinction drivers, this is surely not the last word on the subject, even for plants. However, even the hint of a link between drivers of extinction in the Anthropocene and drivers of diversification through the ages is intellectually exciting and, perhaps, even, somehow, of practical importance.

References

[1] Greenberg, D. A., & Mooers, A. Ø. (2017). Linking speciation to extinction: Diversification raises contemporary extinction risk in amphibians. Evolution Letters, 1, 40–48. doi: 10.1002/evl3.4
[2] Davies, T. J., Smith, G. F., Bellstedt, D. U., Boatwright, J. S., Bytebier, B., Cowling, R. M., Forest, F., et al. (2011). Extinction risk and diversification are linked in a plant biodiversity hotspot. PLoS Biology, 9:e1000620. doi: 10.1371/journal.pbio.1000620
[3] Johnson, C. N., Delean S., & Balmford, A. (2002). Phylogeny and the selectivity of extinction in Australian marsupials. Animal Conservation, 5, 135–142. doi: 10.1017/S1367943002002196
[4] Tanentzap, A. J., Igea, J., Johnston, M. G., & Larcombe, M. G. (2018). Range size dynamics can explain why evolutionarily age and diversification rate correlate with contemporary extinction risk in plants. bioRxiv, 152215, ver. 5 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/152215

The increase in the prevalence of (antibiotic) resistance has become a major global health concern and is an excellent example of the impact of real-time evolution on human society. This has led to a boom of studies that investigate the mechanisms and factors involved in the evolution of resistance, and to the spread of the concept of "costs of resistance". This concept refers to the relative fitness disadvantage of a drug-resistant genotype compared to a non-resistant reference genotype in the ancestral (untreated) environment.

In their paper, Lenormand et al. [1] discuss the history of this concept and highlight its caveats and limitations. The authors address both practical and theoretical problems that arise from the simplistic view of "costly resistance" and argue that they can be prejudicial for antibiotic resistance studies. For a better understanding, they visualize their points of critique by means of Fisher's Geometric model.

The authors give an interesting historical overview of how the concept arose and speculate that it emerged (during the 1980s) in an attempt by ecologists to spread awareness that fitness can be environment-dependent, and because of the concept's parallels to trade-offs in life-history evolution. They then identify several problems that arise from the concept, which, besides the conceptual misunderstandings that they can cause, are important to keep in mind when designing experimental studies.

The authors highlight and explain the following points:
1. Costs of resistance do not necessarily imply pleiotropic effects of a resistance mutation, and pleiotropy is not necessarily the cause of fitness trade-offs.
2. Any non-treated environment and any treatment dose can result in a different cost.
3. Different reference genotypes may result in different costs. Specifically, the reference genotype has to be "optimally" adapted to the reference environment to provide an accurate measurement of costs.

Lenormand et al.'s paper [1] is a timely perspective piece in light of the ever-increasing efforts to understand and tackle resistance evolution [2]. Although some readers may shy away from the rather theoretical presentation of the different points of concern, it will be useful for both theoretical and empirical readers by illustrating the misconceptions that can arise from the concept of the cost of resistance. Ultimately, the main lesson to be learned from this paper may not be to ban the term "cost of resistance" from one's vocabulary, but rather to realize that the successful fight against drug resistance requires more differential information than the measurement of fitness effects in a drug-treated vs. non-treated environment in the lab [3-4]. Specifically, a better integration of the ecological aspects of drug resistance evolution and maintenance is needed [5], and we are far from a general understanding of how environmental factors interact and influence an organism's (absolute and relative) fitness and the effect of resistance mutations.

References

[1] Lenormand T, Harmand N, Gallet R. 2018. Cost of resistance: an unreasonably expensive concept. bioRxiv 276675, ver. 3 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/276675
[2] Andersson DI and Hughes D. Persistence of antibiotic resistance in bacterial populations. 2011. FEMS Microbiology Reviews, 35: 901-911. doi: 10.1111/j.1574-6976.2011.00289.x
[3] Chevereau G, Dravecká M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach T. 2015. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS biology 13, e1002299. doi: 10.1371/journal.pbio.1002299
[4] Bengtsson-Palme J, Kristiansson E, Larsson DGJ. 2018. Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews 42: 68–80. doi: 10.1093/femsre/fux053
[5] Hiltunen T, Virta M, Laine AL. 2017. Antibiotic resistance in the wild: an eco-evolutionary perspective. Philosophical Transactions of the Royal Society B: Biological Sciences 372: 20160039. doi: 10.1098/rstb.2016.0039

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

References

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

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

[1] Dumas, P. et al. (2015). Spodoptera frugiperda (Lepidoptera: Noctuidae) host-plant variants: two host strains or two distinct species?. Genetica, 143(3), 305-316. doi: 10.1007/s10709-015-9829-2
[2] Funk, D. J., Filchak, K. E. and Feder J. L. (2002) Herbivorous insects: model systems for the comparative study of speciation ecology. In: Etges W.J., Noor M.A.F. (eds) Genetics of Mate Choice: From Sexual Selection to Sexual Isolation. Contemporary Issues in Genetics and Evolution, vol 9. Springer, Dordrecht. doi: 10.1007/978-94-010-0265-3_10
[3] Groot, A. T., Unbehend, M., Hänniger, S., Juárez, M. L., Kost, S. and Heckel D. G.(2016) Evolution of reproductive isolation of Spodoptera frugiperda. In Allison, J. and Cardé, R. (eds) Sexual communication in moths. Chapter 20: 291-300.
[4] Hänniger, S. et al. (2017). Genetic basis of allochronic differentiation in the fall armyworm. BMC evolutionary biology, 17(1), 68. doi: 10.1186/s12862-017-0911-5
[5] Kost, S., Heckel, D. G., Yoshido, A., Marec, F., and Groot, A. T. (2016). AZ‐linked sterility locus causes sexual abstinence in hybrid females and facilitates speciation in Spodoptera frugiperda. Evolution, 70(6), 1418-1427. doi: 10.1111/evo.12940
[6] Mayr, E. (1942) Systematics and the origin of species. Columbia University Press, New York.
[7] Nagoshi, R. N. (2019). Evidence that a major subpopulation of fall armyworm found in the Western Hemisphere is rare or absent in Africa, which may limit the range of crops at risk of infestation. PloS one, 14(4). doi: 10.1371/journal.pone.0208966
[8] Orsucci, M., Moné, Y., Audiot, P., Gimenez, S., Nhim, S., Naït-Saïdi, R., Frayssinet, M., Dumont, G., Boudon, J.-P., Vabre, M., Rialle, S., Koual, R., Kergoat, G. J., Nagoshi, R. N., Meagher, R. L., d’Alençon, E. and Nègre N. (2020) Transcriptional differences between the two host strains of Spodoptera frugiperda (Lepidoptera: Noctuidae). bioRxiv, 263186, ver. 2 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/263186
[9] Pashley, D. P. (1988) Current Status of Fall Armyworm Host Strains. Florida Entomologist 71 (3): 227–34. doi: 10.2307/3495425
[10] Pogue, M. (2002). A World Revision of the Genus Spodoptera Guenée (Lepidoptera: Noctuidae). American Entomological Society.
[11] Schöfl, G., Heckel, D. G., and Groot, A. T. (2009). Time‐shifted reproductive behaviours among fall armyworm (Noctuidae: Spodoptera frugiperda) host strains: evidence for differing modes of inheritance. Journal of Evolutionary Biology, 22(7), 1447-1459. doi: 10.1111/j.1420-9101.2009.01759.x
[12] Schöfl, G., Dill, A., Heckel, D. G., and Groot, A. T. (2011). Allochronic separation versus mate choice: nonrandom patterns of mating between fall armyworm host strains. The American Naturalist, 177(4), 470-485. doi: 10.1086/658904

Papillomaviruses (PVs) infect almost all mammals and possibly amniotes and bony fishes. While most of them have no significant effects on the hosts, some induce physical lesions. Phylogeny of PVs consists of a few crown groups [1], among which AlphaPVs that infect primates including human have been well studied. They are associated to largely different clinical manifestations: non-oncogenic PVs causing anogenital warts, oncogenic and non-oncogenic PVs causing mucosal lesions, and non-oncogenic PVs causing cutaneous warts.
The PV genome consists of a double stranded circular DNA genome, roughly organized into three parts: an early region coding for six open reading frames (ORFs: E1, E2, E4, E5, E6 and E7) involved in multiple functions including viral replication and cell transformation; a late region coding for structural proteins (L1 and L2); and a non-coding regulatory region (URR) that contains the cis-elements necessary for replication and transcription of the viral genome.
The E5, E6, and E7 are known to act as oncogenes. The E6 protein binds to the cellular p53 protein [2]. The E7 protein binds to the retinoblastoma tumor suppressor gene product, pRB [3]. However, the E5 has been poorly studied, even though a high correlation between the type of E5 protein and the infection phenotype is observed. E5s, being present on the E2/L2 intergenic region in the genomes of a few polyphyletic PV lineages, are so diverged and can only be characterized by high hydrophobicity. No similar sequences have been found in the sequence database.
Willemsen et al. [4] provide valuable evidence on the origin and evolutionary history of E5 genes and their genomic environments. First, they tested common ancestry vs independent origins [5]. Because alignment can lead to biased testing toward the hypothesis of common ancestry [6], they took full account of alignment uncertainty [7] and conducted random permutation test [8]. Although the strong chemical similarity hampered decisive conclusion on the test, they could confirm that E5 may do code proteins, and have unique evolutionary history with far different topology from the neighboring genes.
Still, there is mysteries with the origin and evolution of E5 genes. One of the largest interest may be the evolution of hydrophobicity, because it may be the main cause of variable infection phenotype. The inference has some similarity in nature with the inference of evolutionary history of G+C contents in bacterial genomes [9]. The inference may take account of possible opportunity of convergent or parallel evolution by setting an anchor to the topologies of neighboring genes.

References

[1] Bravo, I. G., & Alonso, Á. (2004). Mucosal human papillomaviruses encode four different E5 proteins whose chemistry and phylogeny correlate with malignant or benign growth. Journal of virology, 78, 13613-13626. doi: 10.1128/JVI.78.24.13613-13626.2004
[2] Werness, B. A., Levine, A. J., & Howley, P. M. (1990). Association of human papillomavirus types 16 and 18 E6 proteins with p53. Science, 248, 76-79. doi: 10.1126/science.2157286
[3] Dyson, N., Howley, P. M., Munger, K., & Harlow, E. D. (1989). The human papilloma virus-16 E7 oncoprotein is able to bind to the retinoblastoma gene product. Science, 243, 934-937. doi: 10.1126/science.2537532
[4] Willemsen, A., Félez-Sánchez, M., & Bravo, I. G. (2019). Genome plasticity in Papillomaviruses and de novo emergence of E5 oncogenes. bioRxiv, 337477, ver. 3 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/337477
[5] Theobald, D. L. (2010). A formal test of the theory of universal common ancestry. Nature, 465, 219–222. doi: 10.1038/nature09014
[6] Yonezawa, T., & Hasegawa, M. (2010). Was the universal common ancestry proved?. Nature, 468, E9. doi: 10.1038/nature09482
[7] Redelings, B. D., & Suchard, M. A. (2005). Joint Bayesian estimation of alignment and phylogeny. Systematic biology, 54(3), 401-418. doi: 10.1080/10635150590947041
[8] de Oliveira Martins, L., & Posada, D. (2014). Testing for universal common ancestry. Systematic biology, 63(5), 838-842. doi: 10.1093/sysbio/syu041
[9] Galtier, N., & Gouy, M. (1998). Inferring pattern and process: maximum-likelihood implementation of a nonhomogeneous model of DNA sequence evolution for phylogenetic analysis. Molecular biology and evolution, 15(7), 871-879. doi: 10.1093/oxfordjournals.molbev.a025991