Many parasites can manipulate their hosts, thus increasing their transmission to new hosts [1]. This is particularly the case for vector-borne parasites, which can alter the feeding behaviour of their hosts. However, predicting the optimal strategy is not straightforward because three actors are involved and the interests of the parasite may conflict with that of the vector. There are few models that consider the evolution of host manipulation by parasites [but see 2-4], but there are virtually none that investigated how parasites can manipulate the host choice of vectors. Even on the empirical side, many aspects of this choice remain unknown. Gandon [5] develops a simple evolutionary epidemiology model that allows him to formulate clear and testable predictions. These depend on which actor controls the trait (the vector or the parasite) and, when there is manipulation, whether it is realised via infected hosts (to attract vectors) or infected vectors (to change host choice). In addition to clarifying the big picture, Gandon [5] identifies some nice properties of the model, for instance an independence of the density/frequency-dependent transmission assumption or a backward bifurcation at R0=1, which suggests that parasites could persist even if their R0 is driven below unity. Overall, this study calls for further investigation of the different scenarios with more detailed models and experimental validation of general predictions.

References

[1] Hughes D, Brodeur J, Thomas F. 2012. Host manipulation by parasites. Oxford University Press.

[2] Brown SP. 1999. Cooperation and conflict in host-manipulating parasites. Proceedings of the Royal Society of London B: Biological Sciences 266: 1899–1904. doi: 10.1098/rspb.1999.0864

[3] Lion S, van Baalen M, Wilson WG. 2006. The evolution of parasite manipulation of host dispersal. Proceedings of the Royal Society of London B: Biological Sciences. 273: 1063–1071. doi: 10.1098/rspb.2005.3412

[4] Vickery WL, Poulin R. 2010. The evolution of host manipulation by parasites: a game theory analysis. Evolutionary Ecology 24: 773–788. doi: 10.1007/s10682-009-9334-0

[5] Gandon S. 2017. Evolution and manipulation of vector host choice. bioRxiv 110577, ver. 3 of 7th June 2017. doi: 10.1101/110577

Adaptations to a new ecological niche allow species to access new resources and circumvent competitors and are hence obvious pathways of evolutionary success. The evolution of agricultural pest species represents an important case to study how a species adapts, on various timescales, to a novel ecological niche. Among the numerous insects that are agricultural pests, the ability to lay eggs (or oviposit) in ripe fruit appears to be a recurrent scenario. Fruit flies (family Tephritidae) employ this strategy, and include amongst their members some of the most destructive pests (e.g., the olive fruit fly Bactrocera olea or the medfly Ceratitis capitata). In their ms, Karageorgi et al. [1] studied how Drosophila suzukii, a new major agricultural pest species that recently invaded Europe and North America, evolved the novel behavior of laying eggs into undamaged fresh fruit. The close relatives of D. suzukii lay their eggs on decaying plant substrates, and thus this represents a marked change in host use that links to substantial economic losses to the fruit industry. Although a handful of studies have identified genetic changes causing new behaviors in various species, the question of the evolution of behavior remains a largely uncharted territory. The study by Karageorgi et al. [1] represents an original and most welcome contribution in this domain for a non-model species. Using clever behavioral experiments to compare D. suzukii to several related Drosophila species, and complementing those results with neurogenetics and mutant analyses using D. suzukii, the authors nicely dissect the sensory changes at the origin of the new egg-laying behavior. The experiments they describe are easy to follow, richly illustrate through figures and images, and particularly well designed to progressively decipher the sensory bases driving oviposition of D. suzukii on ripe fruit. Altogether, Karageorgi et al.’s [1] results show that the egg-laying substrate preference of D. suzukii has considerably evolved in concert with its morphology (especially its enlarged, serrated ovipositor that enables females to pierce the skin of many ripe fruits). Their observations clearly support the view that the evolution of traits that make D. suzukii an agricultural pest included the modification of several sensory systems (i.e. mechanosensation, gustation and olfaction). These differences between D. suzukii and its close relatives collectively underlie a radical change in oviposition behavior, and were presumably instrumental in the expansion of the ecological niche of the species. The authors tentatively propose a multi-step evolutionary scenario from their results with the emergence of D. suzukii as a pest species as final outcome. Such formalization represents an interesting evolutionary model-framework that obviously would rely upon further data and experiments to confirm and refine some of the evolutionary steps proposed, especially the final and recent transition of D. suzukii from non-invasive to invasive species.

References

[1] Karageorgi M, Bräcker LB, Lebreton S, Minervino C, Cavey M, Siju KP, Grunwald Kadow IC, Gompel N, Prud’homme B. 2017. Evolution of multiple sensory systems drives novel egg-laying behavior in the fruit pest Drosophila suzukii. Current Biology, 27: 1-7. doi: 10.1016/j.cub.2017.01.055

In experimental evolution followed by whole genome resequencing, parallel evolution, defined as the increase in frequency of identical changes in independent populations adapting to the same environment, is often considered as the product of similar selection pressures and the parallel changes are interpreted as adaptive.
However, theory predicts that heterogeneity both in mutation rate and selection intensity across the genome can trigger patterns of parallel evolution. It is thus important to evaluate and quantify the contribution of both mutation and selection in determining parallel evolution to interpret more accurately experimental evolution genomic data and also potentially improve our capacity to predict the genes that will respond to selection.
In their manuscript, Bailey, Guo and Bataillon [1] derive a framework of statistical models to partition the role of mutation and selection in determining patterns of parallel evolution at the gene level. The rationale is to use the synonymous mutations dataset as a baseline to characterize the mutation rate heterogeneity, assuming a negligible impact of selection on synonymous mutations and then analyse the non-synonymous dataset to identify additional source(s) of heterogeneity, by examining the proportion of the variation explained by a number of genomic variables.
This framework is applied to a published data set of resequencing of 40 Saccharomyces cerevisiae populations adapting to a laboratory environment [2]. The model explaining at best the synonymous mutations dataset is one of homogeneous mutation rate along the genome with a significant positive effect of gene length, likely reflecting variation in the size of the mutational target. For the non-synonymous mutations dataset, introducing heterogeneity between sites for the probability of a change to increase in frequency is improving the model fit and this heterogeneity can be partially explained by differences in gene length, recombination rate and number of functional protein domains.
The application of the framework to an experimental data set illustrates its capacity to disentangle the role of mutation and selection and to identify genomic variables explaining heterogeneity in parallel evolution probability but also points to potential limits, cautiously discussed by the authors: first, the number of mutations in the dataset analysed needs to be sufficient, in particular to establish the baseline on the synonymous dataset. Here, despite a high replication (40 populations evolved in the exact same conditions), the total number of synonymous mutations that could be analysed was not very high and there was only one case of a gene with synonymous mutation in two independent populations. Second, although the models are able to identify factors affecting the mutation counts, the proportion of the variation explained is quite low. The consequence is that the models correctly predicts the mutation count distribution but the objective of predicting on which genes the response to selection will occur still seems quite far away.
The framework developed in this manuscript [1] clearly represents a very useful tool for the analysis of large “evolve and resequence” data sets and to gain a better understanding of the determinants of parallel evolution in general. The extension of its application to mutations others than SNPs would provide the possibility to get a more complete picture of the differences in contributions of mutation and selection intensity heterogeneities depending on the mutation types.

References

[1] Bailey SF, Guo Q and Bataillon T (2018) Identifying drivers of parallel evolution: A regression model approach. bioRxiv 118695, ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/118695

[2] Lang GI, Rice DP, Hickman, MJ, Sodergren E, Weinstock GM, Botstein D, and Desai MM (2013) Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500: 571–574. doi: 10.1038/nature12344

Many viruses are transmitted by biological vectors, i.e. organisms that transfer the virus from one host to another. Dengue virus (DENV) is one of them. Dengue is a mosquito-borne viral disease that has rapidly spread around the world since the 1940s. One recent estimate indicates 390 million dengue infections per year [1]. As many arthropod-borne vertebrate viruses, DENV has to cross several anatomical barriers in the vector, to multiply in its body and to invade its salivary glands before getting transmissible. As a consequence, vectors are not passive carriers but genuine hosts of the viruses that potentially have important effects on the composition of virus populations and, ultimately, on virus epidemiology and virulence. Within infected vectors, virus populations are expected to acquire new mutations and to undergo genetic drift and selection effects. However, the intensity of these evolutionary forces and the way they shape virus genetic diversity are poorly known.

In their study, Lequime et al. [2] finely disentangled the effects of genetic drift and selection on DENV populations during their infectious cycle within mosquito (Aedes aegypti) vectors. They evidenced that the genetic diversity of viruses within their vectors is shaped by genetic drift, selection and vector genotype. The experimental design consisted in artificial acquisition of purified virus by mosquitoes during a blood meal. The authors monitored the diversity of DENV populations in Ae. aegypti individuals at different time points by high-throughput sequencing (HTS). They estimated the intensity of genetic drift and selection effects exerted on virus populations by comparing the DENV diversity at these sampling time points with the diversity in the purified virus stock (inoculum).

Disentangling the effects of genetic drift and selection remains a methodological challenge because both evolutionary forces operate concomitantly and both reduce genetic diversity. However, selection reduces diversity in a reproducible manner among experimental replicates (here, mosquito individuals): the fittest variants are favoured at the expense of the weakest ones. In contrast, genetic drift reduces diversity in a stochastic manner among replicates. Genetic drift acts equally on all variants irrespectively of their fitness. The strength of genetic drift is frequently evaluated with the effective population size Ne: the lower Ne, the stronger the genetic drift [3]. The estimation of the effective population size of DENV populations by Lequime et al. [2] was based on single-nucleotide polymorphisms (SNPs) that were (i) present both in the inoculum and in the virus populations sampled at the different time points and (ii) that were neutral (or nearly-neutral) and therefore subjected to genetic drift only and insensitive to selection. As expected for viruses that possess small and constrained genomes, such neutral SNPs are extremely rare. Starting from a set of >1800 SNPs across the DENV genome, only three SNPs complied with the neutrality criteria and were enough represented in the sequence dataset for a precise Ne estimation. Using the method described by Monsion et al. [4], Lequime et al. [2] estimated Ne values ranging from 5 to 42 viral genomes (95% confidence intervals ranged from 2 to 161 founding viral genomes). Consequently, narrow bottlenecks occurred at the virus acquisition step, since the blood meal had allowed the ingestion of ca. 3000 infectious virus particles, on average. Interestingly, bottleneck sizes did not differ between mosquito genotypes. Monsion et al.’s [4] formula provides only an approximation of Ne. A corrected formula has been recently published [5]. We applied this exact Ne formula to the means and variances of the frequencies of the three neutral markers estimated before and after the bottlenecks (Table 1 in [2]), and nearly identical Ne estimates were obtained with both formulas.

Selection intensity was estimated from the dN/dS ratio between the nonsynonymous and synonymous substitution rates using the HTS data on DENV populations. DENV genetic diversity increased following initial infection but was restricted by strong purifying selection during virus expansion in the midgut. Again, no differences were detected between mosquito genotypes. However and importantly, significant differences in DENV genetic diversity were detected among mosquito genotypes. As they could not be related to differences in initial genetic drift or to selection intensity, the authors raise interesting alternative hypotheses, including varying rates of de novo mutations due to differences in replicase fidelity or differences in the balancing selection regime. Interestingly, they also suggest that this observation could simply result from a methodological issue linked to the detection threshold of low-frequency SNPs.
 

References

[1] Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, Drake JM, et al. 2013. The global distribution and burden of dengue. Nature 496: 504–7 doi: 10.1038/nature12060

[2] Lequime S, Fontaine A, Gouilh MA, Moltini-Conclois I and Lambrechts L. 2016. Genetic drift, purifying selection and vector genotype shape dengue virus intra-host genetic diversity in mosquitoes. PloS Genetics 12: e1006111 doi: 10.1371/journal.pgen.1006111

[3] Charlesworth B. 2009. Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics 10: 195-205 doi: 10.1038/nrg2526

[4] Monsion B, Froissart R, Michalakis Y and Blanc S. 2008. Large bottleneck size in cauliflower mosaic virus populations during host plant colonization. PloS Pathogens 4: e1000174 doi: 10.1371/journal.ppat.1000174

[5] Thébaud G and Michalakis Y. 2016. Comment on ‘Large bottleneck size in cauliflower mosaic virus populations during host plant colonization’ by Monsion et al. (2008). PloS Pathogens 12: e1005512 doi: 10.1371/journal.ppat.1005512

Recent methodological developments and increased genome sequencing efforts have introduced the tantalizing possibility of inferring ancestral phenotypes using DNA from contemporary species. One intriguing application of this idea is to exploit the apparent correlation between substitution rates and body size to infer ancestral species' body sizes using the inferred patterns of substitution rate variation among species lineages based on genomes of extant species [1].
The recommended paper by Figuet et al. [2] examines the utility of such approaches by analyzing the Cetartiodactyla, a clade of large mammals that have mostly well resolved phylogenetic relationships and a reasonably good fossil record. This combination of genomic data and fossils allows a direct comparison between body size predictions obtained from the genomic data and empirical evidence from the fossil record. If predictions seem good in groups such as the Cetartiodactyla, where there is independent evidence from the fossil record, this would increase the credibility of predictions made for species with less abundant fossils.
Figuet et al. [2] analyze transcriptome data for 41 species and report a significant effect of body mass on overall substitution rate, synonymous vs. non-synonymous rates, and the dynamics of GC-content, thus allowing a prediction of small ancestral body size in this group despite the fact that the extant species that were analyzed are nearly all large.
A comparative method based solely on morphology and phylogenetic relationships would be very unlikely to make such a prediction. There are many sources of uncertainty in the variables and parameters associated with these types of approaches: phylogenetic uncertainty (topology and branch lengths), uncertainty about inferred substitution rates, and so on. Although the authors do not account for all these sources of uncertainty the fact that their predicted body sizes appear sensible is encouraging and undoubtedly the methods will become more statistically sophisticated over time.

References

[1] Romiguier J, Ranwez V, Douzery EJP and Galtier N. 2013. Genomic evidence for large, long-lived ancestors to placental mammals. Molecular Biology and Evolution 30: 5–13. doi: 10.1093/molbev/mss211

[2] Figuet E, Ballenghien M, Lartillot N and Galtier N. 2017. Reconstruction of body mass evolution in the Cetartiodactyla and mammals using phylogenomic data. bioRxiv, ver. 3 of 4th December 2017. 139147. doi: 10.1101/139147