How parasite virulence evolves is arguably the most important question in both the applied and fundamental study of host-parasite interactions. Typically, this research area has been progressing through the formalization of the problem via mathematical modelling. This is because the question is a complex one, as virulence is both affected and affects several aspects of the host-parasite interaction. Moreover, the evolution of virulence is a problem in which ecology (epidemiology) and evolution (changes in trait values through time) are tightly intertwined, generating what is now known as eco-evolutionary dynamics. Therefore, intuition is not sufficient to address how virulence may evolve.
In their classical model, Anderson and May [1] predict that the optimal virulence level results from a trade-off between increasing parasite load within hosts and promoting transmission between hosts. Although very useful and foundational, this model incurs into several simplifying assumptions. One of the most obvious is that it considers that hosts are infected by a single parasite strain/species. Some subsequent models have thus accounted for multiple infections, generally predicting that this will select for higher virulence, because it increases the strength of selection in the within-host compartment.
Usually, when attacked, hosts deploy defences to combat their parasites. In many systems, however, parasites can suppress the immune response of their hosts. This leads to prolonged infection, which is beneficial for the parasite. However, immunosuppressed hosts are also more prone to infection. Thus, multiple infections are more likely in a population of immunosuppressed hosts, leading to higher virulence, hence a shorter infection period. Thus, the consequences of immunosuppression for the evolution of virulence in a system allowing for multiple infections are not straightforward.
Kamiya et al.[2] embrace this challenge. They create an epidemiological model in which the probability of co-infection trades off with the rate of recovery from infection, via immunosuppression. They then use adaptive dynamics to study how either immunosuppression or virulence evolve in response to one another, to then establish what happens when they both coevolve. They find that when virulence only evolves, its evolutionary equilibrium increases as immunosuppression levels increase. In the reverse case, that is, when virulence is set to a fixed value, the evolutionarily stable immunosuppression varies non-linearly with virulence, with first a decrease, but then an increase at high levels of virulence. The initial decrease of immunosuppression may be due to (a) a decrease in infection duration and/or (b) a decrease in the proportion of double infections, caused by increased levels of virulence. However, as virulence increases, the probability of double infections decreases even in non-immunosuppressed hosts, hence increased immunosuppression is selected for.
The combination of both Evolutionary Stable Strategies (ESSs) yields intermediate levels of virulence and immunosuppression. The authors then address how this co-ESS varies with host mortality and with the shape of the trade-off between the probability of co-infection and the rate of recovery. They find that immunosuppression always decreases with increased host mortality, as it becomes not profitable to invest on this trait. In contrast, virulence peaks at intermediate values of host mortality, unlike the monotonical decrease that is found in absence of immunosuppression. Also, this relationship is predicted to vary with the shape of the trade-off underlying the costs and benefits of immunosuppression.
In sum, Kamiya et al. [2] provide a comprehensive analysis of an important problem in the evolution of host-parasite interactions. The model provides clear predictions, and thus can now be tested using the many systems in which immunosuppression has been detected, provided that the traits that compose the model can be measured.

References

[1] Anderson RM and May RM. 1982. Coevolution of hosts and parasites. Parasitology, 1982. 85: 411–426. doi: 10.1017/S0031182000055360

[2] Kamiya T, Mideo N and Alizon S. 2017. Coevolution of virulence and immunosuppression in multiple infections. bioRxiv, ver. 7 peer-reviewed by PCI Evol Biol, 149211. doi: 10.1101/139147

A classic problem in evolutionary biology is to understand the genetic variance in fitness. The simplest hypothesis is that variation exists, even in well-adapted populations, as a result of the balance between mutational input and selective elimination. This variation causes a reduction in mean fitness, known as the mutation load. Though mutation load is difficult to quantify empirically, indirect evidence of segregating genetic variation in fitness is often readily obtained by comparing the fitness of inbred and outbred offspring, i.e., by measuring inbreeding depression. Mutation-selection balance models have been studied as a means of understanding the genetic variance in fitness, mutation load, and inbreeding depression. Since their inception, such models have increased in sophistication, allowing us to ask these questions under more realistic and varied scenarios. The new theoretical work by Abu Awad and Roze [1] is a substantial step forward in understanding how arbitrary levels of self-fertilization affect variation, load and inbreeding depression under mutation-selection balance.
It has never been entirely clear how selfing should affect these population genetic properties in a multi-locus model. From the single-locus perspective, selfing increases homozygosity, which allows for more efficient purging leading to a prediction of less variance and lower load. On the other hand, selfing directly and indirectly affects several types of multilocus associations, which tend to make selection less efficient. Though this is certainly not the first study to consider mutation-selection balance in species with selfing (e.g., [2-5]), it is perhaps the most biologically realistic. The authors consider a model where n traits are under stabilizing selection and where each locus affects an arbitrary subset of these traits. As others have argued [6-7], this type of fitness landscape model “naturally” gives rise to dominance and epistatic effects. Abu Awad and Roze [1] thoroughly investigate this model both with analytical approximations and stochastic simulations (incorporating the effects of drift).
Their analysis reveals three major parameter regimes. The first regime occurs under low mutation rates, when segregating deleterious alleles are sufficiently rare across the genome that multi-locus genetic associations (disequilibria) can be ignored. As expected, in this regime, increased selfing facilitates purging, thereby leading to less standing genetic variation, lower load and less inbreeding depression.
In the second regime, mutation rates are higher and segregating deleterious alleles are more common. Though the effects of multilocus genetic associations cannot be ignored, Abu Awad and Roze [1] show that a good approximation can be obtained by considering only two-locus associations (ignoring the multitude of higher order associations). This is where the sophistication of their analysis yields the greatest insights. Their analysis shows that two different types of interlocus associations are important. First, selfing directly generates identity disequilibrium (correlation in homozygosity between two loci) that occurs because individuals produced through outbreeding tend to be heterozygous across multiple loci whereas individuals produced by selfing tend to be homozygous across multiple loci. These correlations reduce the efficiency of selection when deleterious effects are partially recessive [5]. Second, selfing indirectly affects traditional linkage disequilibrium. Epistatic selection resulting from the fitness landscape generates negative linkage disequilibrium between alleles at different loci that cause the same direction of deviation in a trait from its optimum. Because selfing reduces the effective rate of recombination, linkage disequilibrium reaches higher levels. Because selection tends to generate compensatory combinations of alleles, partially masking their deleterious effects, these associations also make purging less efficient. Their analysis shows the strength of the effect from identity disequilibrium scales with U, the genome-wide rate of deleterious mutations, but the effect of linkage disequilibrium scales with U/n because with more traits (higher n) two randomly chosen alleles are less likely to affect the same trait and so be subject to epistatic selection. Together, the effects of multilocus associations increase the load and can, in some cases, cause the load to increase as selfing increase from moderate to high levels.
However, their analytical approximations become inaccurate under conditions when the number of epistatically interacting segregating mutations (proportional to U/n) becomes large relative to the effective recombination rate (dependent on outcrossing and recombination rates). In this third regime, higher order genetic associations become important. In the limit of no recombination, model behaves as if the whole genome is a single locus with a very large number of alleles, becoming equivalent to previous studies [2-3].
The study by Abu Awad and Roze [1] helps us better understand the “simplest” explanation for genetic variance in fitness—mutation-selection balance—in a model of considerable complexity involving multiple traits under stabilizing selection, which ‘naturally’ allows for pleiotropy and epistasis. Their model tends to confirm the classic prediction of lower variation in fitness, less load, and inbreeding depression in species with higher levels of selfing. However, their careful analysis provides a clearer picture of how (and by how much) epistasis and selfing affect key population genetic properties.

References

[1] Abu Awad D and Roze D. 2017. Effects of partial selfing on the equilibrium genetic variance, mutation load and inbreeding depression under stabilizing selection. bioRxiv, 180000, ver. 4 of 17th November 2017. doi: 10.1101/180000

[2] Lande R. 1977. The influence of the mating system on the maintenance of genetic variability in polygenic characters. Genetics 86: 485–498.

[3] Charlesworth D and Charlesworth B. 1987. Inbreeding depression and its evolutionary consequences. Annual Review of Ecology and Systematics. 18: 237–268. doi: 10.1111/10.1146/annurev.es.18.110187.001321

[4] Lande R and Porcher E. 2015. Maintenance of quantitative genetic variance under partial self-fertilization, with implications for the evolution of selfing. Genetics 200: 891–906. doi: 10.1534/genetics.115.176693

[5] Roze D. 2015. Effects of interference between selected loci on the mutation load, inbreeding depression, and heterosis. Genetics 201: 745–757. doi: 10.1534/genetics.115.178533

[6] Martin G and Lenormand T. 2006. A general multivariate extension of Fisher's geometrical model and the distribution of mutation fitness effects across species. Evolution 60: 893–907. doi: 10.1111/j.0014-3820.2006.tb01169.x

[7] Martin G, Elena SF and Lenormand T. 2007. Distributions of epistasis in microbes fit predictions from a fitness landscape model. Nature Genetics 39: 555–560. doi: 10.1038/ng1998

Because of their shorter generation times and larger census population sizes, pathogens are usually ahead in the evolutionary race with their hosts. The risks linked to pathogen adaptation are still exacerbated in agronomy, where plant and animal populations are not freely evolving but depend on breeders and growers, and are usually highly genetically homogeneous. As a consequence, the speed of pathogen adaptation is crucial for agriculture sustainability. Unraveling the time scale required for pathogens’ adaptation to their hosts would notably greatly improve our estimation of the risks of pathogen emergence, the efficiency of disease control strategies and the design of epidemiological surveillance schemes. However, the temporal scale of pathogen evolution has received much less attention than its spatial scale [1]. In their study of a wheat fungal disease, Suffert et al. [2] reached contrasting conclusions about the pathogen adaptation depending on the time scale (intra- or inter-annual) and on the host genotype (sympatric or allopatric) considered, questioning the experimental assessment of this important problem.

Suffert et al. [2] sampled two pairs of Zymoseptoria tritici (the causal agent of septoria leaf blotch) sub-populations in a bread wheat field plot, representing (i) isolates collected at the beginning or at the end of an epidemic in a single growing season (2009-2010 intra-annual sampling scale) and (ii) isolates collected from plant debris at the end of growing seasons in 2009 and in 2015 (inter-annual sampling scale). Then, they measured in controlled conditions two aggressiveness traits of the isolates of these four Z. tritici sub-populations, the latent period and the lesion size on leaves, on two wheat cultivars. One of the cultivars was considered as "sympatric" because it was at the source of the studied isolates and was predominant in the growing area before the experiment, whereas the other cultivar was considered as "allopatric" since it replaced the previous one and became predominant in the growing area during the sampling period.

On the sympatric host, at the intra-annual scale, they observed a marginally-significant decrease in latent period and a significant decrease of the between-isolate variance for this trait, which are consistent with a selection of pathogen variants with an enhanced aggressiveness. In contrast, at the inter-annual scale, no difference in the mean or variance of aggressiveness trait values was observed on the sympatric host, suggesting a lack of pathogen adaptation. They interpreted the contrast between observations at the two time scales as the consequence of a trade-off for the pathogen between a gain of aggressiveness after several generations of asexual reproduction at the intra-annual scale and a decrease of the probability to reproduce sexually and to be transmitted from one growing season to the next. Indeed, at the end of the growing season, the most aggressive isolates are located on the upper leaves of plants, where the pathogen density and hence probably also the probability to reproduce sexually, is lower. On the allopatric host, the conclusion about the pathogen stability at the inter-annual scale was somewhat different, since a significant increase in the mean lesion size was observed (isolates corresponding to the intra-annual scale were not checked on the allopatric host). This shows the possibility for the pathogen to evolve at the inter-annual scale, for a given aggressiveness trait and on a given host.

In conclusion, Suffert et al.’s [2] study emphasizes the importance of the experimental design in terms of sampling time scale and host genotype choice to analyze the pathogen adaptation to its host plants. It provides also an interesting scenario, at the crossroad of the pathogen’s reproduction regime, niche partitioning and epidemiological processes, to interpret these contrasted results. Pathogen adaptation to plant cultivars with major-effect resistance genes is usually fast, including in the wheat-Z. tritici system [3]. Therefore, this study will be of great help for future studies on pathogen adaptation to plant partial resistance genes and on strategies of deployment of such resistance at the landscape scale.

References
[1] Penczykowski RM, Laine A-L and Koskella B. 2016. Understanding the ecology and evolution of host–parasite interactions across scales. Evolutionary Applications, 9: 37–52. doi: 10.1111/eva.12294

[2] Suffert F, Goyeau H, Sache I, Carpentier F, Gelisse S, Morais D and Delestre G. 2017. Epidemiological trade-off between intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen population. bioRxiv, 151068, ver. 3 of 12th November 2017. doi: 10.1101/151068

[3] Brown JKM, Chartrain L, Lasserre-Zuber P and Saintenac C. 2015. Genetics of resistance to Zymoseptoria tritici and applications to wheat breeding. Fungal Genetics and Biology, 79: 33–41. doi: 10.1016/j.fgb.2015.04.017

Dating nodes in a phylogeny is an important problem in evolution and is typically performed by using molecular clocks and fossil age estimates [1]. The manuscript by Chauve et al. [2] reports a novel method, which uses lateral gene transfers to help ordering nodes in a species tree. The idea is that a lateral gene transfer can only occur between two species living at the same time, which indirectly informs on node relative ages in a phylogeny: the donor species cannot be more recent than the recipient species. Horizontal gene transfers are increasingly recognized as frequent, even in eukaryotes, and especially in micro-organisms that have little fossil records [3-7]. Yet, such an important source of information has been very rarely used so far for inferring relative node ages in phylogenies. In this context, the method by Chauve et al. [2] represents an innovative and original approach to a difficult problem. An obvious limitation of the approach is that it relies on inferences of horizontal transfers, which detection is in itself a difficult problem. Incomplete taxon sampling, or the extinction of the true donor lineage may render patterns difficult to interpret in a temporary fashion. Yet, for clades with no fossils this may be the only piece of information we have at hand, and the growing amount of sequence data is likely to minimize issues derived from incomplete sampling.

The developed method, MaxTiC (for Maximal Time Consistency) [2], represents a very nice application of theoretical developments on the well-known « Feedback Arc Set » computer science problem to the evolutionary question of ordering nodes in a phylogeny. MaxTiC uses as input a species tree and a set of time constraints based on lateral gene transfers inferred using other softwares, and minimizes conflicts between node ordering and these time constraints. The application of MaxTiC on simulated datasets indicated that node ordering was fairly accurate [2]. MaxTiC is implemented in a freely available software, which represents original and relevant contribution to the field of evolutionary biology.

References

[1] Donoghue P and Smith M, editors. 2003. Telling the evolutionary time. CRC press.

[2] Chauve C, Rafiey A, Davin AA, Scornavacca C, Veber P, Boussau B, Szöllősi GJ, Daubin V and Tannier E. 2017. MaxTiC: Fast ranking of a phylogenetic tree by Maximum Time Consistency with lateral gene transfers. bioRxiv 127548, ver. 6 of 6th November 2017. doi: 10.1101/127548

[3] Ropars J, Rodríguez de la Vega RC, Lopez-Villavicencio M, Gouzy J, Sallet E, Debuchy R, Dupont J, Branca A and Giraud T. 2015. Adaptive horizontal gene transfers between multiple cheese-associated fungi. Current Biology 19, 2562–2569. doi: 10.1016/j.cub.2015.08.025

[4] Novo M, Bigey F, Beyne E, Galeote V, Gavory F, Mallet S, Cambon B, Legras JL, Wincker P, Casaregola S and Dequin S. 2009. Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118. Proceeding of the National Academy of Science USA, 106, 16333–16338. doi: 10.1073/pnas.0904673106

[5] Naranjo-Ortíz MA, Brock M, Brunke S, Hube B, Marcet-Houben M, Gabaldón T. 2016. Widespread inter- and intra-domain horizontal gene transfer of d-amino acid metabolism enzymes in Eukaryotes. Frontiers in Microbiology 7, 2001. doi: 10.3389/fmicb.2016.02001

[6] Alexander WG, Wisecaver JH, Rokas A, Hittinger CT. 2016. Horizontally acquired genes in early-diverging pathogenic fungi enable the use of host nucleosides and nucleotides. Proceeding of the National Academy of Science USA. 113, 4116–4121. doi: 10.1073/pnas.1517242113

[7] Marcet-Houben M, Gabaldón T. 2010. Acquisition of prokaryotic genes by fungal genomes. Trends in Genetics. 26, 5–8. doi: 10.1016/j.tig.2009.11.007

Local adaptation, the higher fitness a population achieves in its local “home” environment relative to other environments is a crucial phase in the divergence of populations, and as such both generates and maintains diversity. Local adaptation is enhanced by selection and genetic variation in the relevant traits, and decreased by gene flow and genetic drift.

Demonstrating local adaptation is laborious, and is typically done with a reciprocal transplant design [1], documenting repeated geographic clines [e.g. 2, 3] also provides strong evidence of local adaptation. Even when well documented, it is often unknown which aspects of the environment impose selection. Indeed, differences in environment between different sites that are measured during studies of local adaptation explain little of the variance in the degree of local adaptation [4]. This poses a problem to population management. Given climate change and habitat destruction, understanding the environmental drivers of local adaptation can be crucially important to conducting successful assisted migration or targeted gene flow.

In this manuscript, Macdonald et al. [5] propose a means of identifying which aspects of the environment select for local adaptation without conducting a reciprocal transplant experiment. The idea is that the strength of relationships between traits and environmental variables that are due to plastic responses to the environment will not be influenced by gene flow, but the strength of trait-environment relationships that are due to local adaptation should decrease with gene flow. This then can be used to reduce the somewhat arbitrary list of environmental variables on which data are available down to a targeted list more likely to drive local adaptation in specific traits. To perform such an analysis requires three things: 1) measurements of traits of interest in a species across locations, 2) an estimate of gene flow between locations, which can be replaced with a biologically meaningful estimate of how well connected those locations are from the point of view of the study species, and 3) data on climate and other environmental variables from across a species’ range, many of which are available on line.

Macdonald et al. [5] demonstrate their approach using a skink (Lampropholis coggeri). They collected morphological and physiological data on individuals from multiple populations. They estimated connectivity among those locations using information on habitat suitability and dispersal potential [6], and gleaned climatic data from available databases and the literature. They find that two physiological traits, the critical minimum and maximum temperatures, show the strongest signs of local adaptation, specifically local adaptation to annual mean precipitation, precipitation of the driest quarter, and minimum annual temperature. These are then aspects of skink phenotype and skink habitats that could be explored further, or could be used to provide background information if migration efforts, for example for genetic rescue [7] were initiated. The approach laid out has the potential to spark a novel genre of research on local adaptation. It its simplest form, knowing that local adaptation is eroded by gene flow, it is intuitive to consider that if connectivity reduces the strength of the relationship between an environmental variable and a trait, that the trait might be involved in local adaptation. The approach is less intuitive than that, however – it relies not connectivity per-se, but the interaction between connectivity and different environmental variables and how that interaction alters trait-environment relationships. The authors lay out a number of useful caveats and potential areas that could use further development. It will be interesting to see how the community of evolutionary biologists responds.

References

[1] Blanquart F, Kaltz O, Nuismer SL and Gandon S. 2013. A practical guide to measuring local adaptation. Ecology Letters, 16: 1195-1205. doi: 10.1111/ele.12150

[2] Huey RB, Gilchrist GW, Carlson ML, Berrigan D and Serra L. 2000. Rapid evolution of a geographic cline in size in an introduced fly. Science, 287: 308-309. doi: 10.1126/science.287.5451.308

[3] Milesi P, Lenormand T, Lagneau C, Weill M and Labbé P. 2016. Relating fitness to long-term environmental variations in natura. Molecular Ecology, 25: 5483-5499. doi: 10.1111/mec.13855

[4] Hereford, J. 2009. A quantitative survey of local adaptation and fitness trade-offs. The American Naturalist 173: 579-588. doi: 10.1086/597611

[5] Macdonald SL, Llewelyn J and Phillips BL. 2017. Using connectivity to identify climatic drivers of local adaptation. bioRxiv, ver. 4 of October 4, 2017. doi: 10.1101/145169

[6] Macdonald SL, Llewelyn J, Moritz C and Phillips BL. 2017. Peripheral isolates as sources of adaptive diversity under climate change. Frontiers in Ecology and Evolution, 5:88. doi: 10.3389/fevo.2017.00088

[7] Whiteley AR, Fitzpatrick SW, Funk WC and Tallmon DA. 2015. Genetic rescue to the rescue. Trends in Ecology & Evolution, 30: 42-49. doi: 10.1016/j.tree.2014.10.009