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

In a landmark paper, Flores et al. (2011) showed that the interactions between bacteria and their viruses could be nicely described using a bipartite infection networks.  Two quantitative properties of these networks were of particular interest, namely modularity and nestedness.  Modularity emerges when groups of host species (or genotypes) shared groups of viruses.  Nestedness provided a view of the degree of specialization of both partners: high nestedness suggests that hosts differ in their susceptibility to infection, with some highly susceptible host genotypes selecting for very specialized viruses while strongly resistant host genotypes select for generalist viruses.  Translated to the plant pathology parlance, this extreme case would be equivalent to a gene-for-gene infection model (Flor 1956): new mutations confer hosts with resistance to recently evolved viruses while maintaining resistance to past viruses.  Likewise, virus mutations for expanding host range evolve without losing the ability to infect ancestral host genotypes.  By contrast, a non-nested network would represent a matching-allele infection model (Frank 2000) in which each interacting organism evolves by losing its capacity to resist/infect its ancestral partners, resembling a Red Queen dynamic.  Obviously, the reality is more complex and may lie anywhere between these two extreme situations.

Recently, Valverde et al. (2020) developed a model to explain the emergence of nestedness and modularity in plant-virus infection networks across diverse habitats.  They found that local modularity could coexist with global nestedness and that intraspecific competition was the main driver of the evolution of ecosystems in a continuum between nested-modular and nested networks.  These predictions were tested with field data showing the association between plant host species and different viruses in different agroecosystems (Valverde et al. 2020).  The effect of interspecific competition in the structure of empirical plant host-virus infection networks was also tested by McLeish et al. (2019).  Besides data from agroecosystems, evolution experiments have also shown the pervasive emergence of nestedness during the diversification of independently-evolved lineages of potyviruses in Arabidopsis thaliana genotypes that differ in their susceptibility to infection (Hillung et al. 2014; González et al. 2019; Navarro et al. 2020).

In their study, Moury et al. (2021) have expanded all these previous observations to a diverse set of pathosystems that range from viruses, bacteria, oomycetes, fungi, nematodes to insects.  While modularity was barely seen in only a few of the systems, nestedness was a common trend (observed in ~94% of all systems).  This nestedness, as seen in previous studies and as predicted by theory, emerged as a consequence of the existence of generalist and specialist strains of the parasites that differed in their capacity to infect more or less resistant plant genotypes.

As pointed out by Moury et al. (2021) in their conclusions, the ubiquity of nestedness in plant-parasite infection matrices has strong implications for the evolution and management of infectious diseases.

References

Flor, H. H. (1956). The complementary genic systems in flax and flax rust. In Advances in genetics, 8, 29-54. https://doi.org/10.1016/S0065-2660(08)60498-8

Flores, C. O., Meyer, J. R., Valverde, S., Farr, L., and Weitz, J. S. (2011). Statistical structure of host–phage interactions. Proceedings of the National Academy of Sciences, 108, E288-E297. https://doi.org/10.1073/pnas.1101595108

Frank, S. A. (2000). Specific and non-specific defense against parasitic attack. Journal of Theoretical Biology, 202, 283-304. https://doi.org/10.1006/jtbi.1999.1054

González, R., Butković, A., and Elena, S. F. (2019). Role of host genetic diversity for susceptibility-to-infection in the evolution of virulence of a plant virus. Virus evolution, 5(2), vez024. https://doi.org/10.1093/ve/vez052

Hillung, J., Cuevas, J. M., Valverde, S., and Elena, S. F. (2014). Experimental evolution of an emerging plant virus in host genotypes that differ in their susceptibility to infection. Evolution, 68, 2467-2480. https://doi.org/10.1111/evo.12458

McLeish, M., Sacristán, S., Fraile, A., and García-Arenal, F. (2019). Coinfection organizes epidemiological networks of viruses and hosts and reveals hubs of transmission. Phytopathology, 109, 1003-1010. https://doi.org/10.1094/PHYTO-08-18-0293-R

Moury B, Audergon J-M, Baudracco-Arnas S, Krima SB, Bertrand F, Boissot N, Buisson M, Caffier V, Cantet M, Chanéac S, Constant C, Delmotte F, Dogimont C, Doumayrou J, Fabre F, Fournet S, Grimault V, Jaunet T, Justafré I, Lefebvre V, Losdat D, Marcel TC, Montarry J, Morris CE, Omrani M, Paineau M, Perrot S, Pilet-Nayel M-L and Ruellan Y (2021) The quasi-universality of nestedness in the structure of quantitative plant-parasite interactions. bioRxiv, 2021.03.03.433745, ver. 4 recommended and peer-reviewed by PCI Evolutionary Biology. https://doi.org/10.1101/2021.03.03.433745

Navarro, R., Ambros, S., Martinez, F., Wu, B., Carrasco, J. L., and Elena, S. F. (2020). Defects in plant immunity modulate the rates and patterns of RNA virus evolution. bioRxiv. doi: https://doi.org/10.1101/2020.10.13.337402

Valverde, S., Vidiella, B., Montañez, R., Fraile, A., Sacristán, S., and García-Arenal, F. (2020). Coexistence of nestedness and modularity in host–pathogen infection networks. Nature ecology & evolution, 4, 568-577. https://doi.org/10.1038/s41559-020-1130-9

It is a classical result in the virulence evolution literature that treatments decreasing parasite replication within the host should select for higher replication rates, thus driving increased levels of virulence if the two are correlated. There is some evidence for this in vitro but very little in the field. HIV infections in humans offer a unique opportunity to go beyond the simple predictions that treatments should favour more virulent strains because many details of this host-parasite system are known, especially the link between set-point virus load, transmission rate and virulence.

To tackle this question, Herbeck et al. [1] used a detailed individual-based model. This is original because it allows them to integrate existing knowledge from the epidemiology and evolution of HIV (e.g. recent estimates of the ‘heritability’ of set-point virus load from one infection to the next). This detailed model allows them to formulate predictions regarding the effect of different treatment policies; especially regarding the current policy switch away from treatment initiation based on CD4 counts towards universal treatment.

The results show that, perhaps as expected from the theory, treatments based on the level of remaining host target cells (CD4 T cells) do not affect virulence evolution because they do not strongly affect the virulence level that maximizes HIV’s transmission potential. However, early treatments can lead to moderate increase in virulence within several years if coverage is high enough. These results seem quite robust to variation of all the parameters in realistic ranges.

The great step forward in this model is the ability to obtain quantitative prediction regarding how a virus may evolve in response to public health policies. Here the main conclusion is that given our current knowledge in HIV biology, the risk of virulence evolution is perhaps more limited than expected from a direct application of virulence evolution model. Interestingly, the authors also conclude that recently observed increased in HIV virulence [2-3] cannot be explained by the impact of antiretroviral therapy alone; which raises the question about the main mechanism behind this increase. Finally, the authors make the interesting suggestion that “changing virulence is amenable to being monitored alongside transmitted drug resistance in sentinel surveillance”.

References

[1] Herbeck JT, Mittler JE, Gottlieb GS, Goodreau SM, Murphy JT, Cori A, Pickles M, Fraser C. 2016. Evolution of HIV virulence in response to widespread scale up of antiretroviral therapy: a modeling study. Virus Evolution 2:vew028. doi: 10.1093/ve/vew028

[2] Herbeck JT, Müller V, Maust BS, Ledergerber B, Torti C, et al. 2012. Is the virulence of HIV changing? A meta-analysis of trends in prognostic markers of HIV disease progression and transmission. AIDS 26:193-205. doi: 10.1097/QAD.0b013e32834db418

[3] Pantazis N, Porter K, Costagliola D, De Luca A, Ghosn J, et al. 2014. Temporal trends in prognostic markers of HIV-1 virulence and transmissibility: an observational cohort study. Lancet HIV 1:e119-26. doi: 10.1016/s2352-3018(14)00002-2

Perhaps because of its deadliness, the 2013-2016 Ebola Virus (EBOV) epidemics in West-Africa has led to unprecedented publication and sharing of full virus genome sequences. This was both rapid (90 full genomes were shared within weeks [1]) and important (more than 1500 full genomes have been released overall [2]). Furthermore, the availability of the metadata (especially GPS location) has led to depth analyses of the geographical spread of the epidemics [3].
In their work, Dellicour et al. [4] pursue earlier phylogeographical investigations in an original and yet simple approach to address questions of key public health importance. The originality of the approach is dual. First, from a technical standpoint, they capture the spread of infectious diseases in a continuous framework using a novel model that allows for rare long-distance dispersal events. Second, in a more classical discrete meta-population framework, they simulate the effect of public health interventions by pruning the phylogenetic tree and assessing how this affects key parameters. For instance, to simulate the effect of closing borders they remove subsets of the phylogeny that involved dispersal between countries and to simulate the effect of protecting a region by quarantine they remove all the leaves (i.e. the infections sampled) from this region. This phylogeny pruning is both original and simple. It is however limited because it currently assumes that policies are 100% effective and earlier modelling work on human influenza showed that long distance travel bans had to be implemented with >99% efficiency in order to slow epidemic growth from a time scale of days to weeks [5].
From a biological standpoint, Dellicour et al. [4] corroborate earlier findings that highly populated locations (>1,000,000 inhabitants) were crucial in explaining the magnitude of the epidemics but also show the importance of the transmission between the three capital cities. They also show that rare long-distance dispersing events of the virus are not key to explaining the magnitude of the epidemics (even though they assume 100% efficiency of suppressing long-distance event). Finally, thanks to their continuous model they estimate the speed of spread of the epidemics and are able to detect the effect of border closing on this speed.
Overall, this study [4], which involves state-of-the-art Bayesian inference methods of infection phylogenies using MCMC, stands out because of its effort to simulate public health interventions. It stands as an encouragement for the development of intervention models with increased realism and for even faster and larger virus sequence data sharing.

References

[1] Gire et al. 2014. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345: 1369–1372. doi: 10.1126/science.1259657.
[2] Holmes EC, Dudas G, Rambaut A and Andersen KG. 2016. The evolution of Ebola virus: insights from the 2013-2016 epidemic. Nature 538: 193–200. doi: 10.1038/nature19790.
[3] Dudas et al. 2017. Virus genomes reveal factors that spread and sustained the Ebola epidemic. Nature 544: 309–315 (2017). doi: 10.1038/nature22040.
[4] Dellicour S, Baele G, Dudas G, Faria NR, Pybus OG, Suchard MA, Rambaud A and Lemey P. 2018. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. bioRxiv, 163691, ver. 3 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/163691.
[5] Hollingsworth TD, Ferguson NM and Anderson RM. 2006. Will travel restrictions control the international spread of pandemic influenza? Nature Medicine 12, 497–499. doi: 10.1038/nm0506-497.

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