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

This very nice paper by Martinez et al. [1] provides further evidence, if further evidence was needed, of the extent to which heritable microorganisms run the evolutionary show.
Wolbachia is an ubiquitous endosymbiont of arthropods who has been recently shown to protect its hosts against viral infections. Here, Martinez et al. are able to show that this multifaceted heritable symbiont weakens selective pressures induced by viruses on host immune genes. In a series of very elegant experiments, Wolbachia-infected and Wolbachia-free populations of D. melanogaster were exposed to Drosophila C virus (a natural, and highly virulent Drosophila pathogen). At the end of a 9-generation artificial selection protocol with DCV, resistance against DCV increased in flies, both in the presence and absence of Wolbachia. Wolbachia-infected flies were still substantially more resistant to DCV viruses than their Wolbachia-free counterparts. Crucially, however, the frequency of the pastrel resistant allele (a key immune gene for DCV resistance) was significantly lower in the Wolbachia-infected lines. As a consequence, when the DCV-evolved lines were treated with antibiotics to cure them from the bacterial infection, the lines who had evolved with Wolbachia tended to be more susceptible to the virus than their uninfected counterparts.
In other words, infection by protective heritable symbionts can affect how selection acts on the host's nuclear-based resistance, effectively rendering it dependent on its symbiont for the fight against pathogens.
But the interest of these results may not be simply academic. The protective qualities of Wolbachia against a range of pathogens have opened up the exciting possibility of transferring these bacteria to mosquito vectors of key human diseases such as dengue or malaria. The long term evolutionary potential for these novel Wolbachia-host interactions has, however, been little explored. Either the Wolbachia, the pathogen or, as shown here, the host, could evolve in more or less predictable ways. There is, for example, evidence showing that in novel hosts Wolbachia evolves rapidly and tends to gradually lose its virulence. If the lost virulence was to result in a decrease in their pathogen defensive qualities, the mosquito, having lost the efficiency of its conventional immune defences, could end up being more vulnerable to infection than before the Wolbachia introduction. Martinez et al.'s paper is a nice example of how investigating the evolutionary potential of such Wolbachia-host-pathogen interactions can be hugely informative as to the long term prospects of these new control methods.

Reference

[1] Martinez J, Cogni R, Cao C, Smith S, Illingworth CJR & Jiggins FM. 2016. Addicted? Reduced host resistance in populations with defensive symbionts. Proceedings of the Royal Society of London B 283:20160778. doi: 10.1098/rspb.2016.0778

As plants cannot run from their enemies, natural selection has favoured the evolution of diverse chemical compounds (phytochemicals) to protect them against herbivores and pathogens. This provides an opportunity for plant feeders to exploit these compounds to combat their own enemies. Indeed, it is widely known that herbivores use such compounds as protection against predators [1]. Recently, this reasoning has been extended to pathogens, and elegant studies have shown that some herbivores feed on phytochemical-containing plants reducing both parasite abundance within hosts [2] and their virulence [3].
Suffering less from parasites is clearly beneficial for infected herbivores. Why then, is this behaviour not fixed in nature? There are, at least, two possible explanations. First, feeding on ‘medicinal’, often toxic, plants may impose costs upon uninfected individuals. Second, parasites may evolve resistance to such chemicals. Whereas the first possibility has been explored, and is taken as evidence for ‘self-medication’ [3], the second hypothesis requires investigation. A recent study by Palmer-Young et al. [4] fills this gap. This article reports evolution of resistance to two different phytochemicals, alone and in combination, in the trypanosome Crithidia bombi, a bumble bee (Bombus impatiens) parasite. To show this, the authors experimentally evolved a strain of C. bombi in the presence of thymol, eugenol or both simultaneously. These phytochemicals are commonly found in the nectar of several plant species, in particular those of the Lamiaceae, a family containing several aromatic herbs. Prior to selection both phytochemicals reduced C. bombi growth by about 50%. However, C. bombi rapidly evolved resistance in both single and the double phytochemical treatments. Moreover, no cost of resistance was detected when evolved parasites were placed in the ancestral, phytochemical-free environment. Therefore, resistance to phytochemicals would be expected to spread rapidly in natural populations of C. bombi. Clearly, thus, the herbivore strategy of sequestering plant secondary chemical compounds as a defence against their pathogens should fail. The question then is ‘why do they still do it’? Can self-medication work in the longer-term for bumblebees?
Well, yes. The very fact that resistance evolved shows that resistance is not fixed in natural C. bombi populations. This is surprising considering that resistance is not costly. This might be due to a number of reasons. Firstly, there may be costs of resistance that were not detected in this experiment. Second, it may not be possible to evolve universal resistance to the heterogeneity present in the phytochemical environment. Indeed, phytochemical environments are highly varied in time and space and bumblebees will visit different plants presenting different phytochemical cocktails throughout the season. Furthermore, migration of bees from populations exposed to different phytochemicals may prevent the fixation of one resistance type.
Or, it may be self-medication behaviour itself that prevents the evolution of resistance? Indeed, in the same way that infected bees seek cooler temperatures to slow growth of a parasitoid fly [5], they may also seek a more varied diet with diverse phytochemicals to which the parasite cannot evolve, but which reduces parasite growth. Further understanding of arthropod self-medication may thus be instrumental to prevent the observed worldwide decline of pollinators [6]. Furthermore, it may inform on mechanisms that prevent rapid evolution of drug resistance in other systems.

References

[1] Duffey SS. 1980. Sequestration of plant natural products by insects. Annual Review of Entomology 25: 447-477. doi: 10.1146/annurev.en.25.010180.002311

[2] Richardson LL et al. 2015. Secondary metabolites in floral nectar reduce parasite infections in bumblebees. Proceedings of the Royal Society of London B 282: 20142471. doi: 10.1098/rspb.2014.2471

[3] Lefèvre T et al. 2010. Evidence for trans-generational medication in nature. Ecology Letters 13: 1485-93. doi: 10.1111/j.1461-0248.2010.01537.x

[4] Palmer-Young EC, Sadd BM, Adler LS. 2017. Evolution of resistance to single and combined floral phytochemicals by a bumble bee parasite. Journal of Evolutionary Biology 30: 300-312. doi: 10.1111/jeb.13002

[5] Müller CB, Schmid-Hempel P. 1993. Exploitation of cold temperature as defence against parasitoids in bumblebees. Nature 363: 65-67. doi: 10.1038/363065a0

[6] Potts SG et al. 2010. Global pollinator declines: trends, impacts and drivers. Trends in Ecology and Evolution 25: 345-353. doi: 10.1016/j.tree.2010.01.007

Within-host parasite density is tightly linked to parasite fitness often determining both transmission success and virulence (parasite-induced harm to the host) (Alizon et al., 2009, Anderson & May, 1982). Parasite density may thus be controlled by selection balancing these conflicting pressures. Actual within-host density regulation may be under host or parasite control, or due to other environmental factors (Wale et al., 2019, Vale et al., 2011, Chrostek et al., 2013). Vertically transmitted parasites may also be more vulnerable to drift associated with bottlenecks between generations, which may also determine within-host population size (Mathe-Hubert et al., 2019, Mira & Moran, 2002).

Bénard et al. (2021) use 3 experiments to disentangle the role of drift versus host factors in the control of within-host Wolbachia growth in Drosophila melanogaster. They use the wMelPop Wolbachia strain in which virulence (fly longevity) and within-host growth correlate positively with copy number in the genomic region Octomom (Chrostek et al., 2013, Chrostek & Teixeira, 2015). Octomom copy number can be used as a marker for different genetic lineages within the wMelPop strain.

In a first experiment, they introgressed and backcrossed this Wolbachia strain into 6 different host genetic backgrounds and show striking differences in within-host symbiont densities which correlate positively with Octomom copy number. This is consistent with host genotype selecting different Wolbachia strains, but also with bottlenecks and drift between generations. To distinguish between these possibilities, they perform 2 further experiments. 

A second experiment repeated experiment 1, but this time introgression was into 3 independent lines of the Bolivia and USA Drosophila populations; those that, respectively, exhibited the lowest and highest Wolbachia density and Octomom copy number. In this experiment, growth and Octomom copy number were measured across the 3 lines, for each population, after 1, 13 and 25 generations. Although there were little differences between replicates at generation 1, there were differences at generations 13 and 25 among the replicates of both the Bolivia and USA lines. These results are indicative of parasite control, or drift being responsible for within-host growth rather than host factors. 

A third experiment tested whether Wolbachia density and copy number were under host or parasite control. This was done, again using the USA and Bolivia lines, but this time those from the first experiment, several generations following the initial introgression and backcrossing. The newly introgressed lines were again followed for 25 generations. At generation 1, Wolbachia phenotypes resembled those of the donor parasite population and not the recipient host population indicating a possible maternal effect, but a lack of host control over the parasite. Furthermore, Wolbachia densities and Octomom number differed among replicate lines through time for Bolivia populations and from the donor parasite lines for both populations. These differences among replicate lines that share both host and parasite origins suggest that drift and/or maternal effects are responsible for within-host Wolbachia density and Octomom number. 

These findings indicate that drift appears to play a role in shaping Wolbachia evolution in this system. Nevertheless, completely ruling out the role of the host or parasite in controlling densities will require further study. The findings of Bénard and coworkers (2021) should stimulate future work on the contribution of drift to the evolution of vertically transmitted parasites.

References

Alizon S, Hurford A, Mideo N, Baalen MV (2009) Virulence evolution and the trade-off hypothesis: history, current state of affairs and the future. Journal of Evolutionary Biology, 22, 245–259. https://doi.org/10.1111/j.1420-9101.2008.01658.x

Anderson RM, May RM (1982) Coevolution of hosts and parasites. Parasitology, 85, 411–426. https://doi.org/10.1017/S0031182000055360

Bénard A, Henri H, Noûs C, Vavre F, Kremer N (2021) Wolbachia load variation in Drosophila is more likely caused by drift than by host genetic factors. bioRxiv, 2020.11.29.402545, ver. 4  recommended and peer-reviewed by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.11.29.402545

Chrostek E, Marialva MSP, Esteves SS, Weinert LA, Martinez J, Jiggins FM, Teixeira L (2013) Wolbachia Variants Induce Differential Protection to Viruses in Drosophila melanogaster: A Phenotypic and Phylogenomic Analysis. PLOS Genetics, 9, e1003896. https://doi.org/10.1371/journal.pgen.1003896

Chrostek E, Teixeira L (2015) Mutualism Breakdown by Amplification of Wolbachia Genes. PLOS Biology, 13, e1002065. https://doi.org/10.1371/journal.pbio.1002065

Mathé‐Hubert H, Kaech H, Hertaeg C, Jaenike J, Vorburger C (2019) Nonrandom associations of maternally transmitted symbionts in insects: The roles of drift versus biased cotransmission and selection. Molecular Ecology, 28, 5330–5346. https://doi.org/10.1111/mec.15206

Mira A, Moran NA (2002) Estimating Population Size and Transmission Bottlenecks in Maternally Transmitted Endosymbiotic Bacteria. Microbial Ecology, 44, 137–143. https://doi.org/10.1007/s00248-002-0012-9

Vale PF, Wilson AJ, Best A, Boots M, Little TJ (2011) Epidemiological, Evolutionary, and Coevolutionary Implications of Context-Dependent Parasitism. The American Naturalist, 177, 510–521. https://doi.org/10.1086/659002

Wale N, Jones MJ, Sim DG, Read AF, King AA (2019) The contribution of host cell-directed vs. parasite-directed immunity to the disease and dynamics of malaria infections. Proceedings of the National Academy of Sciences, 116, 22386–22392. https://doi.org/10.1073/pnas.1908147116

Species are immersed within communities in which they interact mutualistically, as in pollination or seed dispersal, or nonreciprocally, such as in predation or parasitism, with other species and these interactions play a paramount role in shaping biodiversity (Bascompte and Jordano 2013). Researchers have become increasingly interested in the processes that shape these interactions and how these influence community structure and responses to disturbances. Species interactions are often described using bipartite interaction networks and one important question is how the evolutionary history of the species involved influences the network, including whether there is phylogenetic signal in interactions, in other words whether closely related species interact with other closely related species (Bascompte and Jordano 2013, Perez-Lamarque et al. 2022). To address this question different approaches, correlative and model-based, have been developed to test for phylogenetic signal in interactions, although comparative analyses of the performance of these different metrics are lacking. In their article Perez-Lamarque et al. (2022) set out to test the statistical performance of two widely-used methods, Mantel tests and Phylogenetic Bipartite Linear Models (PBLM; Ives and Godfray 2006) using simulations. Phylogenetic signal is measured as the degree to which distance to the nearest common ancestor predicts the observed similarity in trait values among species. In species interaction networks, the data are actually the between-species dissimilarity among interacting species (Perez-Lamarque et al. 2022), and typical approaches to test for phylogenetic signal cannot be used. However, the Mantel test provides a useful means of analyzing the correlation between two distance matrices, the between-species phylogenetic distance and the between-species dissimilarity in interactions. The PBLM approach, on the other hand, assumes that interactions between species are influenced by unobserved traits that evolve along the phylogenies following a given phenotypic evolution model and the parameters of this model are interpreted in terms of phylogenetic signal (Ives and Godfray 2006). Perez-Lamarque et al (2022) found that the model-based PBLM approach has a high type-I error rate, in other words it often detected phylogenetic signal when there was none. The simple Mantel test was found to present a low type-I error rate and moderate statistical power. However, it tended to overestimate the degree to which species interact with dissimilar partners. In addition to the aforementioned analyses, the authors also tested whether the simple Mantel test was able to detect phylogenetic signal in interactions among species within a given clade in the phylogeny, as phylogenetic signal in species interactions may be localized within specific clades. The article concludes with general guidelines for users wishing to test phylogenetic signal in their interaction networks and illustrates them with an example of an orchid-mycorrhizal fungus network from the oceanic island of La Réunion (Martos et al 2012). This broadly accessible article provides a valuable analysis of the performance of tests of phylogenetic signal in interaction networks enabling users to make informed choices of the analytical methods they wish to employ, and provide useful and detailed guidelines. Therefore, the work should be of broad interest to researchers studying species interactions.  

References

Bascompte J, Jordano P (2013) Mutualistic Networks. Princeton University Press. https://doi.org/10.1515/9781400848720

Ives AR, Godfray HCJ (2006) Phylogenetic Analysis of Trophic Associations. The American Naturalist, 168, E1–E14. https://doi.org/10.1086/505157

Martos F, Munoz F, Pailler T, Kottke I, Gonneau C, Selosse M-A (2012) The role of epiphytism in architecture and evolutionary constraint within mycorrhizal networks of tropical orchids. Molecular Ecology, 21, 5098–5109. https://doi.org/10.1111/j.1365-294X.2012.05692.x

Perez-Lamarque B, Maliet O, Pichon B, Selosse M-A, Martos F, Morlon H (2022) Do closely related species interact with similar partners? Testing for phylogenetic signal in bipartite interaction networks. bioRxiv, 2021.08.30.458192, ver. 6 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.08.30.458192