Many emerging diseases arise by parasites switching to new host species, while other parasites seem to remain with same host lineage for very long periods of time, even over timescales where an ancestral host species splits into two or more new species. The ability to understand these dynamics would form an important part of our understanding of infectious disease.

Experiments are clearly important for understanding these processes, but so are comparative studies, investigating the variation that we find in nature. Such comparative data do show strong signs of non-randomness, and this suggests that the epidemiological and ecological processes might be predictable, at least in part. For example, when we map patterns of parasite presence/absence onto host phylogenies, we often find that certain host clades harbour many more parasites than expected, or that closely-related hosts harbour closely-related parasites. Nevertheless, it remains difficult to interpret these patterns to make inferences about ecological and epidemiological processes. This is partly because non-random associations can arise in multiple ways. For example, parasites might be inherited from the common ancestor of related hosts, or might switch to new hosts, but preferentially establish on novel hosts that are closely related to their existing host. Infection might also influence the shape of host phylogeny, either by increasing the rate of host extinction or, conversely, increasing the rate of speciation (as with manipulative symbionts that might induce reproductive isolation).

These various processes have, by and large, been studied in isolation, but the model introduced by Engelstädter and Fortuna [1], makes an important first step towards studying them together. Without such combined analyses, we will not be able to tell if the processes have their own unique signatures, or whether the same sort of non-randomness can arise in multiple ways.

A major finding of the work is that the size of a host clade can be an important determinant of its overall infection level. This had been shown in previous work, assuming that the host phylogeny was fixed, but the current paper shows that it extends also to situations where host extinction and speciation takes place at a comparable rate to host shifting. This finding, then, calls into question the natural assumption that a clade of host species that is highly parasite ridden, must have some genetic or ecological characteristic that makes them particularly prone to infection, arguing that the clade size, rather than any characteristic of the clade members, might be the important factor. It will be interesting to see whether this prediction about clade size is borne out with comparative studies.

Another feature of the study is that the framework is naturally extendable, to include further processes, such as the influence of parasite presence on extinction or speciation rates. No doubt extensions of this kind will form the basis of important future work.

References

[1] Engelstädter J and Fortuna NZ. 2018. The dynamics of preferential host switching: host phylogeny as a key predictor of parasite prevalence and distribution. bioRxiv 209254 ver. 5 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/209254

Symbiotic associations with bacterial partners have facilitated important evolutionary transitions in the life histories of eukaryotes. For instance, many insects have established long-term interactions with intracellular bacteria that provide them with essential nutrients lacking in their diet. However, despite the high level of interdependency among organisms involved in endosymbiotic systems, examples of symbiont replacements along the evolutionary history of insect hosts are numerous. In their paper, Husnik and McCutcheon [1] test the stability of symbiotic systems in a particularly imbricated Russian-doll type interaction, where one bacterium lives insides another bacterium, which itself lives inside insect cells. For their study, they chose representative species of mealybugs (Pseudococcidae), a species rich group of sap-feeding insects that hosts diverse and complex symbiotic systems. In species of the subfamily Pseudococcinae, data published so far suggest that the primary symbiont, a ß-proteobacterium named Tremblaya princeps, is supplemented by a second bacterial symbiont (a ϒ-proteobaterium) that lives within its cytoplasm; both participate to the metabolic pathways that provide essential amino acids and vitamins to their hosts. Here, Husnik and McCutcheon generate host and endosymbiont genome data for five phylogenetically divergent species of Pseudococcinae in order to better understand: 1) the evolutionary history of the symbiotic associations; 2) the metabolic roles of each partner, 3) the timing and origin of Horizontal Gene Transfers (HGT) between the hosts and their symbionts.
Their results show that all species harbour the primary and at least one secondary symbiont, whose intra-bacterial localization was verified using fluorescence in situ hybridization. In one species (Pseudococcus longispinus), Tremblaya even hosts two intracellular bacteria each with a large genome (even though it is not entirely clear whether these two symbionts are indeed found within Tremblaya). The obligate presence of an intra-bacterial symbiont is best explained by the loss in Tremblaya princeps of critical genes for translation that require “intracellular complementation”.
The most striking result concerns the identity of the intra-bacterial symbiont: genome sizes and structures of the “secondary” (intra-Tremblaya) symbionts vary drastically according to the host species. Phylogenetic analyses based on 80 conserved proteins, place all these ϒ-proteoacteria (except one of the P. longispinus symbionts) in a Sodalis allied clade. However, their relationships do not mirror the one of their hosts, and some of them show signs of very recent acquisition. Altogether these results provide strong evidence for several independent acquisitions of these highly intra-bacterial integrated symbionts. Scenarios for the history of the symbiosis are clearly laid out and discussed by the authors, and the scenario involving several independent replacements of a the intra-Tremblaya symbiont by diverse Sodalis-like bacteria appears the most likely given the data presented here. Selected biosynthetic functions are then mapped onto the host and symbiont genomes, showing the high level of interdependency of the partners for the synthesis of essential amino acids and vitamin. These maps also identify genes in the host genome that might have been acquired through HGT from bacteria and show that many of them are shared by all mealybug species sequenced so far. HGT events have thus predated the acquisitions of the current “intra-Tremblaya“ symbionts and have probably been acquired from previous symbiont infections.
Overall, this is a thorough study, using a diverse set of data and meticulous analyses that present convincing evidence that replacements of symbionts occurred repeatedly even in an imbricated symbiotic system. The phylogenetic analyses inferring the timing of HGTs also depict a highly dynamic history of gene losses and retentions in both host and symbiont genomes. Altogether these results demonstrate how the chimeric nature of individuals allows shuffling at different levels of organisation: organisms like symbionts can be replaced, genes can be exchanged among the partners of the chimera. The surprise is that constraints arising from interdependencies do not impede these exchanges. In the discussion, the authors present a very interesting parallel with the evolution of organelles: if the story presented here mirrors the one of mitochondria, it supports a gradualist view where mitochondria arrived late in the evolution of eukaryotic cells that already contained many bacterial genes resulting from HGT from previous symbionts.

Reference

[1] Husnik F., McCutcheon JP. 2016. Repeated replacements of an intrabacterial symbiont in the tripartite nested mealybug symbiosis. PNAS 113: E5416-E5424. doi: 10.1073/pnas.1603910113

Interactions between species are a driving force in evolution. Many organisms host symbiotic partners that live all or part of their life in or on their host. Whether they are mutualistic or parasitic, these symbiotic associations impose strong selective pressures on both partners and affect their evolutionary trajectories. In fine, they can have a significant impact on the diversification patterns of both host and symbiont lineages, with symbiotic lineages sometimes speciating simultaneously with their hosts and/or switching from one host species to another. Long-term associations between species can also result in gene transfers between the involved organisms. Those lateral gene transfers are a source of ecological innovation but can obscure the phylogenetic signals and render the process of phylogenetic reconstructions complex (Lerat et al. 2003).

Methods known as reconciliations explore similarities and differences between phylogenetic trees. They have been widely used to both compare the diversification patterns of hosts and symbionts and identify lateral gene transfers between species. Though the reconciliation approaches used in host/ symbiont and species/ gene phylogenetic studies are identical, they are always applied separately to solve either molecular evolution questions or investigate the evolution of ecological interactions. However, the two questions are often intimately linked and the current interest in multi-level systems (e.g. the holobiont concept) calls for a unique model that will take into account three-level nested organization (gene/symbiont/ host) where both symbiont and genes can transfer among hosts. 

Here Menet and collaborators (2023) provide such a model to produce three-level reconciliations. In order to do so, they extend the two-level reconciliation model implemented in “ALE” software (Szöllősi et al. 2013), one of the most used and proven reconciliation methods. Briefly, given a symbiont gene tree, a symbiont tree and a host tree, as in previous reconciliation models, the symbiont tree is mapped onto the host tree by mixing three types of events: Duplication, Transfer or Loss (DTL), with a possibility of the symbiont evolving temporarily outside the host phylogeny (in a “ghost” host lineage). The gene tree evolves similarly inside the symbiont tree, but horizontal transfers are constrained to symbionts co-occurring within the same host. Joint reconciliation scenarios are reconstructed and DTL event rates and likelihoods are estimated according to the model. As a nice addition, the authors propose a method to infer the symbiont phylogeny through amalgamation from gene trees and a host tree.

The authors then explore the diverse possibilities offered by this method by testing it on both simulated datasets and biological datasets in order to check whether considering three nested levels is worthwhile. They convincingly show that three-level reconciliation has a better capacity to retrieve the symbiont donors and receivers of horizontal gene transfers, probably because transfers are constrained by additional elements relevant to the biological systems. Using, aphids, their obligate endosymbionts, and the symbiont genes involved in their nutritional functions, they identify horizontal gene transfers between aphid symbionts that are missed by two-level reconciliations but detected by expertise (Manzano-Marín et al. 2020). The other dataset presented here is on the human pathogen Helicobacter pylori, which history is supposed to reflect human migration. They use more than 1000 H. pylori gene families, and four populations, and use likelihood computations to compare different hypotheses on the diversification of the host.

In summary, this study is a proof-of-concept of a 3-level reconciliation, where the authors manage to convey the applicability of their framework to many biological systems. Reported complexities, confirmed by reported running times, show that the method is computationally efficient. Without a doubt, the tool presented here will be very useful to evolutionary biologists who want to investigate multi-scale cophylogenies and it will move forward the study of associations between host and symbionts when symbiont genomic data are available.

REFERENCES

Lerat, E., Daubin, V., & Moran, N. A. (2003). From gene trees to organismal phylogeny in prokaryotes: the case of the γ-Proteobacteria. PLoS biology, 1(1), e19.
https://doi.org/10.1371/journal.pbio.0000019
 
Manzano-Marın, A., Coeur d'acier, A., Clamens, A. L., Orvain, C., Cruaud, C., Barbe, V., & Jousselin, E. (2020). Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems. The ISME Journal, 14(1), 259-273.
https://doi.org/10.1038/s41396-019-0533-6

Menet H, Trung AN, Daubin V, Tannier E (2023) Host-symbiont-gene phylogenetic reconciliation. bioRxiv, 2022.07.01.498457, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.07.01.498457

Szöllősi, G. J., Rosikiewicz, W., Boussau, B., Tannier, E., & Daubin, V. (2013). Efficient exploration of the space of reconciled gene trees. Systematic biology, 62(6), 901-912.
https://doi.org/10.1093/sysbio/syt054

How do phenotypic plasticity and adaptive evolution interact in a novel or changing environment? Does evolution by natural selection generally reinforce initially plastic phenotypic responses, or does it instead oppose them? And to what extent does evolution of a trait involve evolution of its plasticity? These questions have lied at the heart of research on phenotypic evolution in heterogeneous environments ever since it was realized that the environment is likely to affect the expression of many (perhaps most) characters of an individual. Importantly, this broad definition of phenotypic plasticity as change in the average phenotype of a given genotype in response to its environment of development (or expression) does not involve any statement about the adaptiveness of the plastic changes. Theory on the evolution of plasticity has devoted much effort to understanding how reaction norm should evolve under different regimes of environmental change in space and time, and depending on genetic constraints on reaction norm shapes. However on an empirically ground, the questions above have mostly been addressed for individual traits, often chosen a priori for their likeliness to exhibit adaptive plasticity, and we still lack more systematic answers. These can be provided by so-called ‘phenomic’ approaches, where a large number of traits are tracked without prior information on their biological or ecological function. A problem is that the number of phenotypic characters that can be measured in an organism is virtually infinite (and to some extent arbitrary), and that scaling issues makes it difficult to compare different sets of traits. Gene-expression levels offer a partial solution to this dilemma, as they can be considered as a very large number of traits (one per typed gene) that can be measured easily and uniformly (fold change in the number of reads in RNAseq). As for any traits, expression levels of different genes may be genetically correlated, to an extent that depends on their regulation mechanism: cis-regulatory sequences that only affect expression of neighboring genes are likely to cause independent gene expression, while more systematic modifiers of expression (e.g. trans-regulators such as transcription factors) may cause correlated genetic responses of the expression of many genes. Huang and Agrawal [1] have studied plasticity and evolution of gene expression level in young larvae of populations of Drosophila melanogaster that have evolved for about 130 generations under either a constant environment (salt or cadmium), or an environment that is heterogeneous in time or space (combining salt and cadmium). They report a wealth of results, of which we summarize the most striking here. First, among genes that (i) were initially highly plastic and (ii) evolved significant divergence in expression levels between constant environment treatments, the evolved divergence is predominantly in the opposite direction to the initial plastic response. This suggests that either plasticity was initially maladaptive, or the selective pressure changed during the evolutionary process (see below). This somewhat unexpected result strikingly mirrors that from a study published last year in Nature [2], where the same pattern was found for responses of guppies to the presence of predators. However, Huang and Agrawal [1] went beyond this study by deciphering the underlying mechanisms in several interesting ways. First, they showed that change in gene expression often occurred at genes close to SNPs with differentiated frequencies across treatments (but not at genes with differentiated SNPs in their coding sequences), suggesting that cis-regulatory sequences are involved. This is also suggested by the fact that changes in gene expression are mostly caused by the increased expression of only one allele at polymorphic loci, and is a first step towards investigating the genetic underpinnings of (co)variation in gene expression levels. Another interesting set of findings concerns evolution of plasticity in treatments with variable environments. To compare the gene-expression plasticity that evolved in these treatments to an expectation, the authors considered that the expression levels in populations maintained for a long time under constant salt or cadmium had reached an optimum. The differences between these expression levels were thus assumed to predict the level of plasticity that should evolve in a heterogeneous environment (with both cadmium and salt) under perfect environmental predictability. The authors showed that plasticity did evolve more in the expected direction in heterogeneous than in constant environments, resulting in better adapted final expression levels across environments. Taken collectively, these results provide an unprecedented set of patterns that are greatly informative on how plasticity and evolution interact in constant versus changing environments. But of course, interpretations in terms of adaptive versus maladaptive plasticity are more challenging, as the authors themselves admit. Even though environmentally determined gene expression is the basic mechanism underlying the phenotypic plasticity of most traits, it is extremely difficult to relate to more integrated phenotypes for which we can understand the selection pressures, especially in multicellular organisms. The authors have recently investigated evolutionary change of quantitative traits in these selected lines, so it might be possible to establish links between reaction norms for macroscopic traits to those for gene expression levels. Such an approach would also involve tracking gene expression throughout life, rather than only in young larvae as done here, thus putting phenotypic complexity back in the picture also for expression levels. Another difficulty is that a plastic response that was originally adaptive may be replaced by an opposite evolutionary response in the long run, without having to invoke initially maladaptive plasticity. For instance, the authors mention the possibility that a generic stress response is initially triggered by cadmium, but is eventually unnecessary and costly after evolution of genetic mechanisms for cadmium detoxification (a case of so-called genetic accommodation). In any case, this study by Huang and Agrawal [1], together with the one by Ghalambor et al. last year [2], reports novel and unexpected results, which are likely to stimulate researchers interested in plasticity and evolution in heterogeneous environments for the years to come.

References

[1] Huang Y, Agrawal AF. 2016. Experimental Evolution of Gene Expression and Plasticity in Alternative Selective Regimes. PLoS Genetics 12:e1006336. doi: 10.1371/journal.pgen.1006336

[2] Ghalambor CK, Hoke KL, Ruell EW, Fischer EK, Reznick DN, Hughes KA. 2015. Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature 525: 372-375. doi: 10.1038/nature15256

Many species breed in environments that are unpredictable, for instance in terms of the availability of resources needed to raise the offspring. Organisms might respond to such spatial and temporal unpredictability by adopting plastic responses to adjust their reproductive investment according to perceived cues of environmental quality. Some species such as the amphibians might also face the problem of ephemeral habitats, when the ponds where they breed have a chance of drying up before metamorphosis has occurred. In this case, maximizing long-term fitness might involve a strategy of spreading the risk, even though the reproductive success of a single reproductive bout might be lower. Understanding how animals (and plants) get adapted to stochastic environments is particularly crucial in the current context of rapid environmental changes.

In this article, Jourdan-Pineau et al. report the results of field surveys of the Parsley Frog (Pelodytes punctatus) in Southern France. This frog has peculiar breeding phenology with females breeding in autumn and spring. The authors provide quite an extensive amount of information on the reproductive success of eggs laid in each season and the possible ecological factors accounting for differences between seasons. Although the presence of interspecific competitors and predators does not seem to account for pond-specific reproductive success, the survival of tadpoles hatching from eggs laid in spring is severely impaired when tadpoles from the autumn cohort have managed to survive. This intraspecific competition takes the form of a “priority” effect where tadpoles from the autumn cohort outcompete the smaller larvae from the spring cohort. Given this strong priority effect, one might tentatively predict that females laying in spring should avoid ponds with tadpoles from the autumn cohort. Surprisingly, however, the authors did not find any evidence for such avoidance, which might indicate strong constraints on the availability of ponds where females might possibly lay.

Assuming that each female can indeed lay both in autumn and spring, how is this bimodal phenology maintained? Would not be worthier to allocate all the eggs to the autumn (or the spring) laying season? Eggs laid in autumn and spring have to face different environmental hazards, reducing their hatching success and the probability to produce metamorphs (for instance, tadpoles hatching from eggs laid in autumn have to overwinter which might be a particularly risky phase).             

Jourdan-Pineau and coworkers addressed this question by adapting a bet-hedging model that was initially developed to investigate the strategy of allocation into seed dormancy of annual plants (Cohen 1966) to the case of the bimodal phenology of the Parsley Frog. By feeding the model with the parameter values obtained from the field surveys, they found that the two breeding strategies (laying in autumn and in spring) can coexist as long as the probability of breeding success in the autumn cohort is between 20% and 80% (the range of values allowing the coexistence of a bimodal phenology shrinking a little bit when considering that frogs can reproduce 5 times during their lifespan instead of three times).

This paper provides a very nice illustration of the importance of combining approaches (here field monitoring to gather data that can be used to feed models) to understand the evolution of peculiar breeding strategies. Although future work should attempt to gather individual-based data (in addition to population data), this work shows that spreading the risk can be an adaptive strategy in environments characterized by strong stochastic variation.

References

Cohen D (1966) Optimizing reproduction in a randomly varying environment. Journal of Theoretical Biology, 12, 119–129. https://doi.org/10.1016/0022-5193(66)90188-3

Jourdan-Pineau H., Crochet P.-A., David P. (2022) Bimodal breeding phenology in the Parsley Frog Pelodytes punctatus as a bet-hedging strategy in an unpredictable environment despite strong priority effects. bioRxiv, 2022.02.24.481784, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.02.24.481784