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

Understanding the factors that shape the virome of a host is key to understanding virus ecology and evolution (Obbard, 2018; French & Holmes, 2020). There is still much to learn about the diversity and distribution of viruses in a host community (Wille et al., 2019; Chen et al., 2023). The viruses of parasitoid wasps are well studied, and their viruses, or integrated viral genes, are known to suppress their insect host’s immune response to enhance parasitoid survival (Herniou et al., 2013; Coffman et al., 2022). Likewise, the insect virome is being increasingly well studied (Shi et al., 2016), with the virome of Drosophila species being particularly well characterised over the best part of the last century (L'Heritier & Teissier, 1937; L'Heritier, 1970; Brun & Plus, 1980; Longdon et al., 2010; Longdon et al., 2011; Longdon et al., 2012; Webster et al., 2015; Webster et al., 2016; Medd et al., 2018; Wallace et al., 2021). However, the viromes of parasitoids and their insect host communities have been less well studied (Leigh et al., 2018; Caldas-Garcia et al., 2023), and the inherent connectivity between parasitoids and their hosts provides an interesting system to study virus host range and cross-species transmission.

Here, Varaldi et al (Varaldi et al., 2024) have examined the viruses associated with a community of nine Drosophilidae hosts and six parasitoids. Using both RNA and DNA sequencing of insects reared for two generations, they selected viruses that are maintained in the lab either via vertical transmission or contamination of rearing medium. From 55 pools of insects they found 53 virus-like sequences, 37 of which were novel. Parasitoids were host to nearly twice as many viruses as their Drosophila hosts, although they note this could be due to differences in the rearing temperatures of the hosts.  

They next quantified if species, year, season, or location played a role in structuring the virome, finding only a significant effect of host species, which explained just over 50% of the variation in virus distribution. No evidence was found of related species sharing more similar virus communities. Although looking at a limited number of species, this suggests that these viruses are not co-speciating or preferentially host switching between closely related species.

Finally, they carried out crosses between lines of the parasitoid Leptopilina heterotoma that were infected and uninfected for a novel Iflavirus found in their sequencing data.  They found evidence of high levels of maternal transmission and lower level horizontal transmission between wasp larvae parasitising the same host. No evidence of changes in parasitoid-induced mortality, developmental success or the sex ratio was found in iflavirus-infected parasitoids. Interestingly individuals infected with this RNA virus also contained viral DNA, but this did not appear to be integrated into the wasp genome.

Overall, this work has taken the first steps in examining the community structure of the virome of parasitoids together with their Drosophilidae hosts. This work will not doubt stimulate follow-up studies to explore the evolution and ecology of these novel virus communities.

References

Brun G, Plus N (1980) The viruses of Drosophila. In: The genetics and biology of Drosophila eds Ashburner M & Wright TRF), pp. 625-702. Academic Press, New York.
 
Caldas-Garcia GB, Santos VC, Fonseca PLC, de Almeida JPP, Costa MA, Aguiar ERGR (2023) The Viromes of Six Ecosystem Service Provider Parasitoid Wasps. Viruses, 15. https://doi.org/10.3390/v15122448
 
Chen YM, Hu SJ, Lin XD, Tian JH, Lv JX, Wang MR, Luo XQ, Pei YY, Hu RX, Song ZG, Holmes EC, Zhang YZ (2023) Host traits shape virome composition and virus transmission in wild small mammals. Cell, 186, 4662-4675 e4612. https://doi.org/10.1016/j.cell.2023.08.029
 
Coffman KA, Hankinson QM, Burke GR (2022) A viral mutualist employs posthatch transmission for vertical and horizontal spread among parasitoid wasps. Proceedings of the National Academy of Sciences of the United States of America, 119. https://doi.org/10.1073/pnas.2120048119
 
French RK, Holmes EC (2020) An Ecosystems Perspective on Virus Evolution and Emergence. Trends in Microbiology, 28, 165-175. https://doi.org/10.1016/j.tim.2019.10.010
 
Herniou EA, Huguet E, Thézé J, Bézier A, Periquet G, Drezen JM (2013) When parasitic wasps hijacked viruses: genomic and functional evolution of polydnaviruses. Philosophical Transactions of the Royal Society B-Biological Sciences, 368. https://doi.org/10.1098/rstb.2013.0051
 
L'Heritier PH (1970) Drosophila viruses and their role as evolutionary factors. Evolutionary Biology, 4, 185-209
 
L'Heritier PH, Teissier G (1937) Une anomalie physiologique héréditaire chez la Drosophile. C.R. Acad. Sci. Paris, 231, 192-194
 
Leigh BA, Bordenstein SR, Brooks AW, Mikaelyan A, Bordenstein SR (2018) Finer-Scale Phylosymbiosis: Insights from Insect Viromes. Msystems, 3. https://doi.org/10.1128/mSystems.00131-18
 
Longdon B, Obbard DJ, Jiggins FM (2010) Sigma viruses from three species of Drosophila form a major new clade in the rhabdovirus phylogeny. Proceedings of the Royal Society B, 277, 35-44. 
https://doi.org/10.1098/rspb.2009.1472
 
Longdon B, Wilfert L, Jiggins FM (2012) The Sigma Viruses of Drosophila. Caister Academic Press, Norfolk, UK.
 
Longdon B, Wilfert L, Osei-Poku J, Cagney H, Obbard DJ, Jiggins FM (2011) Host switching by a vertically-transmitted rhabdovirus in Drosophila. Biology Letters, 7, 747-750. 
https://doi.org/10.1098/rsbl.2011.0160
 
Medd NC, Fellous S, Waldron FM, Xuereb A, Nakai M, Cross JV, Obbard DJ (2018) The virome of Drosophila suzukii, an invasive pest of soft fruit. Virus Evol, 4, vey009. 
https://doi.org/10.1093/ve/vey009
 
Obbard DJ (2018) Expansion of the metazoan virosphere: progress, pitfalls, and prospects. Curr Opin Virol, 31, 17-23. https://doi.org/10.1016/j.coviro.2018.08.008
 
Shi M, Lin XD, Tian JH, Chen LJ, Chen X, Li CX, Qin XC, Li J, Cao JP, Eden JS, Buchmann J, Wang W, Xu J, Holmes EC, Zhang YZ (2016) Redefining the invertebrate RNA virosphere. Nature. https://doi.org/10.1038/nature20167
 
Varaldi J, Lepetit D, Burlet N, Faber C, Baretje B, Allemand R (2024) Community structure of heritable viruses in a Drosophila-parasitoids complex. bioRxiv, 2023.2007.2029.551099, ver.3, peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2023.07.29.551099
 
Wallace MA, Coffman KA, Gilbert C, Ravindran S, Albery GF, Abbott J, Argyridou E, Bellosta P, Betancourt AJ, Colinet H, Eric K, Glaser-Schmitt A, Grath S, Jelic M, Kankare M, Kozeretska I, Loeschcke V, Montchamp-Moreau C, Ometto L, Onder BS, Orengo DJ, Parsch J, Pascual M, Patenkovic A, Puerma E, Ritchie MG, Rota-Stabelli O, Schou MF, Serga SV, Stamenkovic-Radak M, Tanaskovic M, Veselinovic MS, Vieira J, Vieira CP, Kapun M, Flatt T, Gonzalez J, Staubach F, Obbard DJ (2021) The discovery, distribution, and diversity of DNA viruses associated with Drosophila melanogaster in Europe. Virus Evol, 7, veab031. https://doi.org/10.1093/ve/veab031
 
Webster CL, Longdon B, Lewis SH, Obbard DJ (2016) Twenty-Five New Viruses Associated with the Drosophilidae (Diptera). Evol Bioinform Online, 12, 13-25. https://doi.org/10.4137/EBO.S39454
 
Webster CL, Waldron FM, Robertson S, Crowson D, Ferrai G, Quintana JF, Brouqui JM, Bayne EH, Longdon B, Buck AH, Lazzaro BP, Akorli J, Haddrill PR, Obbard DJ (2015) The discovery, distribution and evolution of viruses associated with Drosophila melanogaster. Plos Biology, 13(7): e1002210. https://doi.org/10.1371/journal.pbio.1002210
 
Wille M, Shi M, Klaassen M, Hurt AC, Holmes EC (2019) Virome heterogeneity and connectivity in waterfowl and shorebird communities. ISME J, 13, 2603-2616. https://doi.org/10.1038/s41396-019-0458-0

Modelling the evolution of complete genome sequences in populations requires accounting for the recombination process, as a single tree can no longer describe the underlying genealogy. The sequentially Markov coalescent (SMC, McVean and Cardin 2005; Marjoram and Wall 2006) approximates the standard coalescent with recombination process and permits estimating population genetic parameters (e.g., population sizes, recombination rates) using population genomic datasets. As such datasets become available for an increasing number of species, more fine-tuned models are needed to encompass the diversity of life cycles of organisms beyond the model species on which most methods have been benchmarked.

The work by Korfmann et al. (Korfmann et al. 2024) represents a significant step forward as it accounts for multiple mergers in SMC models. Multiple merger models account for simultaneous coalescence events so that more than two lineages find a common ancestor in a given generation. This feature is not allowed in standard coalescent models and may result from selection or skewed offspring distributions, conditions likely met by a broad range of species, particularly microbial.

Yet, this work goes beyond extending the SMC, as it introduces several methodological innovations. The "classical" SMC-based inference approaches rely on hidden Markov models to compute the likelihood of the data while efficiently integrating over the possible ancestral recombination graphs (ARG). Following other recent works (e.g. Gattepaille et al. 2016), Korfmann et al. propose to separate the ARG inference from model parameter estimation under maximum likelihood (ML). They introduce a procedure where the ARG is first reconstructed from the data and then taken as input in the model fitting step. While this approach does not permit accounting for the uncertainty in the ARG reconstruction (which is typically large), it potentially allows for the extraction of more information from the ARG, such as the occurrence of multiple merging events. Going away from maximum likelihood inference, the authors trained a graph neural network (GNN) on simulated ARGs, introducing a new, flexible way to estimate population genomic parameters.

The authors used simulations under a beta-coalescent model with diverse demographic scenarios and showed that the ML and GNN approaches introduced can reliably recover the simulated parameter values. They further show that when the true ARG is given as input, the GNN outperforms the ML approach, demonstrating its promising power as ARG reconstruction methods improve. In particular, they showed that trained GNNs can disentangle the effects of selective sweeps and skewed offspring distributions while inferring past population size changes.

This work paves the way for new, exciting applications, though many questions must be answered. How frequent are multiple mergers? As the authors showed that these events "erase" the record of past demographic events, how many genomes are needed to conduct reliable inference, and can the methods computationally cope with the resulting (potentially large) amounts of required data? This is particularly intriguing as micro-organisms, prone to strong selection and skewed offspring distributions, also tend to carry smaller genomes.

References

Gattepaille L, Günther T, Jakobsson M. 2016. Inferring Past Effective Population Size from Distributions of Coalescent Times. Genetics 204:1191-1206.
https://doi.org/10.1534/genetics.115.185058
 
Korfmann K, Sellinger T, Freund F, Fumagalli M, Tellier A. 2024. Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent. bioRxiv, 2022.09.28.508873. ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.09.28.508873
 
Marjoram P, Wall JD. 2006. Fast "coalescent" simulation. BMC Genet. 7:16.
https://doi.org/10.1186/1471-2156-7-16
 
McVean GAT, Cardin NJ. 2005. Approximating the coalescent with recombination. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 360:1387-1393.
https://doi.org/10.1098/rstb.2005.1673

The harm most parasites cause to their host, i.e. the virulence, is a mystery because host death often means the end of the infectious period. For obligate killer parasites, or “parasitoids”, that need to kill their host to transmit to other hosts the question is reversed. Indeed, more rapid host death means shorter generation intervals between two infections and mathematical models show that, in the simplest settings, natural selection should always favour more virulent strains (Levin and Lenski, 1983). Adding biological details to the model modifies this conclusion and, for instance, if the relationship between the infection duration and the number of parasites transmission stages produced in a host is non-linear, strains with intermediate levels of virulence can be favoured (Ebert and Weisser 1997). Other effects, such as spatial structure, could yield similar effects (Lion and van Baalen, 2007).

In their study, MacDonald et al. (2021) explore another type of constraint, which is seasonality. Earlier studies, such as that by Donnelly et al. (2013) showed that this constraint can affect virulence evolution but they had focused on directly transmitted parasites. Using a mathematical model capturing the dynamics of a parasitoid, MacDonald et al. (2021) show if two main assumptions are met, namely that at the end of the season only transmission stages (or “propagules”) survive and that there is a constant decay of these propagules with time, then strains with intermediate levels of virulence are favoured.

Practically, the authors use delay differential equations and an adaptive dynamics approach to identify evolutionary stable strategies. As expected, the longer the short the season length, the higher the virulence (because propagule decay matters less). The authors also identify a non-linear relationship between the variation in host development time and virulence. Generally, the larger the variation, the higher the virulence because the parasitoid has to kill its host before the end of the season. However, if the variation is too wide, some hosts become physically impossible to use for the parasite, whence a decrease in virulence.

Finally, MacDonald et ali. (2021) show that the consequence of adding trade-offs between infection duration and the number of propagules produced is in line with earlier studies (Ebert and Weisser 1997). These mathematical modelling results provide testable predictions for using well-described systems in evolutionary ecology such as daphnia parasitoids, baculoviruses, or lytic phages.

Reference

Donnelly R, Best A, White A, Boots M (2013) Seasonality selects for more acutely virulent parasites when virulence is density dependent. Proc R Soc B, 280, 20122464. https://doi.org/10.1098/rspb.2012.2464

Ebert D, Weisser WW (1997) Optimal killing for obligate killers: the evolution of life histories and virulence of semelparous parasites. Proc R Soc B, 264, 985–991. https://doi.org/10.1098/rspb.1997.0136

Levin BR, Lenski RE (1983) Coevolution in bacteria and their viruses and plasmids. In: Futuyma DJ, Slatkin M eds. Coevolution. Sunderland, MA, USA: Sinauer Associates, Inc., 99–127.

Lion S, van Baalen M (2008) Self-structuring in spatial evolutionary ecology. Ecol. Lett., 11, 277–295. https://doi.org/10.1111/j.1461-0248.2007.01132.x

MacDonald H, Akçay E, Brisson D (2021) Host phenology can drive the evolution of intermediate virulence strategies in some obligate-killer parasites. bioRxiv, 2021.03.13.435259, ver. 8 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.03.13.435259

The evolution of cells within organisms can be an important determinant of disease. This is especially clear in the emergence of tumors and cancers from the underlying healthy tissue. In the healthy state, homeostasis is maintained through complex regulatory processes that ensure a relatively constant population size of cells, which is required for tissue function. Tumor cells escape this homeostasis, resulting in uncontrolled growth and consequent disease. Disease progression is driven by further evolutionary processes within the tumor, and so is the response of tumors to therapies. Therefore, evolutionary biology is an important component required for a better understanding of carcinogenesis and the treatment of cancers. In particular, evolutionary theory helps define the principles of mutant evolution and thus to obtain a clearer picture of the determinants of tumor emergence and therapy responses.     

The study by Raatz and Traulsen [1] makes an important contribution in this respect. They use mathematical and computational models to investigate trait evolution in the context of evolutionary rescue, motivated by the dynamics of cancer, and also bacterial infections. This study views the establishment of tumors as cell dynamics in harsh environments, where the population is prone to extinction unless mutants emerge that increase evolutionary fitness, allowing them to expand (evolutionary rescue). The core processes of the model include growth, death, and mutations. Random mutations are assumed to give rise to cell lineages with different trait combinations, where the birth and death rates of cells can change.  The resulting evolutionary trajectories are investigated in the models, and interesting new results were obtained. For example, the turnover of the population was identified as an important determinant of trait evolution. Turnover is defined as the balance between birth and death, with large rates corresponding to fast turnover and small rates to slow turnover. It was found that for fast cell turnover, a given adaptive step in the trait space results in a smaller increase in survival probability than for cell populations with slower turnover. In other words, evolutionary rescue is more difficult to achieve for fast compared to slow turnover populations. While more mutants can be produced for faster cell turnover rates, the analysis showed that this is not sufficient to overcome the barrier to the evolutionary rescue. This result implies that aggressive tumors with fast cell birth and death rates are less likely to persist and progress than tumors with lower turnover rates. This work emphasizes the importance of measuring the turnover rate in different tumors to advance our understanding of the determinants of tumor initiation and progression. The authors discuss that the well-documented heterogeneity in tumors likely also applies to cellular turnover. If a tumor consists of sub-populations with faster and slower turnover, it is possible that a slower turnover cell clone (e.g. characterized by a degree of dormancy) would enjoy a selective advantage. Another source of heterogeneity in turnover could be given by the hierarchical organization of tumors. Similar to the underlying healthy tissue, many tumors are thought to be maintained by a population of cancer stem cells, while the tumor bulk is made up of more differentiated cells. Tissue stem cells tend to be characterized by a lower turnover than progenitor or transit-amplifying cells. Depending on the assumptions about the self-renewal capacity of these different cell populations, the potential for evolutionary rescue could be different depending on the cell compartment in which the mutant emerges. This might be interesting to explore in the future.

There are also implications for treatment. Two types of treatment were investigated: density-affecting treatments in which the density of cells is reduced without altering their trait parameters, and trait-affecting treatments in which the birth and/or death rates are altered. Both types of treatment were found to change the trajectories of trait adaptation, which has potentially important practical implications. Interestingly, it was found that competitive release during treatment can result in situations where after treatment cessation, the non-extinct populations recover to reach sizes that were higher than in the absence of treatment. This points towards the potential of adaptive therapy approaches, where sensitive cells are maintained to some extent to suppress resistant clones [2] competitively. In this context, it is interesting that the success of such approaches might also depend on the turnover of the tumor cell population, as shown by a recent mathematical modeling study [3]. In particular, it was found that adaptive therapy is less likely to work for slow compared to fast turnover tumors. Yet, the current study by Raatz and Traulsen [1] suggests that tumors are more likely to evolve in a slow turnover setting.

While there is strong relevance of this analysis for tumor evolution, the results generated in this study have more general relevance. Besides tumors, the paper discusses applications to bacterial disease dynamics in some detail, which is also interesting to compare and contrast to evolutionary processes in cancer. Overall, this study provides insights into the dynamics of evolutionary rescue that represent valuable additions to evolutionary theory.  

References

[1] Raatz M, Traulsen A (2023) Promoting extinction or minimizing growth? The impact of treatment on trait trajectories in evolving populations. bioRxiv, 2022.06.17.496570, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.06.17.496570

[2] Gatenby RA, Silva AS, Gillies RJ, Frieden BR (2009) Adaptive Therapy. Cancer Research, 69, 4894–4903. https://doi.org/10.1158/0008-5472.CAN-08-3658

[3] Strobl MAR, West J, Viossat Y, Damaghi M, Robertson-Tessi M, Brown JS, Gatenby RA, Maini PK, Anderson ARA (2021) Turnover Modulates the Need for a Cost of Resistance in Adaptive Therapy. Cancer Research, 81, 1135–1147. https://doi.org/10.1158/0008-5472.CAN-20-0806